From 0f0777371f27054e1ca3e702af0885eb7a92e7de Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 28 Apr 2026 17:45:16 +0200
Subject: [PATCH 01/18] Add token efficiency benchmarks

---
 assets/images/recall_vs_tokens.png            |  Bin 0 -> 62621 bytes
 benchmarks/README.md                          |   91 +
 benchmarks/baselines/ablations.py             |   33 +-
 benchmarks/baselines/coderankembed.py         |   51 +-
 benchmarks/baselines/colgrep.py               |   53 +-
 benchmarks/baselines/ripgrep.py               |   51 +-
 benchmarks/context_efficiency.py              |  837 ++
 benchmarks/data.py                            |   48 +-
 ...context-efficiency-judge-6248855cfef8.json | 9804 +++++++++++++++++
 ...ontext-efficiency-recall-6248855cfef8.json |  313 +
 benchmarks/run_benchmark.py                   |   17 +-
 benchmarks/speed_benchmark.py                 |   62 +-
 benchmarks/tools.py                           |   64 +
 pyproject.toml                                |    2 +
 uv.lock                                       |  334 +
 15 files changed, 11524 insertions(+), 236 deletions(-)
 create mode 100644 assets/images/recall_vs_tokens.png
 create mode 100644 benchmarks/context_efficiency.py
 create mode 100644 benchmarks/results/context-efficiency-judge-6248855cfef8.json
 create mode 100644 benchmarks/results/context-efficiency-recall-6248855cfef8.json
 create mode 100644 benchmarks/tools.py

diff --git a/assets/images/recall_vs_tokens.png b/assets/images/recall_vs_tokens.png
new file mode 100644
index 0000000000000000000000000000000000000000..83e153c88d3d7501e3d608a6bffd90ed326045cc
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diff --git a/benchmarks/README.md b/benchmarks/README.md
index 3ba8566..d36d068 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -4,6 +4,7 @@ Quality and speed benchmarks for `semble`.
 
 - [Main results](#main-results)
 - [By language](#by-language)
+- [Context efficiency](#context-efficiency)
 - [Ablations](#ablations)
 - [Dataset](#dataset)
 - [Methods](#methods)
@@ -57,6 +58,74 @@ NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best sc
 | typescript | 0.706 | **0.708** | 0.545 | 0.430 | 0.128 |
 | **overall** | **0.854** | **0.862** | **0.765** | **0.693** | **0.126** |
 
+## Context efficiency
+
+Coding agents like Claude Code and OpenCode rely on `grep` to find candidate files and then `Read` those files into context. We model this workflow (`grep + read file`) and measure how many tokens each retrieval strategy spends to actually answer a query — judged end-to-end by an external LLM.
+
+> Across a 198-query category-balanced sample judged by GPT-5-mini, semble's retrieved context is sufficient to answer **88%** of queries at 2k tokens. A grep-based agent that first asks an LLM to extract grep-friendly keywords from the query (the strongest realistic baseline — closer to what Claude Code and OpenCode actually do) reaches **71%** but spends ~11k tokens reading candidate files. The raw retrieval-token saving is **81%** vs keyword grep and **50%** vs naive grep; including a pessimistic fallback model that adds the cost of redoing grep on a miss, end-to-end savings are **77%** and **69%** respectively.
+
+![Recall vs. retrieved tokens](../assets/images/recall_vs_tokens.png)
+
+### Answer sufficiency (LLM-as-judge)
+
+For each query we hand the retrieved context to GPT-5-mini and ask whether it contains enough relevant code to answer the query. Sample is 198 queries, balanced 66/66/66 across categories (note: the underlying benchmark is imbalanced 703/344/204 semantic/architecture/symbol; the macro-balanced sample is more conservative for semble's claim, since proportional sampling would weight the harder semantic category higher). Three retrieval workflows:
+
+- **semble** — top-K chunks, hard-capped at 2k tokens.
+- **keyword-grep+read** — gpt-5-mini extracts 1–3 grep-friendly terms from the query, joined as a regex (`term1|term2`); the agent reads the top-20 matched files in full, hard-capped at 16k tokens. Mirrors how Claude Code and OpenCode actually use grep.
+- **grep+read (naive)** — the raw query string is passed verbatim to `rg --fixed-strings`; matching files are read in full, hard-capped at 16k tokens. Strawman included for comparison.
+
+All three workflows search the same file universe — semble's indexed code-file extensions, with `node_modules`, `dist`, `build`, `.venv`, etc. excluded.
+
+| Method | Budget | Answer rate | Mean tokens retrieved | Raw token reduction vs semble | Expected end-to-end tokens† |
+|---|---:|---:|---:|---:|---:|
+| **semble** | 2k | **0.879** | 2,000 | — | **2,906** |
+| keyword-grep+read | 16k | 0.712 | 10,595 | 81.1% | 12,747 |
+| grep+read (naive) | 16k | 0.293 | 3,996 | 50.0% | 9,281 |
+
+**Raw retrieval reduction:** 81.1% vs keyword-grep, 50.0% vs naive grep.
+**End-to-end reduction (with fallback model):** 77.2% vs keyword-grep, 68.7% vs naive grep.
+
+†The end-to-end column adds a uniform fallback term `(1 − answer_rate) × 7,474` to every method, modeling the agent redoing a grep workflow on a miss. The constant 7,474 is the median tokens grep+read spends to surface a relevant file. Sweeping it 3k–16k gives an end-to-end reduction range of **61–74% vs naive grep** and **74–79% vs keyword grep** (recomputed from the saved artifact), so the conclusion is not load-bearing on this constant. The raw retrieval reduction is the model-free comparison.
+
+**Answer rate by query category:**
+
+| Category | semble | keyword-grep+read | naive grep |
+|---|---:|---:|---:|
+| symbol (named entity lookup) | 0.97 | 0.94 | 0.86 |
+| semantic (behavior / concept) | 0.85 | 0.52 | 0.02 |
+| architecture (design / structure) | 0.82 | 0.68 | 0.00 |
+
+Keyword extraction closes the gap on symbol queries, helps substantially on semantic and architecture — but at ~5× the retrieval token cost (full file reads, top-20 candidates), so the end-to-end gap remains large.
+
+### Recall at fixed token budgets
+
+A coarser, label-based view across the full 1,251-query benchmark: a relevant file is "covered" once any retrieved unit comes from it.
+
+| Method | 500 | 1k | 2k | 4k | 8k | 16k | 32k |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| **semble** | **0.684** | **0.848** | **0.936** | **0.976** | **0.991** | **0.996** | **0.996** |
+| grep + read file | 0.000 | 0.007 | 0.023 | 0.042 | 0.076 | 0.101 | 0.127 |
+| ripgrep -C 8 | 0.069 | 0.091 | 0.107 | 0.117 | 0.131 | 0.143 | 0.150 |
+
+<details>
+<summary>Methodology</summary>
+
+**Retrieval units.** For each query we retrieve units in rank order — semble chunks for semble, full files (in match-count order) for `grep + read file`, merged context windows for `ripgrep -C N` — and accumulate tokens (`cl100k_base` via `tiktoken`). Naive `grep` is run with `--fixed-strings --ignore-case` on the raw query. The keyword variant first asks gpt-5-mini for 1–3 search terms (joined as a regex `|`); patterns that fail an identifier-token sanity check fall back to `re.escape(query)`. Both grep variants are scoped via `--glob` to the same code-file extensions and ignored directories that semble's file walker uses, so all three workflows search the same corpus.
+
+**Budget enforcement (judge mode only).** In the LLM-as-judge eval each context is hard-capped at its stated budget: the candidate string is encoded once, then sliced by token IDs and asserted `len(ids) ≤ budget`. The recall mode below does not truncate; its curves record cumulative tokens of whole retrieved units, which is the right shape for "tokens to surface the answer."
+
+**Sample policy.** The judge sample is **macro-balanced** — 66 queries per category — not proportional to the underlying 703/344/204 split. This is conservative for semble's claim: a proportional sample would overweight semantic queries (where grep does worst) and inflate the headline. The recall benchmark below uses all 1,251 queries with no rebalancing.
+
+**Sensitivity of the top-20 file cap.** Sweeping top-{5, 10, 20, 50} on the same sample (saved alongside the default cap in the same artifact), keyword-grep+read answer rates fall in 71.2–72.7% and the end-to-end reduction range is 76.3–77.2%. The cap is bounded by the 16k token budget, not the file count, so the choice is not load-bearing.
+
+**Sufficiency judging.** GPT-5-mini sees the query and the retrieved context (capped at the per-method budget) and answers a strict yes/no on whether the context contains code that directly addresses the query.
+
+**End-to-end cost model.** `expected_cost = retrieval_tokens + (1 − answer_rate) × fallback_tokens`. The fallback is set to 7,474 tokens — the median tokens grep+read spends to surface a relevant file, derived from the recall benchmark. This is *pessimistic* for semble: it assumes the agent rebuilds context from scratch on every miss, with no credit for chunks semble already returned.
+
+**Recall.** A relevant file is "covered" once any retrieved unit comes from it. Most labels are file-level; where line spans are present we require span overlap.
+
+</details>
+
 ## Ablations
 
 `raw` returns retrieval scores directly; `+ ranking` feeds them through semble's hybrid ranker.
@@ -190,6 +259,28 @@ uv run python -m benchmarks.baselines.coderankembed --mode semantic
 
 </details>
 
+<details>
+<summary>Context-efficiency benchmark</summary>
+
+Requires the `benchmark` extra (`uv sync --extra benchmark`), `rg` on `$PATH`, and an `OPENAI_API_KEY` environment variable (used for keyword extraction and the LLM-as-judge baseline; `--no-keyword` skips it for the recall mode).
+
+```bash
+# Recall vs. token-budget across all queries; plots automatically.
+uv run python -m benchmarks.context_efficiency recall
+uv run python -m benchmarks.context_efficiency recall --repo fastapi --no-keyword
+
+# LLM-as-judge sufficiency on a stratified sample.
+uv run python -m benchmarks.context_efficiency judge --sample 200
+uv run python -m benchmarks.context_efficiency judge --sample 200 --keyword-caps 5 10 20 50
+
+# Regenerate the plot from a saved recall payload.
+uv run python -m benchmarks.context_efficiency plot
+```
+
+Writes `benchmarks/results/context-efficiency-{recall,judge}-<sha12>.json` and `assets/images/recall_vs_tokens.png`.
+
+</details>
+
 <details>
 <summary>Plots</summary>
 
diff --git a/benchmarks/baselines/ablations.py b/benchmarks/baselines/ablations.py
index 3969b28..348c962 100644
--- a/benchmarks/baselines/ablations.py
+++ b/benchmarks/baselines/ablations.py
@@ -11,11 +11,11 @@
 from benchmarks.data import (
     RepoSpec,
     Task,
-    apply_task_filters,
-    available_repo_specs,
+    add_filter_args,
     grouped_tasks,
-    load_tasks,
+    load_filtered_tasks,
     save_results,
+    summarize_modes,
 )
 from benchmarks.metrics import ndcg_at_k, target_rank
 from semble import SembleIndex
@@ -163,12 +163,10 @@ def _bench(
 
 def _parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="semble ablation benchmarks.")
-    parser.add_argument("--repo", action="append", default=[], help="Limit to one or more repo names.")
-    parser.add_argument("--language", action="append", default=[], help="Limit to one or more languages.")
+    add_filter_args(parser, verbose=True)
     parser.add_argument(
         "--mode", action="append", default=[], choices=_MODES, help="Mode(s) to evaluate (default: all)."
     )
-    parser.add_argument("--verbose", action="store_true", help="Print per-query results.")
     return parser.parse_args()
 
 
@@ -177,12 +175,7 @@ def main() -> None:
     args = _parse_args()
     modes = args.mode or _MODES
 
-    repo_specs = available_repo_specs()
-    tasks = apply_task_filters(
-        load_tasks(repo_specs=repo_specs), repos=args.repo or None, languages=args.language or None
-    )
-    if not tasks:
-        raise SystemExit("No benchmark tasks matched the requested filters.")
+    repo_specs, tasks = load_filtered_tasks(args.repo or None, args.language or None)
 
     print("Loading model...", file=sys.stderr)
     started = time.perf_counter()
@@ -210,21 +203,7 @@ def main() -> None:
     summary = {
         "tool": "semble-ablations",
         "model": _DEFAULT_MODEL_NAME,
-        "by_mode": {
-            mode: {
-                "avg_ndcg10": round(
-                    sum(r.ndcg10 for r in results if r.mode == mode)
-                    / max(1, sum(1 for r in results if r.mode == mode)),
-                    4,
-                ),
-                "avg_p50_ms": round(
-                    sum(r.p50_ms for r in results if r.mode == mode)
-                    / max(1, sum(1 for r in results if r.mode == mode)),
-                    1,
-                ),
-            }
-            for mode in modes
-        },
+        "by_mode": summarize_modes(results, modes),
         "repos": [asdict(r) for r in results],
     }
     print(json.dumps(summary, indent=2))
diff --git a/benchmarks/baselines/coderankembed.py b/benchmarks/baselines/coderankembed.py
index ee4035a..b96ba32 100644
--- a/benchmarks/baselines/coderankembed.py
+++ b/benchmarks/baselines/coderankembed.py
@@ -3,6 +3,7 @@
 import sys
 import time
 from collections import defaultdict
+from collections.abc import Sequence
 from dataclasses import asdict, dataclass, field
 from pathlib import Path
 
@@ -12,16 +13,16 @@
 from benchmarks.data import (
     RepoSpec,
     Task,
-    apply_task_filters,
-    available_repo_specs,
+    add_filter_args,
     grouped_tasks,
-    load_tasks,
+    load_filtered_tasks,
     results_path,
     save_results,
+    summarize_modes,
 )
 from benchmarks.metrics import ndcg_at_k, target_rank
 from semble import SembleIndex
-from semble.types import SearchResult
+from semble.types import EmbeddingMatrix, SearchResult
 
 _MODEL_NAME = "nomic-ai/CodeRankEmbed"
 _TOP_K = 10
@@ -29,21 +30,18 @@
 
 
 class _AsymmetricWrapper:
-    """Wrap SentenceTransformer with asymmetric query/document prompts.
-
-    Single-element lists are treated as queries; larger batches as documents.
-    max_seq_length is capped to avoid OOM on CPU with long chunks.
-    """
+    """Wrap SentenceTransformer with asymmetric query/document prompts."""
 
     def __init__(self, model: SentenceTransformer, max_seq_length: int = 512) -> None:
         self._model = model
         self._model.max_seq_length = max_seq_length
 
-    def encode(self, texts: list[str]) -> np.ndarray:
+    def encode(self, texts: Sequence[str], /) -> EmbeddingMatrix:
         """Encode texts with query or document prompt based on batch size."""
-        if len(texts) == 1:
-            return self._model.encode(texts, prompt_name="query", batch_size=1)  # type: ignore[return-value]
-        return self._model.encode(texts, batch_size=1)  # type: ignore[return-value]
+        text_list = list(texts)
+        if len(text_list) == 1:
+            return self._model.encode(text_list, prompt_name="query", batch_size=1)  # type: ignore[return-value]
+        return self._model.encode(text_list, batch_size=1)  # type: ignore[return-value]
 
 
 @dataclass(frozen=True)
@@ -115,21 +113,7 @@ def _build_summary(results: list[RepoResult], modes: list[str]) -> dict[str, obj
     return {
         "tool": "coderankembed",
         "model": _MODEL_NAME,
-        "by_mode": {
-            mode: {
-                "avg_ndcg10": round(
-                    sum(result.ndcg10 for result in results if result.mode == mode)
-                    / max(1, sum(1 for result in results if result.mode == mode)),
-                    4,
-                ),
-                "avg_p50_ms": round(
-                    sum(result.p50_ms for result in results if result.mode == mode)
-                    / max(1, sum(1 for result in results if result.mode == mode)),
-                    1,
-                ),
-            }
-            for mode in modes
-        },
+        "by_mode": summarize_modes(results, modes),
         "repos": [asdict(result) for result in results],
     }
 
@@ -227,12 +211,10 @@ def _bench(
 
 def _parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="Benchmark CodeRankEmbed on the semble benchmark suite.")
-    parser.add_argument("--repo", action="append", default=[], help="Limit to one or more repo names.")
-    parser.add_argument("--language", action="append", default=[], help="Limit to one or more languages.")
+    add_filter_args(parser, verbose=True)
     parser.add_argument(
         "--mode", action="append", default=[], choices=["semantic", "hybrid"], help="Search mode(s) (default: both)."
     )
-    parser.add_argument("--verbose", action="store_true", help="Print per-query results.")
     return parser.parse_args()
 
 
@@ -242,12 +224,7 @@ def main() -> None:
     modes = args.mode or ["semantic", "hybrid"]
     is_full_run = not args.repo and not args.language
 
-    repo_specs = available_repo_specs()
-    tasks = apply_task_filters(
-        load_tasks(repo_specs=repo_specs), repos=args.repo or None, languages=args.language or None
-    )
-    if not tasks:
-        raise SystemExit("No benchmark tasks matched the requested filters.")
+    repo_specs, tasks = load_filtered_tasks(args.repo or None, args.language or None)
 
     print(f"Loading {_MODEL_NAME}...", file=sys.stderr)
     started = time.perf_counter()
diff --git a/benchmarks/baselines/colgrep.py b/benchmarks/baselines/colgrep.py
index 534b347..9adc1fc 100644
--- a/benchmarks/baselines/colgrep.py
+++ b/benchmarks/baselines/colgrep.py
@@ -9,14 +9,14 @@
 from benchmarks.data import (
     RepoSpec,
     Task,
-    apply_task_filters,
-    available_repo_specs,
+    add_filter_args,
     grouped_tasks,
-    load_tasks,
+    load_filtered_tasks,
     results_path,
     save_results,
 )
 from benchmarks.metrics import file_rank, ndcg_at_k
+from benchmarks.tools import run_colgrep_files
 
 _COLGREP = "colgrep"
 _TOP_K = 10
@@ -34,25 +34,6 @@ class RepoResult:
     index_ms: float
 
 
-def _run_colgrep(query: str, benchmark_dir: Path, top_k: int, *, code_only: bool = True) -> list[str]:
-    """Return list of absolute file paths from colgrep JSON output."""
-    cmd = [_COLGREP, "--force-cpu"]
-    if code_only:
-        cmd.append("--code-only")
-    cmd += ["--json", "-k", str(top_k), query, str(benchmark_dir)]
-    try:
-        proc = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
-    except subprocess.TimeoutExpired:
-        return []
-    if proc.returncode != 0:
-        return []
-    try:
-        data = json.loads(proc.stdout)
-    except json.JSONDecodeError:
-        return []
-    return [item["unit"]["file"] for item in data if "unit" in item and "file" in item["unit"]]
-
-
 def _evaluate_repo(
     tasks: list[Task], benchmark_dir: Path, *, code_only: bool = True, verbose: bool = False
 ) -> tuple[float, float]:
@@ -65,7 +46,7 @@ def _evaluate_repo(
         file_paths: list[str] = []
         for _ in range(_LATENCY_RUNS):
             started = time.perf_counter()
-            file_paths = _run_colgrep(task.query, benchmark_dir, _TOP_K, code_only=code_only)
+            file_paths = run_colgrep_files(task.query, benchmark_dir, top_k=_TOP_K, code_only=code_only)
             query_latencies.append((time.perf_counter() - started) * 1000)
         latencies.append(sorted(query_latencies)[_LATENCY_RUNS // 2])
 
@@ -88,11 +69,7 @@ def _evaluate_repo(
 
 
 def _init_index(path: Path) -> tuple[bool, float]:
-    """Build (or rebuild) the colgrep index at path; return (non_empty, elapsed_ms).
-
-    :param path: Directory to index.
-    :return: Tuple of (non_empty, index_ms) where non_empty is False if colgrep reported 0 files.
-    """
+    """Build the ColGREP index and return whether it indexed files plus elapsed time."""
     subprocess.run([_COLGREP, "clear", str(path)], capture_output=True, timeout=30)
     cmd = [_COLGREP, "init", "--force-cpu", "-y", str(path)]
     started = time.perf_counter()
@@ -106,14 +83,7 @@ def _init_index(path: Path) -> tuple[bool, float]:
 
 
 def _resolve_path(spec: RepoSpec) -> tuple[Path, float]:
-    """Return the path ColGREP should index and elapsed index build time.
-
-    Tries benchmark_dir first; if that yields 0 files falls back to checkout_dir,
-    which is the project root ColGREP needs to discover the .git boundary.
-
-    :param spec: Repo spec providing benchmark_dir and checkout_dir.
-    :return: Tuple of (effective_path, index_ms).
-    """
+    """Return the path ColGREP should index and elapsed index build time."""
     path = spec.benchmark_dir
     ok, index_ms = _init_index(path)
     if ok:
@@ -174,9 +144,7 @@ def _load_completed(out_path: Path) -> dict[str, RepoResult]:
 
 def _parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="Benchmark ColGREP on the semble benchmark suite.")
-    parser.add_argument("--repo", action="append", default=[], help="Limit to one or more repo names.")
-    parser.add_argument("--language", action="append", default=[], help="Limit to one or more languages.")
-    parser.add_argument("--verbose", action="store_true", help="Print per-query results.")
+    add_filter_args(parser, verbose=True)
     parser.add_argument(
         "--no-code-only",
         action="store_true",
@@ -229,12 +197,7 @@ def main() -> None:
     args = _parse_args()
     is_full_run = not args.repo and not args.language
 
-    repo_specs = available_repo_specs()
-    tasks = apply_task_filters(
-        load_tasks(repo_specs=repo_specs), repos=args.repo or None, languages=args.language or None
-    )
-    if not tasks:
-        raise SystemExit("No benchmark tasks matched the requested filters.")
+    repo_specs, tasks = load_filtered_tasks(args.repo or None, args.language or None)
 
     repo_tasks = grouped_tasks(tasks)
 
diff --git a/benchmarks/baselines/ripgrep.py b/benchmarks/baselines/ripgrep.py
index 724ab90..94077f9 100644
--- a/benchmarks/baselines/ripgrep.py
+++ b/benchmarks/baselines/ripgrep.py
@@ -1,6 +1,5 @@
 import argparse
 import json
-import subprocess
 import sys
 import time
 from dataclasses import dataclass
@@ -8,15 +7,14 @@
 
 from benchmarks.data import (
     Task,
-    apply_task_filters,
-    available_repo_specs,
+    add_filter_args,
     grouped_tasks,
-    load_tasks,
+    load_filtered_tasks,
     save_results,
 )
 from benchmarks.metrics import file_rank, ndcg_at_k
+from benchmarks.tools import run_ripgrep_count
 
-_RG = "rg"
 _TOP_K = 10
 _LATENCY_RUNS = 3
 
@@ -31,36 +29,6 @@ class RepoResult:
     p50_ms: float
 
 
-def _run_ripgrep(query: str, benchmark_dir: Path, *, fixed_strings: bool = True) -> list[str]:
-    """Return file paths sorted by match count descending (rg --count output)."""
-    cmd = [_RG, "--count", "--no-heading", "--ignore-case", "--hidden", "--glob", "!.git"]
-    if fixed_strings:
-        cmd.append("--fixed-strings")
-    cmd += [query, str(benchmark_dir)]
-
-    try:
-        proc = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
-    except subprocess.TimeoutExpired:
-        return []
-    if proc.returncode not in (0, 1):
-        return []
-
-    entries: list[tuple[str, int]] = []
-    for line in proc.stdout.splitlines():
-        line = line.strip()
-        if not line:
-            continue
-        *path_parts, count_str = line.split(":")
-        try:
-            count = int(count_str)
-        except ValueError:
-            continue
-        entries.append((":".join(path_parts), count))
-
-    entries.sort(key=lambda entry: -entry[1])
-    return [path for path, _ in entries[:_TOP_K]]
-
-
 def _evaluate_repo(
     tasks: list[Task],
     benchmark_dir: Path,
@@ -77,7 +45,7 @@ def _evaluate_repo(
         file_paths: list[str] = []
         for _ in range(_LATENCY_RUNS):
             started = time.perf_counter()
-            file_paths = _run_ripgrep(task.query, benchmark_dir, fixed_strings=fixed_strings)
+            file_paths = run_ripgrep_count(task.query, benchmark_dir, top_k=_TOP_K, fixed_strings=fixed_strings)
             query_latencies.append((time.perf_counter() - started) * 1000)
         latencies.append(sorted(query_latencies)[_LATENCY_RUNS // 2])
 
@@ -99,9 +67,7 @@ def _evaluate_repo(
 
 def _parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description="Benchmark ripgrep on the semble benchmark suite.")
-    parser.add_argument("--repo", action="append", default=[], help="Limit to one or more repo names.")
-    parser.add_argument("--language", action="append", default=[], help="Limit to one or more languages.")
-    parser.add_argument("--verbose", action="store_true", help="Print per-query results.")
+    add_filter_args(parser, verbose=True)
     parser.add_argument(
         "--no-fixed-strings",
         dest="fixed_strings",
@@ -116,12 +82,7 @@ def main() -> None:
     """Run the ripgrep baseline benchmark."""
     args = _parse_args()
 
-    repo_specs = available_repo_specs()
-    tasks = apply_task_filters(
-        load_tasks(repo_specs=repo_specs), repos=args.repo or None, languages=args.language or None
-    )
-    if not tasks:
-        raise SystemExit("No benchmark tasks matched the requested filters.")
+    repo_specs, tasks = load_filtered_tasks(args.repo or None, args.language or None)
 
     mode_label = "fixed-strings" if args.fixed_strings else "regex"
     print(f"ripgrep ({mode_label})", file=sys.stderr)
diff --git a/benchmarks/context_efficiency.py b/benchmarks/context_efficiency.py
new file mode 100644
index 0000000..d00719f
--- /dev/null
+++ b/benchmarks/context_efficiency.py
@@ -0,0 +1,837 @@
+import argparse
+import asyncio
+import json
+import random
+import re
+import subprocess
+import sys
+import time
+from collections import Counter, defaultdict
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, TypeAlias
+
+import matplotlib.pyplot as plt
+import matplotlib.ticker as ticker
+import numpy as np
+import tiktoken
+from model2vec import StaticModel
+from openai import AsyncOpenAI
+
+from benchmarks.data import (
+    Target,
+    Task,
+    add_filter_args,
+    grouped_tasks,
+    load_filtered_tasks,
+    save_results,
+    target_matches_location,
+)
+from semble import SembleIndex
+from semble.index.dense import _DEFAULT_MODEL_NAME
+from semble.index.file_walker import DEFAULT_IGNORED_DIRS, FILE_TYPES, FileCategory
+from semble.types import Chunk
+
+_RG_INCLUDE_GLOBS: tuple[str, ...] = tuple(
+    f"*{ext}" for ext, spec in FILE_TYPES.items() if spec.category == FileCategory.CODE
+)
+_RG_EXCLUDE_GLOBS: tuple[str, ...] = tuple(f"!{d}" for d in DEFAULT_IGNORED_DIRS)
+
+_BUDGETS = (500, 1000, 2000, 4000, 8000, 16000, 32000)
+_PLOT_BUDGETS = sorted({int(b) for b in np.logspace(np.log10(100), np.log10(64000), 60)})
+_TOKENIZER_NAME = "cl100k_base"
+_RG_CONTEXTS = (8,)
+_RG_MAX_MATCHES = 500
+_SEMBLE_TOP_K = 50
+_KEYWORD_MAX_FILES = 20
+_KEYWORD_MODEL = "gpt-5-mini"
+_KEYWORD_CONCURRENCY = 16
+_JUDGE_MODEL = "gpt-5-mini"
+_JUDGE_CONCURRENCY = 8
+_JUDGE_RETRIES = 3
+_JUDGE_DEFAULT_SAMPLE = 200
+_JUDGE_SEMBLE_BUDGET = 2000
+_JUDGE_GREP_BUDGET = 16000
+
+_IMAGES_DIR = Path(__file__).parent.parent / "assets" / "images"
+_RESULTS_DIR = Path(__file__).parent / "results"
+
+_KEYWORD_PROMPT = """\
+Extract 1–3 grep-friendly search terms from the code search query below.
+Terms will be joined with | and used as a ripgrep regex pattern (not fixed strings).
+Prefer specific identifiers, class names, or technical keywords over generic words.
+Return only the pipe-separated terms — no explanation, no quotes, no punctuation.
+
+Examples:
+  flat_hash_map  →  flat_hash_map
+  absl::flat_hash_map and flat_hash_set containers  →  flat_hash_map|flat_hash_set
+  how does authentication middleware work  →  middleware|authentication
+  request lifecycle and error handling  →  lifecycle|error_handling
+  how are worker threads managed  →  worker_thread|thread_pool|ThreadPool
+
+Query: {query}
+Terms:\
+"""
+
+_JUDGE_PROMPT = """\
+You are evaluating whether retrieved code context is sufficient to answer a code search query.
+
+A code agent ran a search for the query below and got back the retrieved context shown. \
+Decide whether that context contains enough relevant code for an engineer to give a substantive \
+answer to the query (showing how something is implemented, where it lives, or how it works).
+
+Reply YES if the context contains code that directly addresses the query.
+Reply NO if the context is empty, off-topic, or only tangentially related.
+
+Reply with exactly one word: YES or NO.
+
+Query: {query}
+
+Retrieved context:
+{context}
+"""
+
+
+Curve: TypeAlias = list[tuple[int, int]]
+MethodCurves: TypeAlias = list[tuple[Curve, int]]
+
+
+@dataclass(frozen=True)
+class JudgeRecord:
+    """One LLM-judge sufficiency verdict."""
+
+    repo: str
+    query: str
+    category: str
+    method: str
+    tokens_used: int
+    answered: bool
+
+
+def _semble_units(index: SembleIndex, query: str) -> list[Chunk]:
+    """Top-K semble chunks as units, ordered by score."""
+    return [r.chunk for r in index.search(query, top_k=_SEMBLE_TOP_K)]
+
+
+def _rg_command(pattern: str, repo_dir: Path, *, fixed_strings: bool) -> list[str]:
+    """Build the rg command line, scoped to the same code-file universe semble indexes."""
+    cmd = ["rg", "--json"]
+    if fixed_strings:
+        cmd.append("--fixed-strings")
+    cmd.append("--ignore-case")
+    for glob in (*_RG_EXCLUDE_GLOBS, *_RG_INCLUDE_GLOBS):
+        cmd += ["--glob", glob]
+    cmd += [pattern, str(repo_dir)]
+    return cmd
+
+
+def _rg_matches(pattern: str, repo_dir: Path, *, fixed_strings: bool) -> list[tuple[str, int]]:
+    """Return (file_path, line_number) matches via rg --json, in rg's output order."""
+    try:
+        proc = subprocess.run(
+            _rg_command(pattern, repo_dir, fixed_strings=fixed_strings),
+            capture_output=True,
+            text=True,
+            timeout=30,
+        )
+    except subprocess.TimeoutExpired:
+        return []
+    if proc.returncode not in (0, 1):
+        return []
+    matches: list[tuple[str, int]] = []
+    for line in proc.stdout.splitlines():
+        if not line:
+            continue
+        try:
+            evt = json.loads(line)
+        except json.JSONDecodeError:
+            continue
+        if evt.get("type") != "match":
+            continue
+        data = evt.get("data", {})
+        path = data.get("path", {}).get("text")
+        ln = data.get("line_number")
+        if path and isinstance(ln, int):
+            matches.append((path, ln))
+    return matches
+
+
+def _ripgrep_units(query: str, repo_dir: Path, context: int) -> list[Chunk]:
+    """Ripgrep windows: files ranked by match count desc, windows merged within file."""
+    matches = _rg_matches(query, repo_dir, fixed_strings=True)
+    if not matches:
+        return []
+    per_file: dict[str, list[int]] = defaultdict(list)
+    for path, ln in matches[:_RG_MAX_MATCHES]:
+        per_file[path].append(ln)
+    ranked = sorted(per_file.items(), key=lambda kv: (-len(kv[1]), kv[0]))
+    units: list[Chunk] = []
+    for path, lines in ranked:
+        windows: list[list[int]] = []
+        for ln in sorted(set(lines)):
+            start, end = max(1, ln - context), ln + context
+            if windows and start <= windows[-1][1] + 1:
+                windows[-1][1] = max(windows[-1][1], end)
+            else:
+                windows.append([start, end])
+        try:
+            file_lines = Path(path).read_text(encoding="utf-8", errors="replace").splitlines()
+        except OSError:
+            continue
+        for start, end in windows:
+            end_clamped = min(end, len(file_lines))
+            text = "\n".join(file_lines[start - 1 : end_clamped])
+            units.append(Chunk(content=text, file_path=path, start_line=start, end_line=end_clamped))
+    return units
+
+
+def _grep_file_units(
+    pattern: str,
+    repo_dir: Path,
+    *,
+    fixed_strings: bool,
+    max_files: int | None = None,
+) -> list[Chunk]:
+    """Return whole matched files in match-count order."""
+    matches = _rg_matches(pattern, repo_dir, fixed_strings=fixed_strings)
+    if not matches:
+        return []
+    ranked = sorted(Counter(path for path, _ in matches[:_RG_MAX_MATCHES]).items(), key=lambda kv: (-kv[1], kv[0]))
+    if max_files is not None:
+        ranked = ranked[:max_files]
+    units: list[Chunk] = []
+    for path, _ in ranked:
+        try:
+            text = Path(path).read_text(encoding="utf-8", errors="replace")
+        except OSError:
+            continue
+        units.append(Chunk(content=text, file_path=path, start_line=1, end_line=text.count("\n") + 1))
+    return units
+
+
+def _keyword_method(cap: int) -> str:
+    """Return the method label for a keyword-grep file cap."""
+    return "keyword-grep+read" if cap == _KEYWORD_MAX_FILES else f"keyword-grep+read-top{cap}"
+
+
+def _retrieval_units_for_task(
+    index: SembleIndex,
+    task: Task,
+    repo_dir: Path,
+    *,
+    keywords: dict[str, str] | None,
+    keyword_caps: tuple[int, ...] = (_KEYWORD_MAX_FILES,),
+    include_ripgrep: bool = False,
+) -> list[tuple[str, list[Chunk]]]:
+    """Return retrieval-method units for a task in benchmark order."""
+    methods = [
+        ("semble", _semble_units(index, task.query)),
+        ("grep+read", _grep_file_units(task.query, repo_dir, fixed_strings=True)),
+    ]
+    if keywords is not None:
+        pattern = keywords.get(task.query, task.query)
+        methods.extend(
+            (_keyword_method(cap), _grep_file_units(pattern, repo_dir, fixed_strings=False, max_files=cap))
+            for cap in keyword_caps
+        )
+    if include_ripgrep:
+        methods.extend((f"ripgrep-c{c}", _ripgrep_units(task.query, repo_dir, c)) for c in _RG_CONTEXTS)
+    return methods
+
+
+_KEYWORD_TOKEN_RE = re.compile(r"^[A-Za-z_][A-Za-z0-9_:.\-]{0,63}$")
+
+
+def _sanitize_keywords(pattern: str) -> str | None:
+    """Accept a pipe-joined regex pattern only if every segment is an identifier-like token."""
+    parts = [p.strip() for p in pattern.split("|") if p.strip()]
+    if not parts or len(parts) > 5:
+        return None
+    if not all(_KEYWORD_TOKEN_RE.match(p) for p in parts):
+        return None
+    return "|".join(parts)
+
+
+async def _extract_keyword_pattern(query: str, client: AsyncOpenAI) -> str:
+    """Ask the LLM for 1–3 pipe-separated grep terms; sanitize, fall back to re.escape(query) on failure."""
+    fallback = re.escape(query)
+    try:
+        resp = await client.responses.create(
+            model=_KEYWORD_MODEL,
+            input=_KEYWORD_PROMPT.format(query=query),
+            reasoning={"effort": "minimal"},
+        )
+        raw = (resp.output_text or "").strip().strip('"').strip("'")
+        sanitized = _sanitize_keywords(raw)
+        return sanitized if sanitized is not None else fallback
+    except Exception:  # noqa: BLE001
+        return fallback
+
+
+async def _prefetch_keywords(queries: list[str]) -> dict[str, str]:
+    """Return {query: regex_pattern} for all unique queries via LLM keyword extraction."""
+    unique = list(dict.fromkeys(queries))
+    client = AsyncOpenAI()
+    sem = asyncio.Semaphore(_KEYWORD_CONCURRENCY)
+    results: dict[str, str] = {}
+
+    async def worker(q: str) -> None:
+        async with sem:
+            results[q] = await _extract_keyword_pattern(q, client)
+
+    await asyncio.gather(*(worker(q) for q in unique))
+    return results
+
+
+def _curve(units: list[Chunk], targets: tuple[Target, ...], enc: Any) -> Curve:
+    """Cumulative (tokens, covered_target_count) after each retrieved unit, starting at (0, 0)."""
+    covered = [False] * len(targets)
+    cumulative = 0
+    points: Curve = [(0, 0)]
+    for unit in units:
+        cumulative += len(enc.encode(unit.content, disallowed_special=()))
+        for i, tgt in enumerate(targets):
+            if not covered[i] and target_matches_location(unit.file_path, unit.start_line, unit.end_line, tgt):
+                covered[i] = True
+        points.append((cumulative, sum(covered)))
+    return points
+
+
+def _recall_at(curve: Curve, budget: int, n_total: int) -> float:
+    """Recall (covered_targets / n_total) at the largest cumulative-tokens point <= budget."""
+    if n_total == 0 or not curve:
+        return 0.0
+    covered_at = 0
+    for tokens, covered in curve:
+        if tokens > budget:
+            break
+        covered_at = covered
+    return covered_at / n_total
+
+
+def _mean_recall_at(curves: MethodCurves, budgets: tuple[int, ...] | list[int]) -> dict[int, float]:
+    """Mean recall at each budget across all queries."""
+    return {b: float(np.mean([_recall_at(c, b, n) for c, n in curves if n > 0])) if curves else 0.0 for b in budgets}
+
+
+def _mean_curve(curves: MethodCurves, grid: list[int]) -> list[float]:
+    """Mean recall over all queries at each grid budget."""
+    samples = [[_recall_at(c, b, n) for b in grid] for c, n in curves if n > 0]
+    return np.mean(samples, axis=0).tolist() if samples else [0.0] * len(grid)
+
+
+def _tokens_to_first_hit(curve: Curve) -> int | None:
+    """Cumulative tokens at which the first relevant target is covered, or None if never."""
+    for tokens, covered in curve:
+        if covered > 0:
+            return tokens
+    return None
+
+
+def _pairwise_reduction(semble: MethodCurves, other: MethodCurves) -> dict[str, float] | None:
+    """Median 'tokens to first hit' reduction, paired on queries where both methods hit."""
+    pairs: list[tuple[int, int]] = []
+    for (s_curve, _), (o_curve, _) in zip(semble, other, strict=True):
+        s = _tokens_to_first_hit(s_curve)
+        o = _tokens_to_first_hit(o_curve)
+        if s is not None and o is not None and o > 0:
+            pairs.append((s, o))
+    if not pairs:
+        return None
+    ratios = [s / o for s, o in pairs]
+    return {
+        "n_paired": float(len(pairs)),
+        "median_semble_tokens": float(np.median([s for s, _ in pairs])),
+        "median_other_tokens": float(np.median([o for _, o in pairs])),
+        "median_reduction": 1.0 - float(np.median(ratios)),
+        "mean_reduction": 1.0 - float(np.mean(ratios)),
+        "semble_better_pct": sum(1 for s, o in pairs if s < o) / len(pairs),
+    }
+
+
+def _format_context(units: list[Chunk], budget: int, enc: Any) -> tuple[str, int]:
+    """
+    Concatenate units with file headers, hard-capped at budget tokens.
+
+    Greedily appends blocks until the running estimate exceeds budget, then re-encodes the
+    joined string and slices by token IDs as the final guarantee.
+    """
+    if not units or budget <= 0:
+        return "(no context retrieved)", 0
+
+    parts: list[str] = []
+    rough_tokens = 0
+    for unit in units:
+        block = f"// {unit.location}\n{unit.content}"
+        parts.append(block)
+        rough_tokens += len(enc.encode(block, disallowed_special=())) + 2  # +2 for "\n\n"
+        if rough_tokens >= budget + 64:
+            break
+
+    context = "\n\n".join(parts)
+    ids = enc.encode(context, disallowed_special=())
+    if len(ids) > budget:
+        marker = "\n... [truncated]"
+        marker_ids = enc.encode(marker, disallowed_special=())
+        keep = max(0, budget - len(marker_ids))
+        context = enc.decode(ids[:keep]) + marker
+        ids = enc.encode(context, disallowed_special=())
+        if len(ids) > budget:
+            context = enc.decode(ids[:budget])
+            ids = enc.encode(context, disallowed_special=())
+    if len(ids) > budget:
+        raise AssertionError(f"context formatting exceeded budget: {len(ids)} > {budget}")
+    return context, len(ids)
+
+
+async def _judge_one(client: AsyncOpenAI, query: str, context: str) -> bool | None:
+    """Call the judge model; return True/False or None on parse failure or repeated errors."""
+    prompt = _JUDGE_PROMPT.format(query=query, context=context)
+    last_err: Exception | None = None
+    for attempt in range(_JUDGE_RETRIES):
+        try:
+            resp = await client.responses.create(
+                model=_JUDGE_MODEL,
+                input=prompt,
+                reasoning={"effort": "minimal"},
+            )
+            text = (resp.output_text or "").strip().upper()
+            if text.startswith("Y"):
+                return True
+            if text.startswith("N"):
+                return False
+            return None
+        except Exception as exc:  # noqa: BLE001
+            last_err = exc
+            await asyncio.sleep(1.5 * (attempt + 1))
+    print(f"  judge failed after {_JUDGE_RETRIES} retries: {last_err}", file=sys.stderr)
+    return None
+
+
+async def _judge_many(pending: list[tuple[Task, str, str, int]], concurrency: int) -> list[JudgeRecord]:
+    """Run judge calls with bounded concurrency."""
+    client = AsyncOpenAI()
+    sem = asyncio.Semaphore(concurrency)
+    results: list[JudgeRecord | None] = [None] * len(pending)
+
+    async def worker(i: int, task: Task, method: str, context: str, tokens: int) -> None:
+        async with sem:
+            verdict = await _judge_one(client, task.query, context)
+            if verdict is None:
+                return
+            results[i] = JudgeRecord(
+                repo=task.repo,
+                query=task.query,
+                category=task.category,
+                method=method,
+                tokens_used=tokens,
+                answered=verdict,
+            )
+
+    await asyncio.gather(*(worker(i, *p) for i, p in enumerate(pending)))
+    return [r for r in results if r is not None]
+
+
+def _stratified_sample(tasks: list[Task], n: int, seed: int) -> list[Task]:
+    """Sample n tasks stratified across categories."""
+    rng = random.Random(seed)
+    by_cat: dict[str, list[Task]] = defaultdict(list)
+    for task in tasks:
+        by_cat[task.category].append(task)
+    cats = sorted(by_cat)
+    per_cat = n // len(cats)
+    picked: list[Task] = []
+    for cat in cats:
+        bucket = by_cat[cat]
+        rng.shuffle(bucket)
+        picked.extend(bucket[:per_cat])
+    rng.shuffle(picked)
+    return picked
+
+
+def _evaluate_repo_recall(
+    index: SembleIndex,
+    tasks: list[Task],
+    repo_dir: Path,
+    enc: Any,
+    keywords: dict[str, str] | None,
+) -> dict[str, MethodCurves]:
+    """Build per-method curves for every task in the repo."""
+    methods: dict[str, MethodCurves] = defaultdict(list)
+
+    for task in tasks:
+        targets = task.all_relevant
+        n = len(targets)
+        for method, units in _retrieval_units_for_task(
+            index,
+            task,
+            repo_dir,
+            keywords=keywords,
+            include_ripgrep=True,
+        ):
+            methods[method].append((_curve(units, targets, enc), n))
+    return dict(methods)
+
+
+def _build_pending_judge(
+    tasks: list[Task],
+    repo_specs: dict[str, Any],
+    model: StaticModel,
+    enc: Any,
+    keywords: dict[str, str],
+    keyword_caps: tuple[int, ...],
+) -> list[tuple[Task, str, str, int]]:
+    """Build (task, method, context, tokens) inputs for the LLM judge across all sampled tasks."""
+    pending: list[tuple[Task, str, str, int]] = []
+    print(f"\n{'Repo':<22} {'Tasks':>6} {'Time':>8}", file=sys.stderr)
+    print(f"{'-' * 22} {'-' * 6} {'-' * 8}", file=sys.stderr)
+    for repo, task_list in sorted(grouped_tasks(tasks).items()):
+        spec = repo_specs[repo]
+        started = time.perf_counter()
+        index = SembleIndex.from_path(spec.benchmark_dir, model=model)
+        for task in task_list:
+            for method, units in _retrieval_units_for_task(
+                index,
+                task,
+                spec.benchmark_dir,
+                keywords=keywords,
+                keyword_caps=keyword_caps,
+            ):
+                budget = _JUDGE_SEMBLE_BUDGET if method == "semble" else _JUDGE_GREP_BUDGET
+                context, tokens = _format_context(units, budget, enc)
+                pending.append((task, method, context, tokens))
+        print(f"{repo:<22} {len(task_list):>6} {time.perf_counter() - started:>7.1f}s", file=sys.stderr)
+    return pending
+
+
+def _aggregate_judge(records: list[JudgeRecord], attempted_per_method: dict[str, int]) -> dict[str, dict[str, Any]]:
+    """Group records by method and compute answer rate, mean tokens, missing-verdict count."""
+    by_method: dict[str, list[JudgeRecord]] = defaultdict(list)
+    for r in records:
+        by_method[r.method].append(r)
+    out: dict[str, dict[str, Any]] = {}
+    for method, attempted in attempted_per_method.items():
+        recs = by_method.get(method, [])
+        n_yes = sum(1 for r in recs if r.answered)
+        by_cat: dict[str, list[bool]] = defaultdict(list)
+        for r in recs:
+            by_cat[r.category].append(r.answered)
+        out[method] = {
+            "n": len(recs),
+            "n_attempted": attempted,
+            "n_failed": attempted - len(recs),
+            "answer_rate": n_yes / len(recs) if recs else 0.0,
+            "mean_tokens_retrieved": float(np.mean([r.tokens_used for r in recs])) if recs else 0.0,
+            "answer_rate_by_category": {c: sum(v) / len(v) for c, v in sorted(by_cat.items())},
+        }
+    return out
+
+
+_PLOT_STYLE: dict[str, dict[str, object]] = {
+    "semble": {"label": "semble", "color": "#1a5fa8", "linewidth": 2.4, "zorder": 4},
+    "keyword-grep+read": {
+        "label": "keyword grep + read file",
+        "color": "#d35400",
+        "linewidth": 1.8,
+        "zorder": 3,
+        "linestyle": "--",
+    },
+    "grep+read": {"label": "naive grep + read file", "color": "#922b21", "linewidth": 1.8, "zorder": 3},
+    "ripgrep-c8": {
+        "label": "ripgrep -C 8 (snippets)",
+        "color": "#707070",
+        "linewidth": 1.4,
+        "zorder": 2,
+        "linestyle": "--",
+    },
+}
+
+
+def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
+    """Render a recall-vs-tokens curve from a recall-mode payload."""
+    plot_data = payload["plot"]
+    budgets = plot_data["budgets"]
+    recalls = plot_data["recall"]
+    n_queries = payload.get("n_queries", "?")
+
+    fig, ax = plt.subplots(figsize=(8, 5))
+    fig.patch.set_facecolor("white")
+    ax.set_facecolor("white")
+    ax.grid(axis="y", color="#e8e8e8", linewidth=0.7, zorder=0)
+    ax.grid(axis="x", color="#f0f0f0", linewidth=0.5, zorder=0)
+    ax.set_axisbelow(True)
+    for spine in ("top", "right"):
+        ax.spines[spine].set_visible(False)
+    for spine in ("left", "bottom"):
+        ax.spines[spine].set_color("#cccccc")
+
+    for method, style in _PLOT_STYLE.items():
+        if method not in recalls:
+            continue
+        ax.plot(
+            budgets,
+            recalls[method],
+            label=style["label"],
+            color=style["color"],
+            linewidth=style["linewidth"],
+            linestyle=style.get("linestyle", "-"),
+            zorder=style["zorder"],
+        )
+
+    ax.set_xscale("log")
+    ax.set_xlim(min(budgets), max(budgets))
+    ax.set_ylim(0.0, 1.02)
+    ax.set_xlabel("Retrieved context tokens", fontsize=10, color="#444444")
+    ax.set_ylabel("Recall (relevant files surfaced)", fontsize=10, color="#444444")
+    ax.set_title(
+        f"Context efficiency: recall vs. retrieved tokens  (n={n_queries} queries)",
+        fontsize=12,
+        color="#222222",
+        pad=12,
+    )
+    ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda v, _: f"{v / 1000:.0f}k" if v >= 1000 else f"{v:.0f}"))
+    ax.tick_params(labelsize=9, colors="#555555")
+    ax.legend(loc="lower right", fontsize=9, frameon=True, framealpha=0.95, edgecolor="#dddddd")
+    fig.tight_layout()
+    fig.savefig(out_path, dpi=150, bbox_inches="tight")
+    print(f"Saved plot to {out_path}", file=sys.stderr)
+
+
+def _print_recall_summary(method_curves: dict[str, MethodCurves]) -> dict[str, dict[int, float]]:
+    """Print and return recall@budget per method."""
+    print(f"\nTokenizer: {_TOKENIZER_NAME}\n", file=sys.stderr)
+    print(f"{'Method':<20} " + "  ".join(f"{b:>7}" for b in _BUDGETS), file=sys.stderr)
+    print(f"{'-' * 20} " + "  ".join(f"{'-' * 7:>7}" for _ in _BUDGETS), file=sys.stderr)
+    summary: dict[str, dict[int, float]] = {}
+    for method, curves in method_curves.items():
+        recall = _mean_recall_at(curves, _BUDGETS)
+        summary[method] = recall
+        print(f"{method:<20} " + "  ".join(f"{recall[b]:>7.3f}" for b in _BUDGETS), file=sys.stderr)
+    return summary
+
+
+def _print_first_hit_summary(method_curves: dict[str, MethodCurves]) -> dict[str, dict[str, float]]:
+    """Print and return pairwise tokens-to-first-hit reductions vs semble."""
+    print("\nTokens to first relevant file (semble vs other, paired)", file=sys.stderr)
+    print(f"{'vs':<20} {'n':>5}  {'med-semble':>10}  {'med-other':>10}  {'med-reduce':>10}", file=sys.stderr)
+    print(f"{'-' * 20} {'-' * 5}  {'-' * 10}  {'-' * 10}  {'-' * 10}", file=sys.stderr)
+    reductions: dict[str, dict[str, float]] = {}
+    for method, curves in method_curves.items():
+        if method == "semble":
+            continue
+        red = _pairwise_reduction(method_curves["semble"], curves)
+        if red is None:
+            continue
+        reductions[method] = red
+        print(
+            f"{method:<20} {int(red['n_paired']):>5}  "
+            f"{red['median_semble_tokens']:>10.0f}  {red['median_other_tokens']:>10.0f}  "
+            f"{red['median_reduction']:>10.1%}",
+            file=sys.stderr,
+        )
+    return reductions
+
+
+def run_recall(args: argparse.Namespace) -> None:
+    """Run the recall-vs-token-budget benchmark."""
+    repo_specs, tasks = load_filtered_tasks(args.repo or None, args.language or None)
+
+    print("Loading tokenizer + model...", file=sys.stderr)
+    enc = tiktoken.get_encoding(_TOKENIZER_NAME)
+    model = StaticModel.from_pretrained(_DEFAULT_MODEL_NAME)
+
+    keywords: dict[str, str] | None = None
+    if not args.no_keyword:
+        try:
+            print("Pre-fetching keyword patterns (requires OPENAI_API_KEY)...", file=sys.stderr)
+            keywords = asyncio.run(_prefetch_keywords([t.query for t in tasks]))
+            print(f"  done ({len(keywords)} unique queries)", file=sys.stderr)
+        except Exception as exc:  # noqa: BLE001
+            print(f"  keyword pre-fetch failed ({exc}); skipping keyword-grep+read", file=sys.stderr)
+
+    method_curves: dict[str, MethodCurves] = defaultdict(list)
+    print(f"\n{'Repo':<22} {'Language':<12} {'Tasks':>6} {'Time':>8}", file=sys.stderr)
+    print(f"{'-' * 22} {'-' * 12} {'-' * 6} {'-' * 8}", file=sys.stderr)
+    for repo, repo_task_list in sorted(grouped_tasks(tasks).items()):
+        spec = repo_specs[repo]
+        started = time.perf_counter()
+        index = SembleIndex.from_path(spec.benchmark_dir, model=model)
+        per_method = _evaluate_repo_recall(index, repo_task_list, spec.benchmark_dir, enc, keywords)
+        for m, lst in per_method.items():
+            method_curves[m].extend(lst)
+        print(
+            f"{repo:<22} {spec.language:<12} {len(repo_task_list):>6} {time.perf_counter() - started:>7.1f}s",
+            file=sys.stderr,
+        )
+
+    summary = _print_recall_summary(method_curves)
+    reductions = _print_first_hit_summary(method_curves)
+
+    payload: dict[str, Any] = {
+        "tool": "context-efficiency-recall",
+        "tokenizer": _TOKENIZER_NAME,
+        "budgets": list(_BUDGETS),
+        "n_queries": len(method_curves["semble"]),
+        "recall_at_budget": {m: {str(b): round(v, 4) for b, v in r.items()} for m, r in summary.items()},
+        "first_hit_reduction": {m: {k: round(v, 4) for k, v in d.items()} for m, d in reductions.items()},
+        "plot": {
+            "budgets": _PLOT_BUDGETS,
+            "recall": {m: [round(x, 4) for x in _mean_curve(c, _PLOT_BUDGETS)] for m, c in method_curves.items()},
+        },
+    }
+    if args.repo or args.language:
+        print(json.dumps(payload, indent=2))
+        return
+
+    out = save_results("context-efficiency-recall", payload)
+    print(f"\nResults saved to {out}", file=sys.stderr)
+    if not args.no_plot:
+        _IMAGES_DIR.mkdir(parents=True, exist_ok=True)
+        _plot_recall_vs_tokens(payload, _IMAGES_DIR / "recall_vs_tokens.png")
+
+
+def run_judge(args: argparse.Namespace) -> None:
+    """Run the LLM-as-judge sufficiency benchmark on a stratified sample."""
+    repo_specs, tasks = load_filtered_tasks()
+    sample = _stratified_sample(tasks, args.sample, args.seed)
+    print(f"Sampled {len(sample)} queries (seed={args.seed})", file=sys.stderr)
+
+    print("Loading model + tokenizer...", file=sys.stderr)
+    enc = tiktoken.get_encoding(_TOKENIZER_NAME)
+    model = StaticModel.from_pretrained(_DEFAULT_MODEL_NAME)
+
+    print("Pre-fetching keyword patterns...", file=sys.stderr)
+    keywords = asyncio.run(_prefetch_keywords([t.query for t in sample]))
+    print(f"  done ({len(keywords)} unique queries)", file=sys.stderr)
+
+    pending = _build_pending_judge(sample, repo_specs, model, enc, keywords, tuple(args.keyword_caps))
+    attempted_per_method: dict[str, int] = defaultdict(int)
+    for _, method, _, _ in pending:
+        attempted_per_method[method] += 1
+
+    print(f"\nJudging {len(pending)} (query, method) pairs with {_JUDGE_MODEL}...", file=sys.stderr)
+    started = time.perf_counter()
+    records = asyncio.run(_judge_many(pending, args.concurrency))
+    elapsed = time.perf_counter() - started
+    print(f"  done in {elapsed:.1f}s ({len(records)}/{len(pending)} returned a verdict)", file=sys.stderr)
+    n_missing = len(pending) - len(records)
+    if n_missing:
+        print(f"  WARNING: {n_missing} verdicts missing — see n_failed per method.", file=sys.stderr)
+
+    summary = _aggregate_judge(records, dict(attempted_per_method))
+    print(f"\n{'Method':<24} {'n':>5} {'answer_rate':>12} {'mean_tokens':>12}", file=sys.stderr)
+    print(f"{'-' * 24} {'-' * 5} {'-' * 12} {'-' * 12}", file=sys.stderr)
+    for method, info in summary.items():
+        print(
+            f"{method:<24} {info['n']:>5} {info['answer_rate']:>12.3f} {info['mean_tokens_retrieved']:>12.0f}",
+            file=sys.stderr,
+        )
+
+    semble_retrieval = summary.get("semble", {}).get("mean_tokens_retrieved", 0.0)
+    print("\nRaw retrieval tokens (no fallback model):", file=sys.stderr)
+    raw_reductions: dict[str, float] = {}
+    for method, info in summary.items():
+        other = info["mean_tokens_retrieved"]
+        red = 1.0 - semble_retrieval / other if method != "semble" and other > 0 else 0.0
+        raw_reductions[method] = red
+        suffix = f"  reduction vs semble: {red:.1%}" if method != "semble" else ""
+        print(f"  {method:<24} {other:>9.0f}{suffix}", file=sys.stderr)
+
+    fallback = args.fallback_tokens
+    e_costs = {m: s["mean_tokens_retrieved"] + ((1.0 - s["answer_rate"]) * fallback) for m, s in summary.items()}
+    print(f"\nExpected end-to-end tokens (pessimistic fallback = {fallback:.0f} tokens):", file=sys.stderr)
+    for method, c in e_costs.items():
+        print(f"  {method:<24} {c:>9.0f}", file=sys.stderr)
+
+    if "semble" in e_costs:
+        for method in ("grep+read", "keyword-grep+read"):
+            if method in e_costs and e_costs[method] > 0:
+                reduction = 1.0 - e_costs["semble"] / e_costs[method]
+                print(f"Reduction (semble vs {method}, end-to-end): {reduction:.1%}", file=sys.stderr)
+
+    payload = {
+        "tool": "context-efficiency-judge",
+        "judge_model": _JUDGE_MODEL,
+        "tokenizer": _TOKENIZER_NAME,
+        "sample_size": len(sample),
+        "seed": args.seed,
+        "budgets": {"semble": _JUDGE_SEMBLE_BUDGET, "grep+read": _JUDGE_GREP_BUDGET},
+        "fallback_tokens": fallback,
+        "keyword_patterns": keywords,
+        "summary": summary,
+        "raw_retrieval_reduction_vs_semble": {m: round(v, 4) for m, v in raw_reductions.items() if m != "semble"},
+        "expected_cost": e_costs,
+        "records": [
+            {
+                "repo": r.repo,
+                "query": r.query,
+                "category": r.category,
+                "method": r.method,
+                "tokens": r.tokens_used,
+                "answered": r.answered,
+            }
+            for r in records
+        ],
+    }
+    out = save_results("context-efficiency-judge", payload)
+    print(f"\nResults saved to {out}", file=sys.stderr)
+
+
+def run_plot(args: argparse.Namespace) -> None:
+    """Plot recall-vs-tokens from a saved recall-mode payload."""
+    matches = sorted(_RESULTS_DIR.glob("context-efficiency-recall-*.json"))
+    in_path = args.input or (matches[-1] if matches else None)
+    if in_path is None:
+        raise SystemExit(f"No recall results found in {_RESULTS_DIR}")
+    payload = json.loads(in_path.read_text(encoding="utf-8"))
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    _plot_recall_vs_tokens(payload, args.output)
+
+
+def _parse_args() -> argparse.Namespace:
+    """Parse CLI arguments."""
+    parser = argparse.ArgumentParser(description="Context-efficiency benchmark: semble vs grep workflows.")
+    parser.set_defaults(func=run_recall, repo=[], language=[], no_keyword=False, no_plot=False)
+    sub = parser.add_subparsers(dest="mode", required=False)
+
+    recall = sub.add_parser("recall", help="Recall vs. token-budget across all queries (default).")
+    add_filter_args(recall)
+    recall.add_argument("--no-keyword", action="store_true", help="Skip the LLM keyword-extraction baseline.")
+    recall.add_argument("--no-plot", action="store_true", help="Skip plotting after the run.")
+    recall.set_defaults(func=run_recall)
+
+    judge = sub.add_parser("judge", help="LLM-as-judge sufficiency on a stratified sample.")
+    judge.add_argument("--sample", type=int, default=_JUDGE_DEFAULT_SAMPLE, help="Number of stratified queries.")
+    judge.add_argument("--seed", type=int, default=42, help="Random seed for sampling.")
+    judge.add_argument("--concurrency", type=int, default=_JUDGE_CONCURRENCY, help="Concurrent judge calls.")
+    judge.add_argument(
+        "--fallback-tokens",
+        type=float,
+        default=7474.0,
+        help="Tokens an agent spends to fall back on a miss (pessimistic default).",
+    )
+    judge.add_argument(
+        "--keyword-caps",
+        type=int,
+        nargs="+",
+        default=[_KEYWORD_MAX_FILES],
+        help="Top-N file caps for keyword-grep+read sensitivity sweep.",
+    )
+    judge.set_defaults(func=run_judge)
+
+    plot = sub.add_parser("plot", help="Regenerate the recall-vs-tokens plot from a saved JSON.")
+    plot.add_argument("--input", type=Path, default=None, help="Path to recall results (default: newest).")
+    plot.add_argument("--output", type=Path, default=_IMAGES_DIR / "recall_vs_tokens.png", help="Output PNG path.")
+    plot.set_defaults(func=run_plot)
+
+    return parser.parse_args()
+
+
+def main() -> None:
+    """Dispatch to the requested mode."""
+    args = _parse_args()
+    args.func(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/data.py b/benchmarks/data.py
index fb87df7..78e7b28 100644
--- a/benchmarks/data.py
+++ b/benchmarks/data.py
@@ -1,7 +1,10 @@
+import argparse
 import json
 import subprocess
+from collections.abc import Sequence
 from dataclasses import dataclass
 from pathlib import Path
+from typing import Any
 
 BENCH_ROOT = Path.home() / ".cache" / "semble-bench"
 BENCHMARKS_DIR = Path(__file__).parent
@@ -140,6 +143,38 @@ def apply_task_filters(
     return [task for task in filtered if not languages or task.language in languages]
 
 
+def add_filter_args(parser: argparse.ArgumentParser, *, verbose: bool = False) -> None:
+    """Add shared benchmark repo/language filter arguments."""
+    parser.add_argument("--repo", action="append", default=[], help="Limit to one or more repo names.")
+    parser.add_argument("--language", action="append", default=[], help="Limit to one or more languages.")
+    if verbose:
+        parser.add_argument("--verbose", action="store_true", help="Print per-query results.")
+
+
+def load_filtered_tasks(
+    repos: list[str] | None = None, languages: list[str] | None = None
+) -> tuple[dict[str, RepoSpec], list[Task]]:
+    """Load available repo specs and matching tasks, exiting if the selection is empty."""
+    repo_specs = available_repo_specs()
+    tasks = apply_task_filters(load_tasks(repo_specs=repo_specs), repos=repos, languages=languages)
+    if not tasks:
+        raise SystemExit("No benchmark tasks matched the requested filters.")
+    return repo_specs, tasks
+
+
+def summarize_modes(results: Sequence[Any], modes: Sequence[str]) -> dict[str, dict[str, float]]:
+    """Return average NDCG@10 and p50 latency for each mode."""
+    summary: dict[str, dict[str, float]] = {}
+    for mode in modes:
+        mode_results = [r for r in results if r.mode == mode]
+        n = len(mode_results)
+        summary[mode] = {
+            "avg_ndcg10": round(sum(r.ndcg10 for r in mode_results) / n, 4) if n else 0.0,
+            "avg_p50_ms": round(sum(r.p50_ms for r in mode_results) / n, 1) if n else 0.0,
+        }
+    return summary
+
+
 def path_matches(file_path: str, target_path: str) -> bool:
     """Return True if either path is a suffix of the other (handles absolute vs relative paths)."""
     norm_file = file_path.replace("\\", "/")
@@ -173,11 +208,7 @@ def current_sha() -> str:
 
 
 def results_path(method: str) -> Path:
-    """Return the path where results for this method and the current HEAD SHA will be saved.
-
-    :param method: Short tool/method label (e.g. 'ripgrep', 'colgrep').
-    :return: Path of the form benchmarks/results/<method>-<sha12>.json.
-    """
+    """Return benchmarks/results/<method>-<sha12>.json for the current HEAD."""
     sha = current_sha()
     results_dir = BENCHMARKS_DIR / "results"
     results_dir.mkdir(exist_ok=True)
@@ -185,12 +216,7 @@ def results_path(method: str) -> Path:
 
 
 def save_results(method: str, payload: object) -> Path:
-    """Write payload to benchmarks/results/<method>-<sha12>.json and return the path.
-
-    :param method: Short tool/method label used as the filename prefix (e.g. 'ripgrep').
-    :param payload: JSON-serialisable object to write.
-    :return: Path to the written file.
-    """
+    """Write JSON results and return the output path."""
     out_path = results_path(method)
     out_path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
     return out_path
diff --git a/benchmarks/results/context-efficiency-judge-6248855cfef8.json b/benchmarks/results/context-efficiency-judge-6248855cfef8.json
new file mode 100644
index 0000000..80804f7
--- /dev/null
+++ b/benchmarks/results/context-efficiency-judge-6248855cfef8.json
@@ -0,0 +1,9804 @@
+{
+  "tool": "context-efficiency-judge",
+  "judge_model": "gpt-5-mini",
+  "tokenizer": "cl100k_base",
+  "sample_size": 198,
+  "seed": 42,
+  "budgets": {
+    "semble": 2000,
+    "grep+read": 16000
+  },
+  "fallback_tokens": 7474.0,
+  "keyword_patterns": {
+    "WebSocket upgrade and handler": "WebSocket\\ upgrade\\ and\\ handler",
+    "how download requests save responses to disk": "download_request|save_response_to_disk|download_to_file",
+    "serving a file index page for a folder on disk": "file_index|directory_listing|index_page",
+    "flow operators map filter transform and collect": "map|filter|collect",
+    "how middleware chains context transformations between procedures": "middleware|chain|context_transformation",
+    "how HTTP proxy tunnel CONNECT method is initiated": "CONNECT|HTTP_PROXY|proxy_tunnel",
+    "ObjectMapper": "ObjectMapper|ObjectMapper::readValue|com.fasterxml.jackson.databind.ObjectMapper",
+    "Notify": "notify|notification|NotifyListener",
+    "how SETRANGE modifies a substring within a string value": "how\\ SETRANGE\\ modifies\\ a\\ substring\\ within\\ a\\ string\\ value",
+    "parameter type coercion and value validation": "type_coercion|value_validation",
+    "SqlMapper": "SqlMapper",
+    "how plugin installations and updates are managed with tasks": "plugin_install|plugin_update|task_scheduler",
+    "how mini.pick implements a fuzzy finder picker": "mini.pick|fuzzy_finder|picker",
+    "timer implementation and heap-based scheduling": "timer_impl|heap_scheduler|timing_wheel",
+    "how the async mutex prevents concurrent access": "async_mutex|AsyncMutex|mutex_async",
+    "BoxedIntoRoute and type-erased route storage": "BoxedIntoRoute|type-erased|route_storage",
+    "Uri": "Uri|URI|IUriProvider",
+    "CommandDefinition": "CommandDefinition",
+    "jump plugin for bookmarking directories": "jump|bookmark|directory_bookmark",
+    "AsyncClient": "AsyncClient|Async::Client|AsyncClientImpl",
+    "JsonReader base class and token reading API": "JsonReader|token|TokenType",
+    "radix tree path matching": "radix_tree|radix_trie|path_matching",
+    "how res.redirect builds the Location header and status code": "how\\ res\\.redirect\\ builds\\ the\\ Location\\ header\\ and\\ status\\ code",
+    "how Monad composes dependent effectful computations with flatMap": "Monad|flatMap|dependent_effects",
+    "before and after filter hooks for the request lifecycle": "before_filter|after_filter|request_lifecycle",
+    "test runner worker pool scheduling and execution": "worker_pool|test_runner|scheduling",
+    "how xmonad handles floating windows separately from tiled ones": "xmonad|floating|tiled",
+    "how listeners are subscribed and notified after state changes": "listener|subscribe|notify",
+    "StringUtils": "StringUtils|string_utils|StringUtility",
+    "how curl decides whether to retry a failed request": "curl_retry|CURLOPT_RETRY|retry_after",
+    "how backpressure and flow control work in streaming": "backpressure|flow_control|streaming_protocol",
+    "URL": "url|URL|Uri",
+    "Sinatra::ShowExceptions": "Sinatra::ShowExceptions",
+    "InterceptorManager": "InterceptorManager",
+    "MiddlewareBuilder": "MiddlewareBuilder",
+    "how query parameters are extracted and decoded from URIs": "query_param|parse_qs|URLDecoder",
+    "inferProcedureInput": "inferProcedureInput|infer_procedure_input|InferProcedureInput",
+    "Channel": "Channel|channel_layer|broadcast",
+    "APIRouter": "APIRouter",
+    "how discriminated unions select the right model variant": "discriminated_union|tagged_union|variant_selection",
+    "how Phoenix logs request and response lifecycle events": "Phoenix.Logger|telemetry|Plug.Conn",
+    "event hooks system": "event_hook|hook_system|event_listener",
+    "how DSL protocols define layout anchor properties like left, top, edges, and center for views and guides": "layout_anchor|anchor_property|layout_guide",
+    "how HttpRoutes define pattern-matched HTTP request handlers": "HttpRoutes|pattern-matched|request_handler",
+    "how subword token weights are aggregated for whole-word embeddings": "subword_token|whole_word_embedding|token_weighting",
+    "rvm ruby version manager plugin": "rvm|ruby-version|rvm-plugin",
+    "battery level detection for shell prompt": "battery_level|power_supply|upower",
+    "how app.render resolves views and calls the template engine": "app.render|render_views|template_engine",
+    "syslog handler for system-level logging": "syslog|SysLogHandler|systemd-journald",
+    "how type inference extracts input and output types from procedures": "type_inference|input_type|output_type",
+    "how MethodRouter dispatches GET POST PUT DELETE by HTTP method": "MethodRouter|dispatch|HTTP_METHOD",
+    "HCursor and cursor navigation for traversing JSON trees": "HCursor|cursor_navigation|JsonCursor",
+    "how RDB persistence serializes the database to disk": "RDB|persistence|serialize",
+    "how are static files served": "static_file|static_files|serve_static",
+    "Kleisli": "Kleisli|kleisli|KleisliArrow",
+    "Sinatra::Helpers": "Sinatra::Helpers|Sinatra::Base|helpers",
+    "how nvm_has checks whether a command exists on the system": "nvm_has|nvm_has_function|command_exists",
+    "how bats should retry a flaky test": "flaky_test|retry_logic|test_retry",
+    "AnyRouter": "AnyRouter",
+    "request and response interceptors": "interceptor|request_interceptor|response_interceptor",
+    "adl_serializer": "adl_serializer",
+    "toString builder for human-readable object descriptions": "toString|to_string|toStringBuilder",
+    "spawning and aborting async subprocess commands with uv": "uv_spawn|uv_process_kill|libuv",
+    "how zls provides goto definition for symbols": "goto_definition|definition_provider|symbol_resolution",
+    "Utils": "Utils|UtilsImpl|util_namespace",
+    "Codec combining Encoder and Decoder for bidirectional conversion": "Codec|Encoder|Decoder",
+    "Mutex": "Mutex|std::mutex|pthread_mutex_t",
+    "TypeFactory": "TypeFactory",
+    "how server-sent events and subscriptions are streamed to the client": "server-sent-events|SSE|EventSource",
+    "how vitest discovers and runs test files": "vitest|vite-plugin-test|testRegex",
+    "how the PandocMonad typeclass abstracts IO for readers and writers": "PandocMonad|Reader|Writer",
+    "hub github cli aliases and helpers": "hub|github|cli",
+    "how toJSONKeyText creates a ToJSONKey instance from a text function": "toJSONKeyText|ToJSONKey|ToJSONKeyHelper",
+    "RotatingFileHandler": "RotatingFileHandler|RotatingFileHandler.py|RotatingFileHandlerConfig",
+    "HTTPStatusError": "HTTPStatusError",
+    "how zls DocumentStore loads and caches Zig source files": "how\\ zls\\ DocumentStore\\ loads\\ and\\ caches\\ Zig\\ source\\ files",
+    "how curl resolves and connects to a remote host": "curl_resolve|Curl_connect|CURLOPT_RESOLVE",
+    "basic_json": "basic_json|nlohmann::basic_json|to_json",
+    "middleware pipeline composition": "middleware|pipeline|composition",
+    "Application": "Application|App|application.py",
+    "reducer type definition": "reducer|Reducer|ReducerType",
+    "how debug.print outputs to stderr for debugging": "debug.print|stderr|debugging",
+    "FromRequest": "FromRequest|FromRequestParts|axum::extract::FromRequest",
+    "ssh agent plugin for managing ssh keys": "ssh-agent|ssh-agent-plugin|ssh-key-agent",
+    "how mini.completion provides LSP-based autocompletion": "mini.completion|LSP|autocompletion",
+    "node version manager nvm integration": "nvm|node_version_manager|nvmrc",
+    "how client plugins intercept and transform requests and responses": "client_plugin|intercept_request|transform_response",
+    "absl::AnyInvocable for move-only callable wrapper": "AnyInvocable|absl::AnyInvocable|move-only",
+    "case-insensitive hash for request params": "case_insensitive_hash|CaseInsensitiveHash|case_insensitive_map",
+    "how nvm finds the nearest .nvmrc file by walking up the directory tree": "how\\ nvm\\ finds\\ the\\ nearest\\ \\.nvmrc\\ file\\ by\\ walking\\ up\\ the\\ directory\\ tree",
+    "how text sections are rendered in the lazy view": "lazy_view|text_section|render_section",
+    "how zls generates diagnostic errors and warnings": "zls|diagnostic|diagnostics",
+    "PowerShell completion generation": "powershell_completion|tab_completion|CompletionResult",
+    "Sinatra::Application": "Sinatra::Application|Sinatra::Base|Sinatra::Delegator",
+    "BackgroundTasks": "BackgroundTasks|background_task|BackgroundTask",
+    "Rails::Paths": "Rails::Paths|Rails::Application::Paths|rails_paths",
+    "how does Flask convert a view return value into a response object": "flask|make_response|flask.Response",
+    "how route groups and route collections compose routes": "route_group|route_collection|compose_routes",
+    "Request": "http_request|HTTPRequest|request_handler",
+    "IMessagePackFormatter": "IMessagePackFormatter|MessagePackFormatter|IMessagePackSerializer",
+    "how Kleisli composes functions that return monadic values": "Kleisli|monad|bind",
+    "how Phoenix routes are defined and dispatched to controller actions": "Phoenix.Router|route|controller_action",
+    "Command": "command|CLI|execute",
+    "how server trust evaluation handles SSL certificate pinning": "certificate_pinning|ssl_pinning|trust_evaluation",
+    "how slave keys with expiry are tracked in replication": "replication|slave_keys|expire_keys",
+    "how the event loop handles file events and time events": "event_loop|file_event|time_event",
+    "ObjectWriter": "ObjectWriter|ObjectWriter::Write|IObjectWriter",
+    "how lazy.nvim loads a plugin on demand when its trigger fires": "lazy.nvim|on_event|lazy.require",
+    "Monad": "Monad|monad|Functor",
+    "naming conventions for controllers and view modules": "controller|controller_name|view_module",
+    "how pandoc reads RST reStructuredText documents": "pandoc|reStructuredText|rst",
+    "BaseHTTPMiddleware": "BaseHTTPMiddleware|BaseHTTPMiddlewareDispatch",
+    "how PCA reduces embedding dimensionality": "PCA|principal_component_analysis|dimensionality_reduction",
+    "how tasks that are not Send can run on a single thread": "how\\ tasks\\ that\\ are\\ not\\ Send\\ can\\ run\\ on\\ a\\ single\\ thread",
+    "how is response validation and serialization applied before sending": "response_validation|response_serialization|serialize_response",
+    "UDP socket send and receive": "udp_send|udp_receive|recvfrom",
+    "Args": "Args|argparse|CommandLineParser",
+    "how app.handle dispatches requests through the middleware stack": "app.handle|middleware_stack|dispatches",
+    "how curl checks if HTTP If-Modified-Since condition is met": "If-Modified-Since|If-None-Match|curl_conditional_request",
+    "field_validator": "field_validator|FieldValidator|validate_field",
+    "saving and loading models from disk": "save_model|load_model|model_checkpoint",
+    "how the preprocessed test source is evaluated": "preprocessed_test|test_source|evaluate",
+    "StateManager": "StateManager",
+    "how proxy settings are read from environment variables": "http_proxy|HTTP_PROXY|no_proxy",
+    "fake timers and timer mocking utilities": "fake_timers|timer_mock|time_mocking",
+    "Rack::Response": "Rack::Response|Rack::Request|middleware",
+    "how Encoder typeclass converts Scala values to JSON": "Encoder|typeclass|toJson",
+    "how typeMismatch produces an error message for wrong JSON types": "typeMismatch|DecodingError|expected_value_type",
+    "ConstraintMaker": "ConstraintMaker|MASConstraintMaker|makeConstraints",
+    "how derive macros parse and apply field-level serde attributes": "derive_macro|serde_attribute|field_attributes",
+    "available request options and their configuration": "request_options|RequestOptions|request_config",
+    "how zls computes document scope for variable visibility": "document_scope|variable_visibility|zls",
+    "how the HTTP transport backend sends requests over the wire": "HTTPTransport|sendRequest|transport_backend",
+    "how nvm downloads a Node.js tarball from the remote": "nvm|download|tarball",
+    "how zls traverses the Zig AST to resolve symbol scopes": "Zig|AST|symbol_scope",
+    "how nvm manages the PATH to point to the active Node.js bin": "nvm|NODE_PATH|node_version_manager",
+    "value-based deserialization for self-describing formats": "self_describing|value_based_deserialization|self_describing_format",
+    "how does FastAPI run sync route functions without blocking": "how\\ does\\ FastAPI\\ run\\ sync\\ route\\ functions\\ without\\ blocking",
+    "Java runtime and OS system property helpers": "System.getProperty|RuntimeMXBean|os.name",
+    "snapshot testing serialization and comparison": "snapshot_testing|snapshot|serialization",
+    "decorator API for defining commands, options, and arguments": "decorator|command_decorator|option_argument_decorator",
+    "Rack::Builder": "Rack::Builder|rackup|config.ru",
+    "how tRPC procedures are defined and chained with input and output validation": "trpc|procedure|input_output_validation",
+    "formatter": "formatter|formatting|FormatException",
+    "res.send body encoding and ETag generation": "res.send|ETag|body_encoding",
+    "ClientResponse": "ClientResponse|client_response|HttpClientResponse",
+    "how bats finds a library in the BATS_LIB_PATH": "BATS_LIB_PATH|BATS_LIB|bats_find_library",
+    "how log records propagate through the handler bubble chain": "log_record|handler_chain|bubble_chain",
+    "queue connection resolution and connectors": "queue|connector|connection_resolution",
+    "HttpCookies": "HttpCookies|CookieJar|Set-Cookie",
+    "SchemaUtils for creating and dropping database tables": "SchemaUtils|createTable|dropTable",
+    "HTML template rendering": "template_rendering|html_template|template_engine",
+    "how telescope actions map keys to picker operations": "telescope_actions|map_keys|picker_operations",
+    "chrono duration and time point formatting": "std::chrono::duration|std::chrono::time_point|strftime",
+    "static file serving middleware": "static_file|file_server|static_middleware",
+    "model_validator": "model_validator|ModelValidator|validate_model",
+    "how pandoc's app module orchestrates reading, filtering, and writing": "pandoc::App|App.hs|Filters",
+    "TypeAdapter": "TypeAdapter|TypeAdapterFactory|TypeAdapterRuntimeTypeWrapper",
+    "how binary data is deserialized back into typed C# objects": "BinaryFormatter|BinaryReader|Deserialize",
+    "how pandoc generates PDF output via LaTeX or other backends": "pandoc|LaTeX|PDF_backend",
+    "PreparedRequest": "PreparedRequest|PreparedRequestAdapter|PreparedRequestFactory",
+    "how the router matches path segments to controller/action": "router|path_segment|controller_action",
+    "mock handler for simulating HTTP responses in tests": "mock_handler|mock_http|fake_http_response",
+    "CompletionOptions": "CompletionOptions",
+    "Axios": "Axios|axios.request|axios.create",
+    "Rails command dispatch and subcommand routing": "Rails\\ command\\ dispatch\\ and\\ subcommand\\ routing",
+    "combineReducers": "combineReducers|combine_reducers|combine-reducers",
+    "how Dapper caches parameter and deserializer IL": "Dapper|parameter_cache|deserializer_IL",
+    "how Applicative combines independent effects": "Applicative|ApplicativeFunctor|zipWithA",
+    "how axum nests routers and merges router trees": "axum::Router|Router::merge|nest_router",
+    "running work after returning an HTTP response without blocking": "background_task|deferred_response|post_response_task",
+    "connection filter chain setup for proxies and TLS": "filter_chain|connection_manager|tls_context",
+    "how nvm_get_latest fetches the latest available Node.js version": "nvm_get_latest|get_latest|nvm-fetch",
+    "ZodType": "ZodType|ZodSchema|zod.object",
+    "how mini.deps manages Neovim plugin installation and updates": "mini.deps|Neovim|plugin_installation",
+    "Schema": "schema|Schema|DBSchema",
+    "how Validated accumulates errors across independent computations": "Validated|accumulate_errors|ValidationNel",
+    "server startup initialization and configuration loading": "server_startup|initialization|configuration_loading",
+    "createStore": "createStore|configureStore|redux_store",
+    "streaming JSON reader and token parsing": "streaming_json|json_reader|token_parsing",
+    "JsonParser": "JsonParser",
+    "bindActionCreators": "bindActionCreators|redux|react-redux",
+    "how mini.diff shows inline git diff signs in buffers": "how\\ mini\\.diff\\ shows\\ inline\\ git\\ diff\\ signs\\ in\\ buffers",
+    "Router": "Router|Routing|route_match",
+    "DigestAuth": "DigestAuth|DigestAuthentication|HTTPDigest",
+    "how the handler stack wraps middleware around the HTTP send": "handler_stack|middleware|http_send",
+    "Serialize": "serialize|Serializer|Serialization",
+    "axios default configuration and request/response transformers": "axios|default_adapter|transformRequest|transformResponse",
+    "Ecto.Changeset for validating and transforming data": "Ecto.Changeset|changeset|validate_change",
+    "applyMiddleware": "applyMiddleware|middleware|redux",
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+    "ExceptionUtils": "ExceptionUtils|ExceptionUtil|exception_utils",
+    "boolean conversion and parsing helpers": "boolean_conversion|parse_bool|bool_parser",
+    "Ecto.Query DSL for composing SQL queries": "Ecto.Query|Ecto.Queryable|from",
+    "how log handlers are registered and invoked by Logger": "Logger|log_handler|register_handler"
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+      "repo": "zls",
+      "query": "how zls traverses the Zig AST to resolve symbol scopes",
+      "category": "architecture",
+      "method": "grep+read",
+      "tokens": 0,
+      "answered": false
+    },
+    {
+      "repo": "zls",
+      "query": "how zls traverses the Zig AST to resolve symbol scopes",
+      "category": "architecture",
+      "method": "keyword-grep+read-top5",
+      "tokens": 15999,
+      "answered": true
+    },
+    {
+      "repo": "zls",
+      "query": "how zls traverses the Zig AST to resolve symbol scopes",
+      "category": "architecture",
+      "method": "keyword-grep+read-top10",
+      "tokens": 15999,
+      "answered": true
+    },
+    {
+      "repo": "zls",
+      "query": "how zls traverses the Zig AST to resolve symbol scopes",
+      "category": "architecture",
+      "method": "keyword-grep+read",
+      "tokens": 15999,
+      "answered": true
+    },
+    {
+      "repo": "zls",
+      "query": "how zls traverses the Zig AST to resolve symbol scopes",
+      "category": "architecture",
+      "method": "keyword-grep+read-top50",
+      "tokens": 15999,
+      "answered": true
+    },
+    {
+      "repo": "zls",
+      "query": "how zls computes document scope for variable visibility",
+      "category": "architecture",
+      "method": "semble",
+      "tokens": 2000,
+      "answered": true
+    },
+    {
+      "repo": "zls",
+      "query": "how zls computes document scope for variable visibility",
+      "category": "architecture",
+      "method": "grep+read",
+      "tokens": 0,
+      "answered": false
+    },
+    {
+      "repo": "zls",
+      "query": "how zls computes document scope for variable visibility",
+      "category": "architecture",
+      "method": "keyword-grep+read-top5",
+      "tokens": 16000,
+      "answered": true
+    },
+    {
+      "repo": "zls",
+      "query": "how zls computes document scope for variable visibility",
+      "category": "architecture",
+      "method": "keyword-grep+read-top10",
+      "tokens": 16000,
+      "answered": true
+    },
+    {
+      "repo": "zls",
+      "query": "how zls computes document scope for variable visibility",
+      "category": "architecture",
+      "method": "keyword-grep+read",
+      "tokens": 16000,
+      "answered": true
+    },
+    {
+      "repo": "zls",
+      "query": "how zls computes document scope for variable visibility",
+      "category": "architecture",
+      "method": "keyword-grep+read-top50",
+      "tokens": 16000,
+      "answered": true
+    },
+    {
+      "repo": "zod",
+      "query": "ZodType",
+      "category": "symbol",
+      "method": "semble",
+      "tokens": 1999,
+      "answered": true
+    },
+    {
+      "repo": "zod",
+      "query": "ZodType",
+      "category": "symbol",
+      "method": "grep+read",
+      "tokens": 16000,
+      "answered": true
+    },
+    {
+      "repo": "zod",
+      "query": "ZodType",
+      "category": "symbol",
+      "method": "keyword-grep+read-top5",
+      "tokens": 15999,
+      "answered": true
+    },
+    {
+      "repo": "zod",
+      "query": "ZodType",
+      "category": "symbol",
+      "method": "keyword-grep+read-top10",
+      "tokens": 16000,
+      "answered": true
+    },
+    {
+      "repo": "zod",
+      "query": "ZodType",
+      "category": "symbol",
+      "method": "keyword-grep+read",
+      "tokens": 15999,
+      "answered": true
+    },
+    {
+      "repo": "zod",
+      "query": "ZodType",
+      "category": "symbol",
+      "method": "keyword-grep+read-top50",
+      "tokens": 16000,
+      "answered": true
+    }
+  ]
+}
diff --git a/benchmarks/results/context-efficiency-recall-6248855cfef8.json b/benchmarks/results/context-efficiency-recall-6248855cfef8.json
new file mode 100644
index 0000000..aee9276
--- /dev/null
+++ b/benchmarks/results/context-efficiency-recall-6248855cfef8.json
@@ -0,0 +1,313 @@
+{
+  "tool": "context-efficiency-recall",
+  "tokenizer": "cl100k_base",
+  "budgets": [
+    500,
+    1000,
+    2000,
+    4000,
+    8000,
+    16000,
+    32000
+  ],
+  "n_queries": 1251,
+  "recall_at_budget": {
+    "semble": {
+      "500": 0.6843,
+      "1000": 0.848,
+      "2000": 0.9365,
+      "4000": 0.9762,
+      "8000": 0.9912,
+      "16000": 0.996,
+      "32000": 0.9964
+    },
+    "grep+read": {
+      "500": 0.0,
+      "1000": 0.0072,
+      "2000": 0.0228,
+      "4000": 0.042,
+      "8000": 0.0763,
+      "16000": 0.1011,
+      "32000": 0.1267
+    },
+    "ripgrep-c8": {
+      "500": 0.0691,
+      "1000": 0.0911,
+      "2000": 0.1071,
+      "4000": 0.1171,
+      "8000": 0.1307,
+      "16000": 0.1431,
+      "32000": 0.1503
+    }
+  },
+  "first_hit_reduction": {
+    "grep+read": {
+      "n_paired": 191.0,
+      "median_semble_tokens": 275.0,
+      "median_other_tokens": 7864.0,
+      "median_reduction": 0.9708,
+      "mean_reduction": 0.9323,
+      "semble_better_pct": 1.0
+    },
+    "ripgrep-c8": {
+      "n_paired": 190.0,
+      "median_semble_tokens": 274.5,
+      "median_other_tokens": 613.0,
+      "median_reduction": 0.6399,
+      "mean_reduction": 0.2028,
+      "semble_better_pct": 0.7263
+    }
+  },
+  "plot": {
+    "budgets": [
+      100,
+      111,
+      124,
+      138,
+      154,
+      172,
+      192,
+      215,
+      240,
+      267,
+      298,
+      333,
+      372,
+      415,
+      463,
+      516,
+      576,
+      643,
+      717,
+      801,
+      893,
+      997,
+      1112,
+      1241,
+      1385,
+      1545,
+      1724,
+      1923,
+      2146,
+      2395,
+      2672,
+      2981,
+      3326,
+      3711,
+      4141,
+      4620,
+      5155,
+      5751,
+      6417,
+      7160,
+      7989,
+      8913,
+      9945,
+      11096,
+      12380,
+      13813,
+      15412,
+      17196,
+      19186,
+      21407,
+      23884,
+      26649,
+      29733,
+      33174,
+      37014,
+      41298,
+      46077,
+      51410,
+      57361,
+      64000
+    ],
+    "recall": {
+      "semble": [
+        0.0404,
+        0.0484,
+        0.0584,
+        0.0699,
+        0.0823,
+        0.1007,
+        0.1214,
+        0.1533,
+        0.1941,
+        0.2397,
+        0.3183,
+        0.4213,
+        0.5524,
+        0.6367,
+        0.6719,
+        0.6922,
+        0.7134,
+        0.7438,
+        0.772,
+        0.792,
+        0.818,
+        0.8472,
+        0.8624,
+        0.8776,
+        0.8894,
+        0.9101,
+        0.9209,
+        0.9341,
+        0.9432,
+        0.9515,
+        0.9547,
+        0.9619,
+        0.9667,
+        0.9723,
+        0.9778,
+        0.9817,
+        0.984,
+        0.986,
+        0.9888,
+        0.9908,
+        0.9912,
+        0.9924,
+        0.9952,
+        0.9952,
+        0.996,
+        0.996,
+        0.996,
+        0.996,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964
+      ],
+      "grep+read": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0008,
+        0.0032,
+        0.0056,
+        0.0064,
+        0.0064,
+        0.0072,
+        0.008,
+        0.0124,
+        0.014,
+        0.014,
+        0.0188,
+        0.022,
+        0.0268,
+        0.028,
+        0.0312,
+        0.0336,
+        0.0348,
+        0.0396,
+        0.0428,
+        0.0476,
+        0.05,
+        0.0548,
+        0.0584,
+        0.0675,
+        0.0763,
+        0.0787,
+        0.0795,
+        0.0875,
+        0.0903,
+        0.0971,
+        0.0987,
+        0.1047,
+        0.1111,
+        0.1155,
+        0.1195,
+        0.1219,
+        0.1267,
+        0.1275,
+        0.1283,
+        0.1315,
+        0.1371,
+        0.1379,
+        0.1411,
+        0.1435
+      ],
+      "ripgrep-c8": [
+        0.0096,
+        0.0152,
+        0.0204,
+        0.024,
+        0.0288,
+        0.0328,
+        0.038,
+        0.0404,
+        0.0424,
+        0.0472,
+        0.0504,
+        0.0536,
+        0.0592,
+        0.0624,
+        0.0655,
+        0.0691,
+        0.0731,
+        0.0779,
+        0.0827,
+        0.0851,
+        0.0863,
+        0.0911,
+        0.0943,
+        0.0991,
+        0.1047,
+        0.1063,
+        0.1071,
+        0.1071,
+        0.1087,
+        0.1103,
+        0.1115,
+        0.1131,
+        0.1131,
+        0.1147,
+        0.1171,
+        0.1179,
+        0.1211,
+        0.1259,
+        0.1283,
+        0.1307,
+        0.1307,
+        0.1319,
+        0.1383,
+        0.1387,
+        0.1395,
+        0.1407,
+        0.1415,
+        0.1439,
+        0.1439,
+        0.1439,
+        0.1471,
+        0.1487,
+        0.1503,
+        0.1503,
+        0.1503,
+        0.1503,
+        0.1503,
+        0.1503,
+        0.1503,
+        0.1503
+      ]
+    }
+  }
+}
diff --git a/benchmarks/run_benchmark.py b/benchmarks/run_benchmark.py
index 34148fc..dbb690d 100644
--- a/benchmarks/run_benchmark.py
+++ b/benchmarks/run_benchmark.py
@@ -1,5 +1,4 @@
 import argparse
-import json
 import sys
 import time
 from collections import defaultdict
@@ -11,10 +10,9 @@
 from benchmarks.data import (
     RepoSpec,
     Task,
-    apply_task_filters,
-    available_repo_specs,
+    add_filter_args,
     grouped_tasks,
-    load_tasks,
+    load_filtered_tasks,
     save_results,
 )
 from benchmarks.metrics import ndcg_at_k, target_rank
@@ -274,16 +272,9 @@ def _save_results(results: list[RepoResult]) -> None:
 def main() -> None:
     """Parse arguments and run the semble hybrid benchmark."""
     parser = argparse.ArgumentParser(description="Benchmark hybrid semble search across the pinned benchmark repos.")
-    parser.add_argument("--repo", action="append", default=[], help="Limit to one or more repo names.")
-    parser.add_argument("--language", action="append", default=[], help="Limit to one or more languages.")
-    parser.add_argument("--verbose", action="store_true", help="Print per-query results.")
+    add_filter_args(parser, verbose=True)
     args = parser.parse_args()
-    repo_specs = available_repo_specs()
-    tasks = apply_task_filters(
-        load_tasks(repo_specs=repo_specs), repos=args.repo or None, languages=args.language or None
-    )
-    if not tasks:
-        raise SystemExit("No benchmark tasks matched the requested filters.")
+    repo_specs, tasks = load_filtered_tasks(args.repo or None, args.language or None)
     print("Loading model...", file=sys.stderr)
     started = time.perf_counter()
     model = StaticModel.from_pretrained(_DEFAULT_MODEL_NAME)
diff --git a/benchmarks/speed_benchmark.py b/benchmarks/speed_benchmark.py
index 7bdc696..717598f 100644
--- a/benchmarks/speed_benchmark.py
+++ b/benchmarks/speed_benchmark.py
@@ -1,16 +1,15 @@
-import json
 import subprocess
 import sys
 import time
 from collections.abc import Sequence
 from dataclasses import dataclass
-from pathlib import Path
 
 import numpy as np
 from model2vec import StaticModel
 from sentence_transformers import SentenceTransformer
 
 from benchmarks.data import RepoSpec, Task, available_repo_specs, load_tasks, save_results
+from benchmarks.tools import run_colgrep_files, run_ripgrep_count
 from semble import SembleIndex
 from semble.index.dense import _DEFAULT_MODEL_NAME
 from semble.types import EmbeddingMatrix, Encoder
@@ -40,7 +39,6 @@
 
 _TOP_K = 10
 _COLGREP = "colgrep"
-_RG = "rg"
 
 
 @dataclass(frozen=True)
@@ -90,58 +88,6 @@ def encode(self, texts: Sequence[str], /) -> EmbeddingMatrix:
         return self._model.encode(text_list, batch_size=1)  # type: ignore[return-value]
 
 
-def _run_ripgrep(query: str, benchmark_dir: Path) -> list[str]:
-    """Run ripgrep and return top-k file paths sorted by match count."""
-    cmd = [
-        _RG,
-        "--count",
-        "--no-heading",
-        "--ignore-case",
-        "--hidden",
-        "--glob",
-        "!.git",
-        "--fixed-strings",
-        query,
-        str(benchmark_dir),
-    ]
-    try:
-        proc = subprocess.run(cmd, capture_output=True, text=True, timeout=30)
-    except subprocess.TimeoutExpired:
-        return []
-    if proc.returncode not in (0, 1):
-        return []
-    entries: list[tuple[str, int]] = []
-    for line in proc.stdout.splitlines():
-        line = line.strip()
-        if not line:
-            continue
-        *path_parts, count_str = line.split(":")
-        try:
-            entries.append((":".join(path_parts), int(count_str)))
-        except ValueError:
-            continue
-    entries.sort(key=lambda x: -x[1])
-    return [path for path, _ in entries[:_TOP_K]]
-
-
-def _run_colgrep(query: str, benchmark_dir: Path, *, code_only: bool = True) -> list[str]:
-    """Run ColGREP and return top-k file paths from the JSON output."""
-    cmd = [_COLGREP, "--force-cpu", "--json", "-k", str(_TOP_K), query, str(benchmark_dir)]
-    if code_only:
-        cmd.append("--code-only")
-    try:
-        proc = subprocess.run(cmd, capture_output=True, text=True, timeout=60)
-    except subprocess.TimeoutExpired:
-        return []
-    if proc.returncode != 0:
-        return []
-    try:
-        data = json.loads(proc.stdout)
-    except json.JSONDecodeError:
-        return []
-    return [item["unit"]["file"] for item in data if "unit" in item and "file" in item["unit"]]
-
-
 def _bench_semble(
     spec: RepoSpec, tasks: list[Task], model: Encoder | None
 ) -> tuple[float, SembleIndex, tuple[float, ...]]:
@@ -194,13 +140,13 @@ def _bench_colgrep(spec: RepoSpec, tasks: list[Task]) -> tuple[float, tuple[floa
     if proc.returncode != 0:
         print(f"  WARNING: colgrep init failed: {proc.stderr.strip()}", file=sys.stderr)
     if "(0 files)" in proc.stdout or "(0 files)" in proc.stderr:
-        print(f"  SKIP: colgrep indexed 0 files (unsupported language?)", file=sys.stderr)
+        print("  SKIP: colgrep indexed 0 files (unsupported language?)", file=sys.stderr)
         return None
     latencies: list[float] = []
     code_only = spec.language != "bash"
     for task in tasks:
         started = time.perf_counter()
-        _run_colgrep(task.query, spec.benchmark_dir, code_only=code_only)
+        run_colgrep_files(task.query, spec.benchmark_dir, top_k=_TOP_K, code_only=code_only, timeout=60)
         latencies.append((time.perf_counter() - started) * 1000)
     return index_ms, tuple(latencies)
 
@@ -211,7 +157,7 @@ def _bench_ripgrep(spec: RepoSpec, tasks: list[Task]) -> tuple[float, tuple[floa
     for task in tasks:
         for _ in range(3):
             started = time.perf_counter()
-            _run_ripgrep(task.query, spec.benchmark_dir)
+            run_ripgrep_count(task.query, spec.benchmark_dir, top_k=_TOP_K)
             latencies.append((time.perf_counter() - started) * 1000)
     return 0.0, tuple(latencies)
 
diff --git a/benchmarks/tools.py b/benchmarks/tools.py
new file mode 100644
index 0000000..73fc86e
--- /dev/null
+++ b/benchmarks/tools.py
@@ -0,0 +1,64 @@
+import json
+import subprocess
+from pathlib import Path
+
+
+def run_ripgrep_count(
+    query: str,
+    benchmark_dir: Path,
+    *,
+    top_k: int,
+    fixed_strings: bool = True,
+    timeout: int = 30,
+) -> list[str]:
+    """Return file paths sorted by ripgrep match count."""
+    cmd = ["rg", "--count", "--no-heading", "--ignore-case", "--hidden", "--glob", "!.git"]
+    if fixed_strings:
+        cmd.append("--fixed-strings")
+    cmd += [query, str(benchmark_dir)]
+
+    try:
+        proc = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
+    except subprocess.TimeoutExpired:
+        return []
+    if proc.returncode not in (0, 1):
+        return []
+
+    entries: list[tuple[str, int]] = []
+    for line in proc.stdout.splitlines():
+        line = line.strip()
+        if not line:
+            continue
+        *path_parts, count_str = line.split(":")
+        try:
+            entries.append((":".join(path_parts), int(count_str)))
+        except ValueError:
+            continue
+    entries.sort(key=lambda x: -x[1])
+    return [path for path, _ in entries[:top_k]]
+
+
+def run_colgrep_files(
+    query: str,
+    benchmark_dir: Path,
+    *,
+    top_k: int,
+    code_only: bool = True,
+    timeout: int = 30,
+) -> list[str]:
+    """Return file paths from ColGREP JSON output."""
+    cmd = ["colgrep", "--force-cpu"]
+    if code_only:
+        cmd.append("--code-only")
+    cmd += ["--json", "-k", str(top_k), query, str(benchmark_dir)]
+    try:
+        proc = subprocess.run(cmd, capture_output=True, text=True, timeout=timeout)
+    except subprocess.TimeoutExpired:
+        return []
+    if proc.returncode != 0:
+        return []
+    try:
+        data = json.loads(proc.stdout)
+    except json.JSONDecodeError:
+        return []
+    return [item["unit"]["file"] for item in data if "unit" in item and "file" in item["unit"]]
diff --git a/pyproject.toml b/pyproject.toml
index 00d248c..ec4a2d8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -43,6 +43,8 @@ benchmark = [
     "numpy>=1.24.0",
     "einops>=0.8.2",
     "matplotlib>=3.7",
+    "tiktoken>=0.7",
+    "openai>=1.50",
 ]
 dev = [
     "pytest>=8.0",
diff --git a/uv.lock b/uv.lock
index 035e1fd..f983e19 100644
--- a/uv.lock
+++ b/uv.lock
@@ -163,6 +163,111 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/db/3c/33bac158f8ab7f89b2e59426d5fe2e4f63f7ed25df84c036890172b412b5/cfgv-3.5.0-py2.py3-none-any.whl", hash = "sha256:a8dc6b26ad22ff227d2634a65cb388215ce6cc96bbcc5cfde7641ae87e8dacc0", size = 7445, upload-time = "2025-11-19T20:55:50.744Z" },
 ]
 
+[[package]]
+name = "charset-normalizer"
+version = "3.4.7"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/a1/67fe25fac3c7642725500a3f6cfe5821ad557c3abb11c9d20d12c7008d3e/charset_normalizer-3.4.7.tar.gz", hash = "sha256:ae89db9e5f98a11a4bf50407d4363e7b09b31e55bc117b4f7d80aab97ba009e5", size = 144271, upload-time = "2026-04-02T09:28:39.342Z" }
+wheels = [
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From a507b18b399b1b354370bc179f10b39954817cd2 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Tue, 28 Apr 2026 18:03:38 +0200
Subject: [PATCH 02/18] Simplify

---
 assets/images/recall_vs_tokens.png            |  Bin 62621 -> 61963 bytes
 benchmarks/README.md                          |   55 +-
 benchmarks/context_efficiency.py              |  187 +-
 ...context-efficiency-judge-6248855cfef8.json | 9764 +----------------
 ...fficiency-judge-records-6248855cfef8.jsonl |  396 +
 5 files changed, 459 insertions(+), 9943 deletions(-)
 create mode 100644 benchmarks/results/context-efficiency-judge-records-6248855cfef8.jsonl

diff --git a/assets/images/recall_vs_tokens.png b/assets/images/recall_vs_tokens.png
index 83e153c88d3d7501e3d608a6bffd90ed326045cc..6a623cb6ea286fc9bd3bbb977d9006980b9a0c69 100644
GIT binary patch
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diff --git a/benchmarks/README.md b/benchmarks/README.md
index d36d068..e50c8c6 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -60,42 +60,38 @@ NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best sc
 
 ## Context efficiency
 
-Coding agents like Claude Code and OpenCode rely on `grep` to find candidate files and then `Read` those files into context. We model this workflow (`grep + read file`) and measure how many tokens each retrieval strategy spends to actually answer a query — judged end-to-end by an external LLM.
+Coding agents often use `grep` to find candidate files and then read those files into context. We model that direct `grep + read file` workflow and compare it with semble's chunk retrieval.
 
-> Across a 198-query category-balanced sample judged by GPT-5-mini, semble's retrieved context is sufficient to answer **88%** of queries at 2k tokens. A grep-based agent that first asks an LLM to extract grep-friendly keywords from the query (the strongest realistic baseline — closer to what Claude Code and OpenCode actually do) reaches **71%** but spends ~11k tokens reading candidate files. The raw retrieval-token saving is **81%** vs keyword grep and **50%** vs naive grep; including a pessimistic fallback model that adds the cost of redoing grep on a miss, end-to-end savings are **77%** and **69%** respectively.
+> Across a 198-query category-balanced sample judged by GPT-5-mini, semble's retrieved context answers **88%** of queries at 2k tokens. Direct `grep + read file` answers **29%** and spends ~4k tokens. That is a **50% reduction in retrieved code-context tokens**; with a pessimistic fallback model for misses, expected end-to-end context cost drops by **69%**.
 
 ![Recall vs. retrieved tokens](../assets/images/recall_vs_tokens.png)
 
 ### Answer sufficiency (LLM-as-judge)
 
-For each query we hand the retrieved context to GPT-5-mini and ask whether it contains enough relevant code to answer the query. Sample is 198 queries, balanced 66/66/66 across categories (note: the underlying benchmark is imbalanced 703/344/204 semantic/architecture/symbol; the macro-balanced sample is more conservative for semble's claim, since proportional sampling would weight the harder semantic category higher). Three retrieval workflows:
+For each query we hand the retrieved context to GPT-5-mini and ask whether it contains enough relevant code to answer the query. The sample is 198 queries, balanced 66/66/66 across semantic, architecture, and symbol categories.
 
 - **semble** — top-K chunks, hard-capped at 2k tokens.
-- **keyword-grep+read** — gpt-5-mini extracts 1–3 grep-friendly terms from the query, joined as a regex (`term1|term2`); the agent reads the top-20 matched files in full, hard-capped at 16k tokens. Mirrors how Claude Code and OpenCode actually use grep.
-- **grep+read (naive)** — the raw query string is passed verbatim to `rg --fixed-strings`; matching files are read in full, hard-capped at 16k tokens. Strawman included for comparison.
+- **grep+read** — the raw query string is passed to `rg --fixed-strings`; matching files are read in full, hard-capped at 16k tokens.
 
-All three workflows search the same file universe — semble's indexed code-file extensions, with `node_modules`, `dist`, `build`, `.venv`, etc. excluded.
+Both workflows search the same code-file universe: semble's indexed extensions, excluding `node_modules`, `dist`, `build`, `.venv`, and other ignored directories.
 
-| Method | Budget | Answer rate | Mean tokens retrieved | Raw token reduction vs semble | Expected end-to-end tokens† |
+| Method | Budget | Answer rate | Mean tokens retrieved | Token reduction with semble | Expected end-to-end tokens† |
 |---|---:|---:|---:|---:|---:|
 | **semble** | 2k | **0.879** | 2,000 | — | **2,906** |
-| keyword-grep+read | 16k | 0.712 | 10,595 | 81.1% | 12,747 |
-| grep+read (naive) | 16k | 0.293 | 3,996 | 50.0% | 9,281 |
+| grep+read | 16k | 0.293 | 3,996 | 50.0% | 9,281 |
 
-**Raw retrieval reduction:** 81.1% vs keyword-grep, 50.0% vs naive grep.
-**End-to-end reduction (with fallback model):** 77.2% vs keyword-grep, 68.7% vs naive grep.
+**Raw retrieval reduction:** 50.0% fewer retrieved code-context tokens vs `grep+read`.
+**End-to-end reduction (with fallback model):** 68.7% fewer expected context tokens vs `grep+read`.
 
-†The end-to-end column adds a uniform fallback term `(1 − answer_rate) × 7,474` to every method, modeling the agent redoing a grep workflow on a miss. The constant 7,474 is the median tokens grep+read spends to surface a relevant file. Sweeping it 3k–16k gives an end-to-end reduction range of **61–74% vs naive grep** and **74–79% vs keyword grep** (recomputed from the saved artifact), so the conclusion is not load-bearing on this constant. The raw retrieval reduction is the model-free comparison.
+†The end-to-end column adds `(1 − answer_rate) × 7,474`, modeling a fallback search on a miss. The constant is the median tokens `grep+read` spends to surface a relevant file. The raw retrieval reduction is the model-free comparison. This benchmark measures retrieved code-context tokens, not total IDE or agent billing tokens.
 
 **Answer rate by query category:**
 
-| Category | semble | keyword-grep+read | naive grep |
-|---|---:|---:|---:|
-| symbol (named entity lookup) | 0.97 | 0.94 | 0.86 |
-| semantic (behavior / concept) | 0.85 | 0.52 | 0.02 |
-| architecture (design / structure) | 0.82 | 0.68 | 0.00 |
-
-Keyword extraction closes the gap on symbol queries, helps substantially on semantic and architecture — but at ~5× the retrieval token cost (full file reads, top-20 candidates), so the end-to-end gap remains large.
+| Category | semble | grep+read |
+|---|---:|---:|
+| symbol (named entity lookup) | 0.97 | 0.86 |
+| semantic (behavior / concept) | 0.85 | 0.02 |
+| architecture (design / structure) | 0.82 | 0.00 |
 
 ### Recall at fixed token budgets
 
@@ -110,19 +106,11 @@ A coarser, label-based view across the full 1,251-query benchmark: a relevant fi
 <details>
 <summary>Methodology</summary>
 
-**Retrieval units.** For each query we retrieve units in rank order — semble chunks for semble, full files (in match-count order) for `grep + read file`, merged context windows for `ripgrep -C N` — and accumulate tokens (`cl100k_base` via `tiktoken`). Naive `grep` is run with `--fixed-strings --ignore-case` on the raw query. The keyword variant first asks gpt-5-mini for 1–3 search terms (joined as a regex `|`); patterns that fail an identifier-token sanity check fall back to `re.escape(query)`. Both grep variants are scoped via `--glob` to the same code-file extensions and ignored directories that semble's file walker uses, so all three workflows search the same corpus.
-
-**Budget enforcement (judge mode only).** In the LLM-as-judge eval each context is hard-capped at its stated budget: the candidate string is encoded once, then sliced by token IDs and asserted `len(ids) ≤ budget`. The recall mode below does not truncate; its curves record cumulative tokens of whole retrieved units, which is the right shape for "tokens to surface the answer."
-
-**Sample policy.** The judge sample is **macro-balanced** — 66 queries per category — not proportional to the underlying 703/344/204 split. This is conservative for semble's claim: a proportional sample would overweight semantic queries (where grep does worst) and inflate the headline. The recall benchmark below uses all 1,251 queries with no rebalancing.
-
-**Sensitivity of the top-20 file cap.** Sweeping top-{5, 10, 20, 50} on the same sample (saved alongside the default cap in the same artifact), keyword-grep+read answer rates fall in 71.2–72.7% and the end-to-end reduction range is 76.3–77.2%. The cap is bounded by the 16k token budget, not the file count, so the choice is not load-bearing.
-
-**Sufficiency judging.** GPT-5-mini sees the query and the retrieved context (capped at the per-method budget) and answers a strict yes/no on whether the context contains code that directly addresses the query.
+For each query we retrieve units in rank order — semble chunks for semble, full files in match-count order for `grep+read`, and merged context windows for `ripgrep -C 8` in the recall curve. Tokens are counted with `cl100k_base` via `tiktoken`. `grep` is run with `--fixed-strings --ignore-case` on the raw query and scoped via `--glob` to the same code-file extensions and ignored directories that semble indexes.
 
-**End-to-end cost model.** `expected_cost = retrieval_tokens + (1 − answer_rate) × fallback_tokens`. The fallback is set to 7,474 tokens — the median tokens grep+read spends to surface a relevant file, derived from the recall benchmark. This is *pessimistic* for semble: it assumes the agent rebuilds context from scratch on every miss, with no credit for chunks semble already returned.
+In judge mode each context is hard-capped at its stated budget by token IDs and asserted `len(ids) ≤ budget`. Recall mode does not truncate; its curves record cumulative tokens of whole retrieved units. The judge sample is macro-balanced across categories; the recall benchmark uses all 1,251 queries.
 
-**Recall.** A relevant file is "covered" once any retrieved unit comes from it. Most labels are file-level; where line spans are present we require span overlap.
+GPT-5-mini sees the query and capped retrieved context and answers a strict yes/no on whether the context contains code that directly addresses the query. A relevant file is "covered" in recall once any retrieved unit comes from it; where line spans are present we require span overlap.
 
 </details>
 
@@ -262,22 +250,21 @@ uv run python -m benchmarks.baselines.coderankembed --mode semantic
 <details>
 <summary>Context-efficiency benchmark</summary>
 
-Requires the `benchmark` extra (`uv sync --extra benchmark`), `rg` on `$PATH`, and an `OPENAI_API_KEY` environment variable (used for keyword extraction and the LLM-as-judge baseline; `--no-keyword` skips it for the recall mode).
+Requires the `benchmark` extra (`uv sync --extra benchmark`) and `rg` on `$PATH`. Judge mode also needs `OPENAI_API_KEY`.
 
 ```bash
 # Recall vs. token-budget across all queries; plots automatically.
 uv run python -m benchmarks.context_efficiency recall
-uv run python -m benchmarks.context_efficiency recall --repo fastapi --no-keyword
+uv run python -m benchmarks.context_efficiency recall --repo fastapi
 
 # LLM-as-judge sufficiency on a stratified sample.
 uv run python -m benchmarks.context_efficiency judge --sample 200
-uv run python -m benchmarks.context_efficiency judge --sample 200 --keyword-caps 5 10 20 50
 
 # Regenerate the plot from a saved recall payload.
 uv run python -m benchmarks.context_efficiency plot
 ```
 
-Writes `benchmarks/results/context-efficiency-{recall,judge}-<sha12>.json` and `assets/images/recall_vs_tokens.png`.
+Writes `benchmarks/results/context-efficiency-{recall,judge}-<sha12>.json`, compact judge records as JSONL, and `assets/images/recall_vs_tokens.png`.
 
 </details>
 
diff --git a/benchmarks/context_efficiency.py b/benchmarks/context_efficiency.py
index d00719f..45290db 100644
--- a/benchmarks/context_efficiency.py
+++ b/benchmarks/context_efficiency.py
@@ -2,7 +2,6 @@
 import asyncio
 import json
 import random
-import re
 import subprocess
 import sys
 import time
@@ -24,6 +23,7 @@
     add_filter_args,
     grouped_tasks,
     load_filtered_tasks,
+    results_path,
     save_results,
     target_matches_location,
 )
@@ -43,9 +43,6 @@
 _RG_CONTEXTS = (8,)
 _RG_MAX_MATCHES = 500
 _SEMBLE_TOP_K = 50
-_KEYWORD_MAX_FILES = 20
-_KEYWORD_MODEL = "gpt-5-mini"
-_KEYWORD_CONCURRENCY = 16
 _JUDGE_MODEL = "gpt-5-mini"
 _JUDGE_CONCURRENCY = 8
 _JUDGE_RETRIES = 3
@@ -56,23 +53,6 @@
 _IMAGES_DIR = Path(__file__).parent.parent / "assets" / "images"
 _RESULTS_DIR = Path(__file__).parent / "results"
 
-_KEYWORD_PROMPT = """\
-Extract 1–3 grep-friendly search terms from the code search query below.
-Terms will be joined with | and used as a ripgrep regex pattern (not fixed strings).
-Prefer specific identifiers, class names, or technical keywords over generic words.
-Return only the pipe-separated terms — no explanation, no quotes, no punctuation.
-
-Examples:
-  flat_hash_map  →  flat_hash_map
-  absl::flat_hash_map and flat_hash_set containers  →  flat_hash_map|flat_hash_set
-  how does authentication middleware work  →  middleware|authentication
-  request lifecycle and error handling  →  lifecycle|error_handling
-  how are worker threads managed  →  worker_thread|thread_pool|ThreadPool
-
-Query: {query}
-Terms:\
-"""
-
 _JUDGE_PROMPT = """\
 You are evaluating whether retrieved code context is sufficient to answer a code search query.
 
@@ -113,23 +93,20 @@ def _semble_units(index: SembleIndex, query: str) -> list[Chunk]:
     return [r.chunk for r in index.search(query, top_k=_SEMBLE_TOP_K)]
 
 
-def _rg_command(pattern: str, repo_dir: Path, *, fixed_strings: bool) -> list[str]:
+def _rg_command(pattern: str, repo_dir: Path) -> list[str]:
     """Build the rg command line, scoped to the same code-file universe semble indexes."""
-    cmd = ["rg", "--json"]
-    if fixed_strings:
-        cmd.append("--fixed-strings")
-    cmd.append("--ignore-case")
+    cmd = ["rg", "--json", "--fixed-strings", "--ignore-case"]
     for glob in (*_RG_EXCLUDE_GLOBS, *_RG_INCLUDE_GLOBS):
         cmd += ["--glob", glob]
     cmd += [pattern, str(repo_dir)]
     return cmd
 
 
-def _rg_matches(pattern: str, repo_dir: Path, *, fixed_strings: bool) -> list[tuple[str, int]]:
+def _rg_matches(pattern: str, repo_dir: Path) -> list[tuple[str, int]]:
     """Return (file_path, line_number) matches via rg --json, in rg's output order."""
     try:
         proc = subprocess.run(
-            _rg_command(pattern, repo_dir, fixed_strings=fixed_strings),
+            _rg_command(pattern, repo_dir),
             capture_output=True,
             text=True,
             timeout=30,
@@ -158,7 +135,7 @@ def _rg_matches(pattern: str, repo_dir: Path, *, fixed_strings: bool) -> list[tu
 
 def _ripgrep_units(query: str, repo_dir: Path, context: int) -> list[Chunk]:
     """Ripgrep windows: files ranked by match count desc, windows merged within file."""
-    matches = _rg_matches(query, repo_dir, fixed_strings=True)
+    matches = _rg_matches(query, repo_dir)
     if not matches:
         return []
     per_file: dict[str, list[int]] = defaultdict(list)
@@ -188,17 +165,12 @@ def _ripgrep_units(query: str, repo_dir: Path, context: int) -> list[Chunk]:
 def _grep_file_units(
     pattern: str,
     repo_dir: Path,
-    *,
-    fixed_strings: bool,
-    max_files: int | None = None,
 ) -> list[Chunk]:
     """Return whole matched files in match-count order."""
-    matches = _rg_matches(pattern, repo_dir, fixed_strings=fixed_strings)
+    matches = _rg_matches(pattern, repo_dir)
     if not matches:
         return []
     ranked = sorted(Counter(path for path, _ in matches[:_RG_MAX_MATCHES]).items(), key=lambda kv: (-kv[1], kv[0]))
-    if max_files is not None:
-        ranked = ranked[:max_files]
     units: list[Chunk] = []
     for path, _ in ranked:
         try:
@@ -209,80 +181,23 @@ def _grep_file_units(
     return units
 
 
-def _keyword_method(cap: int) -> str:
-    """Return the method label for a keyword-grep file cap."""
-    return "keyword-grep+read" if cap == _KEYWORD_MAX_FILES else f"keyword-grep+read-top{cap}"
-
-
 def _retrieval_units_for_task(
     index: SembleIndex,
     task: Task,
     repo_dir: Path,
     *,
-    keywords: dict[str, str] | None,
-    keyword_caps: tuple[int, ...] = (_KEYWORD_MAX_FILES,),
     include_ripgrep: bool = False,
 ) -> list[tuple[str, list[Chunk]]]:
     """Return retrieval-method units for a task in benchmark order."""
     methods = [
         ("semble", _semble_units(index, task.query)),
-        ("grep+read", _grep_file_units(task.query, repo_dir, fixed_strings=True)),
+        ("grep+read", _grep_file_units(task.query, repo_dir)),
     ]
-    if keywords is not None:
-        pattern = keywords.get(task.query, task.query)
-        methods.extend(
-            (_keyword_method(cap), _grep_file_units(pattern, repo_dir, fixed_strings=False, max_files=cap))
-            for cap in keyword_caps
-        )
     if include_ripgrep:
         methods.extend((f"ripgrep-c{c}", _ripgrep_units(task.query, repo_dir, c)) for c in _RG_CONTEXTS)
     return methods
 
 
-_KEYWORD_TOKEN_RE = re.compile(r"^[A-Za-z_][A-Za-z0-9_:.\-]{0,63}$")
-
-
-def _sanitize_keywords(pattern: str) -> str | None:
-    """Accept a pipe-joined regex pattern only if every segment is an identifier-like token."""
-    parts = [p.strip() for p in pattern.split("|") if p.strip()]
-    if not parts or len(parts) > 5:
-        return None
-    if not all(_KEYWORD_TOKEN_RE.match(p) for p in parts):
-        return None
-    return "|".join(parts)
-
-
-async def _extract_keyword_pattern(query: str, client: AsyncOpenAI) -> str:
-    """Ask the LLM for 1–3 pipe-separated grep terms; sanitize, fall back to re.escape(query) on failure."""
-    fallback = re.escape(query)
-    try:
-        resp = await client.responses.create(
-            model=_KEYWORD_MODEL,
-            input=_KEYWORD_PROMPT.format(query=query),
-            reasoning={"effort": "minimal"},
-        )
-        raw = (resp.output_text or "").strip().strip('"').strip("'")
-        sanitized = _sanitize_keywords(raw)
-        return sanitized if sanitized is not None else fallback
-    except Exception:  # noqa: BLE001
-        return fallback
-
-
-async def _prefetch_keywords(queries: list[str]) -> dict[str, str]:
-    """Return {query: regex_pattern} for all unique queries via LLM keyword extraction."""
-    unique = list(dict.fromkeys(queries))
-    client = AsyncOpenAI()
-    sem = asyncio.Semaphore(_KEYWORD_CONCURRENCY)
-    results: dict[str, str] = {}
-
-    async def worker(q: str) -> None:
-        async with sem:
-            results[q] = await _extract_keyword_pattern(q, client)
-
-    await asyncio.gather(*(worker(q) for q in unique))
-    return results
-
-
 def _curve(units: list[Chunk], targets: tuple[Target, ...], enc: Any) -> Curve:
     """Cumulative (tokens, covered_target_count) after each retrieved unit, starting at (0, 0)."""
     covered = [False] * len(targets)
@@ -454,7 +369,6 @@ def _evaluate_repo_recall(
     tasks: list[Task],
     repo_dir: Path,
     enc: Any,
-    keywords: dict[str, str] | None,
 ) -> dict[str, MethodCurves]:
     """Build per-method curves for every task in the repo."""
     methods: dict[str, MethodCurves] = defaultdict(list)
@@ -466,7 +380,6 @@ def _evaluate_repo_recall(
             index,
             task,
             repo_dir,
-            keywords=keywords,
             include_ripgrep=True,
         ):
             methods[method].append((_curve(units, targets, enc), n))
@@ -478,8 +391,6 @@ def _build_pending_judge(
     repo_specs: dict[str, Any],
     model: StaticModel,
     enc: Any,
-    keywords: dict[str, str],
-    keyword_caps: tuple[int, ...],
 ) -> list[tuple[Task, str, str, int]]:
     """Build (task, method, context, tokens) inputs for the LLM judge across all sampled tasks."""
     pending: list[tuple[Task, str, str, int]] = []
@@ -494,8 +405,6 @@ def _build_pending_judge(
                 index,
                 task,
                 spec.benchmark_dir,
-                keywords=keywords,
-                keyword_caps=keyword_caps,
             ):
                 budget = _JUDGE_SEMBLE_BUDGET if method == "semble" else _JUDGE_GREP_BUDGET
                 context, tokens = _format_context(units, budget, enc)
@@ -527,16 +436,30 @@ def _aggregate_judge(records: list[JudgeRecord], attempted_per_method: dict[str,
     return out
 
 
+def _save_judge_records(records: list[JudgeRecord]) -> Path:
+    """Write per-query judge records as compact JSONL for version comparisons."""
+    out = results_path("context-efficiency-judge-records").with_suffix(".jsonl")
+    lines = [
+        json.dumps(
+            {
+                "repo": r.repo,
+                "query": r.query,
+                "category": r.category,
+                "method": r.method,
+                "tokens": r.tokens_used,
+                "answered": r.answered,
+            },
+            separators=(",", ":"),
+        )
+        for r in records
+    ]
+    out.write_text("\n".join(lines) + "\n", encoding="utf-8")
+    return out
+
+
 _PLOT_STYLE: dict[str, dict[str, object]] = {
     "semble": {"label": "semble", "color": "#1a5fa8", "linewidth": 2.4, "zorder": 4},
-    "keyword-grep+read": {
-        "label": "keyword grep + read file",
-        "color": "#d35400",
-        "linewidth": 1.8,
-        "zorder": 3,
-        "linestyle": "--",
-    },
-    "grep+read": {"label": "naive grep + read file", "color": "#922b21", "linewidth": 1.8, "zorder": 3},
+    "grep+read": {"label": "grep + read file", "color": "#922b21", "linewidth": 1.8, "zorder": 3},
     "ripgrep-c8": {
         "label": "ripgrep -C 8 (snippets)",
         "color": "#707070",
@@ -640,15 +563,6 @@ def run_recall(args: argparse.Namespace) -> None:
     enc = tiktoken.get_encoding(_TOKENIZER_NAME)
     model = StaticModel.from_pretrained(_DEFAULT_MODEL_NAME)
 
-    keywords: dict[str, str] | None = None
-    if not args.no_keyword:
-        try:
-            print("Pre-fetching keyword patterns (requires OPENAI_API_KEY)...", file=sys.stderr)
-            keywords = asyncio.run(_prefetch_keywords([t.query for t in tasks]))
-            print(f"  done ({len(keywords)} unique queries)", file=sys.stderr)
-        except Exception as exc:  # noqa: BLE001
-            print(f"  keyword pre-fetch failed ({exc}); skipping keyword-grep+read", file=sys.stderr)
-
     method_curves: dict[str, MethodCurves] = defaultdict(list)
     print(f"\n{'Repo':<22} {'Language':<12} {'Tasks':>6} {'Time':>8}", file=sys.stderr)
     print(f"{'-' * 22} {'-' * 12} {'-' * 6} {'-' * 8}", file=sys.stderr)
@@ -656,7 +570,7 @@ def run_recall(args: argparse.Namespace) -> None:
         spec = repo_specs[repo]
         started = time.perf_counter()
         index = SembleIndex.from_path(spec.benchmark_dir, model=model)
-        per_method = _evaluate_repo_recall(index, repo_task_list, spec.benchmark_dir, enc, keywords)
+        per_method = _evaluate_repo_recall(index, repo_task_list, spec.benchmark_dir, enc)
         for m, lst in per_method.items():
             method_curves[m].extend(lst)
         print(
@@ -700,11 +614,7 @@ def run_judge(args: argparse.Namespace) -> None:
     enc = tiktoken.get_encoding(_TOKENIZER_NAME)
     model = StaticModel.from_pretrained(_DEFAULT_MODEL_NAME)
 
-    print("Pre-fetching keyword patterns...", file=sys.stderr)
-    keywords = asyncio.run(_prefetch_keywords([t.query for t in sample]))
-    print(f"  done ({len(keywords)} unique queries)", file=sys.stderr)
-
-    pending = _build_pending_judge(sample, repo_specs, model, enc, keywords, tuple(args.keyword_caps))
+    pending = _build_pending_judge(sample, repo_specs, model, enc)
     attempted_per_method: dict[str, int] = defaultdict(int)
     for _, method, _, _ in pending:
         attempted_per_method[method] += 1
@@ -743,11 +653,9 @@ def run_judge(args: argparse.Namespace) -> None:
     for method, c in e_costs.items():
         print(f"  {method:<24} {c:>9.0f}", file=sys.stderr)
 
-    if "semble" in e_costs:
-        for method in ("grep+read", "keyword-grep+read"):
-            if method in e_costs and e_costs[method] > 0:
-                reduction = 1.0 - e_costs["semble"] / e_costs[method]
-                print(f"Reduction (semble vs {method}, end-to-end): {reduction:.1%}", file=sys.stderr)
+    if "semble" in e_costs and e_costs.get("grep+read", 0) > 0:
+        reduction = 1.0 - e_costs["semble"] / e_costs["grep+read"]
+        print(f"Reduction (semble vs grep+read, end-to-end): {reduction:.1%}", file=sys.stderr)
 
     payload = {
         "tool": "context-efficiency-judge",
@@ -757,24 +665,15 @@ def run_judge(args: argparse.Namespace) -> None:
         "seed": args.seed,
         "budgets": {"semble": _JUDGE_SEMBLE_BUDGET, "grep+read": _JUDGE_GREP_BUDGET},
         "fallback_tokens": fallback,
-        "keyword_patterns": keywords,
         "summary": summary,
         "raw_retrieval_reduction_vs_semble": {m: round(v, 4) for m, v in raw_reductions.items() if m != "semble"},
         "expected_cost": e_costs,
-        "records": [
-            {
-                "repo": r.repo,
-                "query": r.query,
-                "category": r.category,
-                "method": r.method,
-                "tokens": r.tokens_used,
-                "answered": r.answered,
-            }
-            for r in records
-        ],
     }
+    records_out = _save_judge_records(records)
+    payload["records_file"] = records_out.name
     out = save_results("context-efficiency-judge", payload)
     print(f"\nResults saved to {out}", file=sys.stderr)
+    print(f"Records saved to {records_out}", file=sys.stderr)
 
 
 def run_plot(args: argparse.Namespace) -> None:
@@ -791,12 +690,11 @@ def run_plot(args: argparse.Namespace) -> None:
 def _parse_args() -> argparse.Namespace:
     """Parse CLI arguments."""
     parser = argparse.ArgumentParser(description="Context-efficiency benchmark: semble vs grep workflows.")
-    parser.set_defaults(func=run_recall, repo=[], language=[], no_keyword=False, no_plot=False)
+    parser.set_defaults(func=run_recall, repo=[], language=[], no_plot=False)
     sub = parser.add_subparsers(dest="mode", required=False)
 
     recall = sub.add_parser("recall", help="Recall vs. token-budget across all queries (default).")
     add_filter_args(recall)
-    recall.add_argument("--no-keyword", action="store_true", help="Skip the LLM keyword-extraction baseline.")
     recall.add_argument("--no-plot", action="store_true", help="Skip plotting after the run.")
     recall.set_defaults(func=run_recall)
 
@@ -810,13 +708,6 @@ def _parse_args() -> argparse.Namespace:
         default=7474.0,
         help="Tokens an agent spends to fall back on a miss (pessimistic default).",
     )
-    judge.add_argument(
-        "--keyword-caps",
-        type=int,
-        nargs="+",
-        default=[_KEYWORD_MAX_FILES],
-        help="Top-N file caps for keyword-grep+read sensitivity sweep.",
-    )
     judge.set_defaults(func=run_judge)
 
     plot = sub.add_parser("plot", help="Regenerate the recall-vs-tokens plot from a saved JSON.")
diff --git a/benchmarks/results/context-efficiency-judge-6248855cfef8.json b/benchmarks/results/context-efficiency-judge-6248855cfef8.json
index 80804f7..d511ba8 100644
--- a/benchmarks/results/context-efficiency-judge-6248855cfef8.json
+++ b/benchmarks/results/context-efficiency-judge-6248855cfef8.json
@@ -9,203 +9,6 @@
     "grep+read": 16000
   },
   "fallback_tokens": 7474.0,
-  "keyword_patterns": {
-    "WebSocket upgrade and handler": "WebSocket\\ upgrade\\ and\\ handler",
-    "how download requests save responses to disk": "download_request|save_response_to_disk|download_to_file",
-    "serving a file index page for a folder on disk": "file_index|directory_listing|index_page",
-    "flow operators map filter transform and collect": "map|filter|collect",
-    "how middleware chains context transformations between procedures": "middleware|chain|context_transformation",
-    "how HTTP proxy tunnel CONNECT method is initiated": "CONNECT|HTTP_PROXY|proxy_tunnel",
-    "ObjectMapper": "ObjectMapper|ObjectMapper::readValue|com.fasterxml.jackson.databind.ObjectMapper",
-    "Notify": "notify|notification|NotifyListener",
-    "how SETRANGE modifies a substring within a string value": "how\\ SETRANGE\\ modifies\\ a\\ substring\\ within\\ a\\ string\\ value",
-    "parameter type coercion and value validation": "type_coercion|value_validation",
-    "SqlMapper": "SqlMapper",
-    "how plugin installations and updates are managed with tasks": "plugin_install|plugin_update|task_scheduler",
-    "how mini.pick implements a fuzzy finder picker": "mini.pick|fuzzy_finder|picker",
-    "timer implementation and heap-based scheduling": "timer_impl|heap_scheduler|timing_wheel",
-    "how the async mutex prevents concurrent access": "async_mutex|AsyncMutex|mutex_async",
-    "BoxedIntoRoute and type-erased route storage": "BoxedIntoRoute|type-erased|route_storage",
-    "Uri": "Uri|URI|IUriProvider",
-    "CommandDefinition": "CommandDefinition",
-    "jump plugin for bookmarking directories": "jump|bookmark|directory_bookmark",
-    "AsyncClient": "AsyncClient|Async::Client|AsyncClientImpl",
-    "JsonReader base class and token reading API": "JsonReader|token|TokenType",
-    "radix tree path matching": "radix_tree|radix_trie|path_matching",
-    "how res.redirect builds the Location header and status code": "how\\ res\\.redirect\\ builds\\ the\\ Location\\ header\\ and\\ status\\ code",
-    "how Monad composes dependent effectful computations with flatMap": "Monad|flatMap|dependent_effects",
-    "before and after filter hooks for the request lifecycle": "before_filter|after_filter|request_lifecycle",
-    "test runner worker pool scheduling and execution": "worker_pool|test_runner|scheduling",
-    "how xmonad handles floating windows separately from tiled ones": "xmonad|floating|tiled",
-    "how listeners are subscribed and notified after state changes": "listener|subscribe|notify",
-    "StringUtils": "StringUtils|string_utils|StringUtility",
-    "how curl decides whether to retry a failed request": "curl_retry|CURLOPT_RETRY|retry_after",
-    "how backpressure and flow control work in streaming": "backpressure|flow_control|streaming_protocol",
-    "URL": "url|URL|Uri",
-    "Sinatra::ShowExceptions": "Sinatra::ShowExceptions",
-    "InterceptorManager": "InterceptorManager",
-    "MiddlewareBuilder": "MiddlewareBuilder",
-    "how query parameters are extracted and decoded from URIs": "query_param|parse_qs|URLDecoder",
-    "inferProcedureInput": "inferProcedureInput|infer_procedure_input|InferProcedureInput",
-    "Channel": "Channel|channel_layer|broadcast",
-    "APIRouter": "APIRouter",
-    "how discriminated unions select the right model variant": "discriminated_union|tagged_union|variant_selection",
-    "how Phoenix logs request and response lifecycle events": "Phoenix.Logger|telemetry|Plug.Conn",
-    "event hooks system": "event_hook|hook_system|event_listener",
-    "how DSL protocols define layout anchor properties like left, top, edges, and center for views and guides": "layout_anchor|anchor_property|layout_guide",
-    "how HttpRoutes define pattern-matched HTTP request handlers": "HttpRoutes|pattern-matched|request_handler",
-    "how subword token weights are aggregated for whole-word embeddings": "subword_token|whole_word_embedding|token_weighting",
-    "rvm ruby version manager plugin": "rvm|ruby-version|rvm-plugin",
-    "battery level detection for shell prompt": "battery_level|power_supply|upower",
-    "how app.render resolves views and calls the template engine": "app.render|render_views|template_engine",
-    "syslog handler for system-level logging": "syslog|SysLogHandler|systemd-journald",
-    "how type inference extracts input and output types from procedures": "type_inference|input_type|output_type",
-    "how MethodRouter dispatches GET POST PUT DELETE by HTTP method": "MethodRouter|dispatch|HTTP_METHOD",
-    "HCursor and cursor navigation for traversing JSON trees": "HCursor|cursor_navigation|JsonCursor",
-    "how RDB persistence serializes the database to disk": "RDB|persistence|serialize",
-    "how are static files served": "static_file|static_files|serve_static",
-    "Kleisli": "Kleisli|kleisli|KleisliArrow",
-    "Sinatra::Helpers": "Sinatra::Helpers|Sinatra::Base|helpers",
-    "how nvm_has checks whether a command exists on the system": "nvm_has|nvm_has_function|command_exists",
-    "how bats should retry a flaky test": "flaky_test|retry_logic|test_retry",
-    "AnyRouter": "AnyRouter",
-    "request and response interceptors": "interceptor|request_interceptor|response_interceptor",
-    "adl_serializer": "adl_serializer",
-    "toString builder for human-readable object descriptions": "toString|to_string|toStringBuilder",
-    "spawning and aborting async subprocess commands with uv": "uv_spawn|uv_process_kill|libuv",
-    "how zls provides goto definition for symbols": "goto_definition|definition_provider|symbol_resolution",
-    "Utils": "Utils|UtilsImpl|util_namespace",
-    "Codec combining Encoder and Decoder for bidirectional conversion": "Codec|Encoder|Decoder",
-    "Mutex": "Mutex|std::mutex|pthread_mutex_t",
-    "TypeFactory": "TypeFactory",
-    "how server-sent events and subscriptions are streamed to the client": "server-sent-events|SSE|EventSource",
-    "how vitest discovers and runs test files": "vitest|vite-plugin-test|testRegex",
-    "how the PandocMonad typeclass abstracts IO for readers and writers": "PandocMonad|Reader|Writer",
-    "hub github cli aliases and helpers": "hub|github|cli",
-    "how toJSONKeyText creates a ToJSONKey instance from a text function": "toJSONKeyText|ToJSONKey|ToJSONKeyHelper",
-    "RotatingFileHandler": "RotatingFileHandler|RotatingFileHandler.py|RotatingFileHandlerConfig",
-    "HTTPStatusError": "HTTPStatusError",
-    "how zls DocumentStore loads and caches Zig source files": "how\\ zls\\ DocumentStore\\ loads\\ and\\ caches\\ Zig\\ source\\ files",
-    "how curl resolves and connects to a remote host": "curl_resolve|Curl_connect|CURLOPT_RESOLVE",
-    "basic_json": "basic_json|nlohmann::basic_json|to_json",
-    "middleware pipeline composition": "middleware|pipeline|composition",
-    "Application": "Application|App|application.py",
-    "reducer type definition": "reducer|Reducer|ReducerType",
-    "how debug.print outputs to stderr for debugging": "debug.print|stderr|debugging",
-    "FromRequest": "FromRequest|FromRequestParts|axum::extract::FromRequest",
-    "ssh agent plugin for managing ssh keys": "ssh-agent|ssh-agent-plugin|ssh-key-agent",
-    "how mini.completion provides LSP-based autocompletion": "mini.completion|LSP|autocompletion",
-    "node version manager nvm integration": "nvm|node_version_manager|nvmrc",
-    "how client plugins intercept and transform requests and responses": "client_plugin|intercept_request|transform_response",
-    "absl::AnyInvocable for move-only callable wrapper": "AnyInvocable|absl::AnyInvocable|move-only",
-    "case-insensitive hash for request params": "case_insensitive_hash|CaseInsensitiveHash|case_insensitive_map",
-    "how nvm finds the nearest .nvmrc file by walking up the directory tree": "how\\ nvm\\ finds\\ the\\ nearest\\ \\.nvmrc\\ file\\ by\\ walking\\ up\\ the\\ directory\\ tree",
-    "how text sections are rendered in the lazy view": "lazy_view|text_section|render_section",
-    "how zls generates diagnostic errors and warnings": "zls|diagnostic|diagnostics",
-    "PowerShell completion generation": "powershell_completion|tab_completion|CompletionResult",
-    "Sinatra::Application": "Sinatra::Application|Sinatra::Base|Sinatra::Delegator",
-    "BackgroundTasks": "BackgroundTasks|background_task|BackgroundTask",
-    "Rails::Paths": "Rails::Paths|Rails::Application::Paths|rails_paths",
-    "how does Flask convert a view return value into a response object": "flask|make_response|flask.Response",
-    "how route groups and route collections compose routes": "route_group|route_collection|compose_routes",
-    "Request": "http_request|HTTPRequest|request_handler",
-    "IMessagePackFormatter": "IMessagePackFormatter|MessagePackFormatter|IMessagePackSerializer",
-    "how Kleisli composes functions that return monadic values": "Kleisli|monad|bind",
-    "how Phoenix routes are defined and dispatched to controller actions": "Phoenix.Router|route|controller_action",
-    "Command": "command|CLI|execute",
-    "how server trust evaluation handles SSL certificate pinning": "certificate_pinning|ssl_pinning|trust_evaluation",
-    "how slave keys with expiry are tracked in replication": "replication|slave_keys|expire_keys",
-    "how the event loop handles file events and time events": "event_loop|file_event|time_event",
-    "ObjectWriter": "ObjectWriter|ObjectWriter::Write|IObjectWriter",
-    "how lazy.nvim loads a plugin on demand when its trigger fires": "lazy.nvim|on_event|lazy.require",
-    "Monad": "Monad|monad|Functor",
-    "naming conventions for controllers and view modules": "controller|controller_name|view_module",
-    "how pandoc reads RST reStructuredText documents": "pandoc|reStructuredText|rst",
-    "BaseHTTPMiddleware": "BaseHTTPMiddleware|BaseHTTPMiddlewareDispatch",
-    "how PCA reduces embedding dimensionality": "PCA|principal_component_analysis|dimensionality_reduction",
-    "how tasks that are not Send can run on a single thread": "how\\ tasks\\ that\\ are\\ not\\ Send\\ can\\ run\\ on\\ a\\ single\\ thread",
-    "how is response validation and serialization applied before sending": "response_validation|response_serialization|serialize_response",
-    "UDP socket send and receive": "udp_send|udp_receive|recvfrom",
-    "Args": "Args|argparse|CommandLineParser",
-    "how app.handle dispatches requests through the middleware stack": "app.handle|middleware_stack|dispatches",
-    "how curl checks if HTTP If-Modified-Since condition is met": "If-Modified-Since|If-None-Match|curl_conditional_request",
-    "field_validator": "field_validator|FieldValidator|validate_field",
-    "saving and loading models from disk": "save_model|load_model|model_checkpoint",
-    "how the preprocessed test source is evaluated": "preprocessed_test|test_source|evaluate",
-    "StateManager": "StateManager",
-    "how proxy settings are read from environment variables": "http_proxy|HTTP_PROXY|no_proxy",
-    "fake timers and timer mocking utilities": "fake_timers|timer_mock|time_mocking",
-    "Rack::Response": "Rack::Response|Rack::Request|middleware",
-    "how Encoder typeclass converts Scala values to JSON": "Encoder|typeclass|toJson",
-    "how typeMismatch produces an error message for wrong JSON types": "typeMismatch|DecodingError|expected_value_type",
-    "ConstraintMaker": "ConstraintMaker|MASConstraintMaker|makeConstraints",
-    "how derive macros parse and apply field-level serde attributes": "derive_macro|serde_attribute|field_attributes",
-    "available request options and their configuration": "request_options|RequestOptions|request_config",
-    "how zls computes document scope for variable visibility": "document_scope|variable_visibility|zls",
-    "how the HTTP transport backend sends requests over the wire": "HTTPTransport|sendRequest|transport_backend",
-    "how nvm downloads a Node.js tarball from the remote": "nvm|download|tarball",
-    "how zls traverses the Zig AST to resolve symbol scopes": "Zig|AST|symbol_scope",
-    "how nvm manages the PATH to point to the active Node.js bin": "nvm|NODE_PATH|node_version_manager",
-    "value-based deserialization for self-describing formats": "self_describing|value_based_deserialization|self_describing_format",
-    "how does FastAPI run sync route functions without blocking": "how\\ does\\ FastAPI\\ run\\ sync\\ route\\ functions\\ without\\ blocking",
-    "Java runtime and OS system property helpers": "System.getProperty|RuntimeMXBean|os.name",
-    "snapshot testing serialization and comparison": "snapshot_testing|snapshot|serialization",
-    "decorator API for defining commands, options, and arguments": "decorator|command_decorator|option_argument_decorator",
-    "Rack::Builder": "Rack::Builder|rackup|config.ru",
-    "how tRPC procedures are defined and chained with input and output validation": "trpc|procedure|input_output_validation",
-    "formatter": "formatter|formatting|FormatException",
-    "res.send body encoding and ETag generation": "res.send|ETag|body_encoding",
-    "ClientResponse": "ClientResponse|client_response|HttpClientResponse",
-    "how bats finds a library in the BATS_LIB_PATH": "BATS_LIB_PATH|BATS_LIB|bats_find_library",
-    "how log records propagate through the handler bubble chain": "log_record|handler_chain|bubble_chain",
-    "queue connection resolution and connectors": "queue|connector|connection_resolution",
-    "HttpCookies": "HttpCookies|CookieJar|Set-Cookie",
-    "SchemaUtils for creating and dropping database tables": "SchemaUtils|createTable|dropTable",
-    "HTML template rendering": "template_rendering|html_template|template_engine",
-    "how telescope actions map keys to picker operations": "telescope_actions|map_keys|picker_operations",
-    "chrono duration and time point formatting": "std::chrono::duration|std::chrono::time_point|strftime",
-    "static file serving middleware": "static_file|file_server|static_middleware",
-    "model_validator": "model_validator|ModelValidator|validate_model",
-    "how pandoc's app module orchestrates reading, filtering, and writing": "pandoc::App|App.hs|Filters",
-    "TypeAdapter": "TypeAdapter|TypeAdapterFactory|TypeAdapterRuntimeTypeWrapper",
-    "how binary data is deserialized back into typed C# objects": "BinaryFormatter|BinaryReader|Deserialize",
-    "how pandoc generates PDF output via LaTeX or other backends": "pandoc|LaTeX|PDF_backend",
-    "PreparedRequest": "PreparedRequest|PreparedRequestAdapter|PreparedRequestFactory",
-    "how the router matches path segments to controller/action": "router|path_segment|controller_action",
-    "mock handler for simulating HTTP responses in tests": "mock_handler|mock_http|fake_http_response",
-    "CompletionOptions": "CompletionOptions",
-    "Axios": "Axios|axios.request|axios.create",
-    "Rails command dispatch and subcommand routing": "Rails\\ command\\ dispatch\\ and\\ subcommand\\ routing",
-    "combineReducers": "combineReducers|combine_reducers|combine-reducers",
-    "how Dapper caches parameter and deserializer IL": "Dapper|parameter_cache|deserializer_IL",
-    "how Applicative combines independent effects": "Applicative|ApplicativeFunctor|zipWithA",
-    "how axum nests routers and merges router trees": "axum::Router|Router::merge|nest_router",
-    "running work after returning an HTTP response without blocking": "background_task|deferred_response|post_response_task",
-    "connection filter chain setup for proxies and TLS": "filter_chain|connection_manager|tls_context",
-    "how nvm_get_latest fetches the latest available Node.js version": "nvm_get_latest|get_latest|nvm-fetch",
-    "ZodType": "ZodType|ZodSchema|zod.object",
-    "how mini.deps manages Neovim plugin installation and updates": "mini.deps|Neovim|plugin_installation",
-    "Schema": "schema|Schema|DBSchema",
-    "how Validated accumulates errors across independent computations": "Validated|accumulate_errors|ValidationNel",
-    "server startup initialization and configuration loading": "server_startup|initialization|configuration_loading",
-    "createStore": "createStore|configureStore|redux_store",
-    "streaming JSON reader and token parsing": "streaming_json|json_reader|token_parsing",
-    "JsonParser": "JsonParser",
-    "bindActionCreators": "bindActionCreators|redux|react-redux",
-    "how mini.diff shows inline git diff signs in buffers": "how\\ mini\\.diff\\ shows\\ inline\\ git\\ diff\\ signs\\ in\\ buffers",
-    "Router": "Router|Routing|route_match",
-    "DigestAuth": "DigestAuth|DigestAuthentication|HTTPDigest",
-    "how the handler stack wraps middleware around the HTTP send": "handler_stack|middleware|http_send",
-    "Serialize": "serialize|Serializer|Serialization",
-    "axios default configuration and request/response transformers": "axios|default_adapter|transformRequest|transformResponse",
-    "Ecto.Changeset for validating and transforming data": "Ecto.Changeset|changeset|validate_change",
-    "applyMiddleware": "applyMiddleware|middleware|redux",
-    "how type adapter factories are registered and selected for a given type": "type_adapter_factory|TypeAdapterFactory|register_type_adapter",
-    "ExceptionUtils": "ExceptionUtils|ExceptionUtil|exception_utils",
-    "boolean conversion and parsing helpers": "boolean_conversion|parse_bool|bool_parser",
-    "Ecto.Query DSL for composing SQL queries": "Ecto.Query|Ecto.Queryable|from",
-    "how log handlers are registered and invoked by Logger": "Logger|log_handler|register_handler"
-  },
   "summary": {
     "semble": {
       "n": 198,
@@ -230,9575 +33,14 @@
         "semantic": 0.015151515151515152,
         "symbol": 0.8636363636363636
       }
-    },
-    "keyword-grep+read-top5": {
-      "n": 198,
-      "n_attempted": 198,
-      "n_failed": 0,
-      "answer_rate": 0.7171717171717171,
-      "mean_tokens_retrieved": 10132.939393939394,
-      "answer_rate_by_category": {
-        "architecture": 0.6818181818181818,
-        "semantic": 0.5454545454545454,
-        "symbol": 0.9242424242424242
-      }
-    },
-    "keyword-grep+read-top10": {
-      "n": 198,
-      "n_attempted": 198,
-      "n_failed": 0,
-      "answer_rate": 0.7272727272727273,
-      "mean_tokens_retrieved": 10565.38383838384,
-      "answer_rate_by_category": {
-        "architecture": 0.7121212121212122,
-        "semantic": 0.5303030303030303,
-        "symbol": 0.9393939393939394
-      }
-    },
-    "keyword-grep+read": {
-      "n": 198,
-      "n_attempted": 198,
-      "n_failed": 0,
-      "answer_rate": 0.7121212121212122,
-      "mean_tokens_retrieved": 10594.934343434343,
-      "answer_rate_by_category": {
-        "architecture": 0.6818181818181818,
-        "semantic": 0.5151515151515151,
-        "symbol": 0.9393939393939394
-      }
-    },
-    "keyword-grep+read-top50": {
-      "n": 198,
-      "n_attempted": 198,
-      "n_failed": 0,
-      "answer_rate": 0.7171717171717171,
-      "mean_tokens_retrieved": 10594.929292929293,
-      "answer_rate_by_category": {
-        "architecture": 0.6818181818181818,
-        "semantic": 0.5303030303030303,
-        "symbol": 0.9393939393939394
-      }
     }
   },
   "raw_retrieval_reduction_vs_semble": {
-    "grep+read": 0.4996,
-    "keyword-grep+read-top5": 0.8027,
-    "keyword-grep+read-top10": 0.8107,
-    "keyword-grep+read": 0.8113,
-    "keyword-grep+read-top50": 0.8113
+    "grep+read": 0.4996
   },
   "expected_cost": {
     "semble": 2905.5505050505053,
-    "grep+read": 9281.00505050505,
-    "keyword-grep+read-top5": 12246.79797979798,
-    "keyword-grep+read-top10": 12603.747474747475,
-    "keyword-grep+read": 12746.540404040403,
-    "keyword-grep+read-top50": 12708.78787878788
+    "grep+read": 9281.00505050505
   },
-  "records": [
-    {
-      "repo": "abseil-cpp",
-      "query": "Mutex",
-      "category": "symbol",
-      "method": "semble",
-      "tokens": 2000,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "Mutex",
-      "category": "symbol",
-      "method": "grep+read",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "Mutex",
-      "category": "symbol",
-      "method": "keyword-grep+read-top5",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "Mutex",
-      "category": "symbol",
-      "method": "keyword-grep+read-top10",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "Mutex",
-      "category": "symbol",
-      "method": "keyword-grep+read",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "Mutex",
-      "category": "symbol",
-      "method": "keyword-grep+read-top50",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "absl::AnyInvocable for move-only callable wrapper",
-      "category": "semantic",
-      "method": "semble",
-      "tokens": 2000,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "absl::AnyInvocable for move-only callable wrapper",
-      "category": "semantic",
-      "method": "grep+read",
-      "tokens": 0,
-      "answered": false
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "absl::AnyInvocable for move-only callable wrapper",
-      "category": "semantic",
-      "method": "keyword-grep+read-top5",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "absl::AnyInvocable for move-only callable wrapper",
-      "category": "semantic",
-      "method": "keyword-grep+read-top10",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "absl::AnyInvocable for move-only callable wrapper",
-      "category": "semantic",
-      "method": "keyword-grep+read",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "abseil-cpp",
-      "query": "absl::AnyInvocable for move-only callable wrapper",
-      "category": "semantic",
-      "method": "keyword-grep+read-top50",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "aeson",
-      "query": "how toJSONKeyText creates a ToJSONKey instance from a text function",
-      "category": "semantic",
-      "method": "semble",
-      "tokens": 1999,
-      "answered": true
-    },
-    {
-      "repo": "aeson",
-      "query": "how toJSONKeyText creates a ToJSONKey instance from a text function",
-      "category": "semantic",
-      "method": "grep+read",
-      "tokens": 0,
-      "answered": false
-    },
-    {
-      "repo": "aeson",
-      "query": "how toJSONKeyText creates a ToJSONKey instance from a text function",
-      "category": "semantic",
-      "method": "keyword-grep+read-top5",
-      "tokens": 15999,
-      "answered": true
-    },
-    {
-      "repo": "aeson",
-      "query": "how toJSONKeyText creates a ToJSONKey instance from a text function",
-      "category": "semantic",
-      "method": "keyword-grep+read-top10",
-      "tokens": 15999,
-      "answered": true
-    },
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+  "records_file": "context-efficiency-judge-records-6248855cfef8.jsonl"
 }
diff --git a/benchmarks/results/context-efficiency-judge-records-6248855cfef8.jsonl b/benchmarks/results/context-efficiency-judge-records-6248855cfef8.jsonl
new file mode 100644
index 0000000..ccab5e3
--- /dev/null
+++ b/benchmarks/results/context-efficiency-judge-records-6248855cfef8.jsonl
@@ -0,0 +1,396 @@
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+{"repo":"rack","query":"Rack::Builder","category":"symbol","method":"grep+read","tokens":2490,"answered":true}
+{"repo":"rack","query":"static file serving middleware","category":"semantic","method":"semble","tokens":2000,"answered":true}
+{"repo":"rack","query":"static file serving middleware","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"rack","query":"Request","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"rack","query":"Request","category":"symbol","method":"grep+read","tokens":16000,"answered":true}
+{"repo":"rails","query":"Rails::Paths","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"rails","query":"Rails::Paths","category":"symbol","method":"grep+read","tokens":2801,"answered":true}
+{"repo":"rails","query":"Rails command dispatch and subcommand routing","category":"semantic","method":"semble","tokens":2000,"answered":true}
+{"repo":"rails","query":"Rails command dispatch and subcommand routing","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redis","query":"how SETRANGE modifies a substring within a string value","category":"semantic","method":"semble","tokens":2000,"answered":false}
+{"repo":"redis","query":"how SETRANGE modifies a substring within a string value","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redis","query":"how RDB persistence serializes the database to disk","category":"architecture","method":"semble","tokens":2000,"answered":false}
+{"repo":"redis","query":"how RDB persistence serializes the database to disk","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redis","query":"how the event loop handles file events and time events","category":"architecture","method":"semble","tokens":2000,"answered":false}
+{"repo":"redis","query":"how the event loop handles file events and time events","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redis","query":"how slave keys with expiry are tracked in replication","category":"semantic","method":"semble","tokens":1999,"answered":false}
+{"repo":"redis","query":"how slave keys with expiry are tracked in replication","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redis","query":"server startup initialization and configuration loading","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"redis","query":"server startup initialization and configuration loading","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redux","query":"how listeners are subscribed and notified after state changes","category":"architecture","method":"semble","tokens":1999,"answered":true}
+{"repo":"redux","query":"how listeners are subscribed and notified after state changes","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redux","query":"middleware pipeline composition","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"redux","query":"middleware pipeline composition","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redux","query":"reducer type definition","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"redux","query":"reducer type definition","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"redux","query":"combineReducers","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"redux","query":"combineReducers","category":"symbol","method":"grep+read","tokens":7010,"answered":true}
+{"repo":"redux","query":"createStore","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"redux","query":"createStore","category":"symbol","method":"grep+read","tokens":9042,"answered":true}
+{"repo":"redux","query":"bindActionCreators","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"redux","query":"bindActionCreators","category":"symbol","method":"grep+read","tokens":941,"answered":true}
+{"repo":"redux","query":"applyMiddleware","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"redux","query":"applyMiddleware","category":"symbol","method":"grep+read","tokens":5283,"answered":true}
+{"repo":"requests","query":"event hooks system","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"requests","query":"event hooks system","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"requests","query":"how proxy settings are read from environment variables","category":"architecture","method":"semble","tokens":1999,"answered":true}
+{"repo":"requests","query":"how proxy settings are read from environment variables","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"requests","query":"PreparedRequest","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"requests","query":"PreparedRequest","category":"symbol","method":"grep+read","tokens":15999,"answered":true}
+{"repo":"serde","query":"how derive macros parse and apply field-level serde attributes","category":"architecture","method":"semble","tokens":1999,"answered":true}
+{"repo":"serde","query":"how derive macros parse and apply field-level serde attributes","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"serde","query":"value-based deserialization for self-describing formats","category":"semantic","method":"semble","tokens":2000,"answered":true}
+{"repo":"serde","query":"value-based deserialization for self-describing formats","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"serde","query":"Serialize","category":"symbol","method":"semble","tokens":1998,"answered":true}
+{"repo":"serde","query":"Serialize","category":"symbol","method":"grep+read","tokens":16000,"answered":false}
+{"repo":"sinatra","query":"before and after filter hooks for the request lifecycle","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"sinatra","query":"before and after filter hooks for the request lifecycle","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"sinatra","query":"Sinatra::ShowExceptions","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"sinatra","query":"Sinatra::ShowExceptions","category":"symbol","method":"grep+read","tokens":3365,"answered":true}
+{"repo":"sinatra","query":"Sinatra::Helpers","category":"symbol","method":"semble","tokens":2000,"answered":false}
+{"repo":"sinatra","query":"Sinatra::Helpers","category":"symbol","method":"grep+read","tokens":0,"answered":false}
+{"repo":"sinatra","query":"Sinatra::Application","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"sinatra","query":"Sinatra::Application","category":"symbol","method":"grep+read","tokens":15999,"answered":true}
+{"repo":"sinatra","query":"case-insensitive hash for request params","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"sinatra","query":"case-insensitive hash for request params","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"snapkit","query":"how DSL protocols define layout anchor properties like left, top, edges, and center for views and guides","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"snapkit","query":"how DSL protocols define layout anchor properties like left, top, edges, and center for views and guides","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"snapkit","query":"ConstraintMaker","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"snapkit","query":"ConstraintMaker","category":"symbol","method":"grep+read","tokens":9219,"answered":true}
+{"repo":"starlette","query":"how are static files served","category":"architecture","method":"semble","tokens":2000,"answered":false}
+{"repo":"starlette","query":"how are static files served","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"starlette","query":"Request","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"starlette","query":"Request","category":"symbol","method":"grep+read","tokens":15999,"answered":false}
+{"repo":"starlette","query":"BaseHTTPMiddleware","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"starlette","query":"BaseHTTPMiddleware","category":"symbol","method":"grep+read","tokens":2060,"answered":true}
+{"repo":"telescope.nvim","query":"how telescope actions map keys to picker operations","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"telescope.nvim","query":"how telescope actions map keys to picker operations","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"tokio","query":"Notify","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"tokio","query":"Notify","category":"symbol","method":"grep+read","tokens":16000,"answered":true}
+{"repo":"tokio","query":"how the async mutex prevents concurrent access","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"tokio","query":"how the async mutex prevents concurrent access","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"tokio","query":"how tasks that are not Send can run on a single thread","category":"architecture","method":"semble","tokens":1999,"answered":true}
+{"repo":"tokio","query":"how tasks that are not Send can run on a single thread","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"trpc","query":"how middleware chains context transformations between procedures","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"trpc","query":"how middleware chains context transformations between procedures","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"trpc","query":"inferProcedureInput","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"trpc","query":"inferProcedureInput","category":"symbol","method":"grep+read","tokens":6138,"answered":true}
+{"repo":"trpc","query":"MiddlewareBuilder","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"trpc","query":"MiddlewareBuilder","category":"symbol","method":"grep+read","tokens":8486,"answered":true}
+{"repo":"trpc","query":"how type inference extracts input and output types from procedures","category":"semantic","method":"semble","tokens":2000,"answered":true}
+{"repo":"trpc","query":"how type inference extracts input and output types from procedures","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"trpc","query":"how server-sent events and subscriptions are streamed to the client","category":"architecture","method":"semble","tokens":1999,"answered":true}
+{"repo":"trpc","query":"how server-sent events and subscriptions are streamed to the client","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"trpc","query":"AnyRouter","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"trpc","query":"AnyRouter","category":"symbol","method":"grep+read","tokens":15999,"answered":true}
+{"repo":"trpc","query":"how tRPC procedures are defined and chained with input and output validation","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"trpc","query":"how tRPC procedures are defined and chained with input and output validation","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"vapor","query":"Application","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"vapor","query":"Application","category":"symbol","method":"grep+read","tokens":16000,"answered":true}
+{"repo":"vapor","query":"how route groups and route collections compose routes","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"vapor","query":"how route groups and route collections compose routes","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"vitest","query":"test runner worker pool scheduling and execution","category":"architecture","method":"semble","tokens":1999,"answered":true}
+{"repo":"vitest","query":"test runner worker pool scheduling and execution","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"vitest","query":"how vitest discovers and runs test files","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"vitest","query":"how vitest discovers and runs test files","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"vitest","query":"StateManager","category":"symbol","method":"semble","tokens":2000,"answered":true}
+{"repo":"vitest","query":"StateManager","category":"symbol","method":"grep+read","tokens":15999,"answered":true}
+{"repo":"vitest","query":"fake timers and timer mocking utilities","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"vitest","query":"fake timers and timer mocking utilities","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"vitest","query":"snapshot testing serialization and comparison","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"vitest","query":"snapshot testing serialization and comparison","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"xmonad","query":"how xmonad handles floating windows separately from tiled ones","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"xmonad","query":"how xmonad handles floating windows separately from tiled ones","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"zig","query":"how debug.print outputs to stderr for debugging","category":"semantic","method":"semble","tokens":2000,"answered":false}
+{"repo":"zig","query":"how debug.print outputs to stderr for debugging","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"zls","query":"how zls provides goto definition for symbols","category":"semantic","method":"semble","tokens":2000,"answered":true}
+{"repo":"zls","query":"how zls provides goto definition for symbols","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"zls","query":"how zls DocumentStore loads and caches Zig source files","category":"architecture","method":"semble","tokens":2000,"answered":false}
+{"repo":"zls","query":"how zls DocumentStore loads and caches Zig source files","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"zls","query":"how zls generates diagnostic errors and warnings","category":"semantic","method":"semble","tokens":1999,"answered":true}
+{"repo":"zls","query":"how zls generates diagnostic errors and warnings","category":"semantic","method":"grep+read","tokens":0,"answered":false}
+{"repo":"zls","query":"how zls traverses the Zig AST to resolve symbol scopes","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"zls","query":"how zls traverses the Zig AST to resolve symbol scopes","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"zls","query":"how zls computes document scope for variable visibility","category":"architecture","method":"semble","tokens":2000,"answered":true}
+{"repo":"zls","query":"how zls computes document scope for variable visibility","category":"architecture","method":"grep+read","tokens":0,"answered":false}
+{"repo":"zod","query":"ZodType","category":"symbol","method":"semble","tokens":1999,"answered":true}
+{"repo":"zod","query":"ZodType","category":"symbol","method":"grep+read","tokens":16000,"answered":true}

From 6d7dea15950e62e91f41868f385c6819c39084a9 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Wed, 29 Apr 2026 08:13:33 +0200
Subject: [PATCH 03/18] Updated benchmark

---
 benchmarks/README.md             | 36 ++++++++---------
 benchmarks/context_efficiency.py | 66 +++++++++++---------------------
 2 files changed, 40 insertions(+), 62 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index e50c8c6..abc4dc1 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -62,36 +62,34 @@ NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best sc
 
 Coding agents often use `grep` to find candidate files and then read those files into context. We model that direct `grep + read file` workflow and compare it with semble's chunk retrieval.
 
-> Across a 198-query category-balanced sample judged by GPT-5-mini, semble's retrieved context answers **88%** of queries at 2k tokens. Direct `grep + read file` answers **29%** and spends ~4k tokens. That is a **50% reduction in retrieved code-context tokens**; with a pessimistic fallback model for misses, expected end-to-end context cost drops by **69%**.
-
 ![Recall vs. retrieved tokens](../assets/images/recall_vs_tokens.png)
 
 ### Answer sufficiency (LLM-as-judge)
 
-For each query we hand the retrieved context to GPT-5-mini and ask whether it contains enough relevant code to answer the query. The sample is 198 queries, balanced 66/66/66 across semantic, architecture, and symbol categories.
+For each query we hand the retrieved context to GPT-5-mini and ask whether it contains enough relevant code to answer the query. The sample is ~198 queries, balanced across semantic, architecture, and symbol categories.
 
-- **semble** — top-K chunks, hard-capped at 2k tokens.
-- **grep+read** — the raw query string is passed to `rg --fixed-strings`; matching files are read in full, hard-capped at 16k tokens.
+- **semble top-N** — the top-N ranked chunks returned by semble, formatted as-is (no artificial cap). Each variant models a different `top_k` setting in the MCP tool.
+- **grep+read** — the raw query string is passed to `rg --fixed-strings`; all matching files are read in full (capped at 32k tokens as a safety ceiling, rarely hit in practice).
 
 Both workflows search the same code-file universe: semble's indexed extensions, excluding `node_modules`, `dist`, `build`, `.venv`, and other ignored directories.
 
-| Method | Budget | Answer rate | Mean tokens retrieved | Token reduction with semble | Expected end-to-end tokens† |
-|---|---:|---:|---:|---:|---:|
-| **semble** | 2k | **0.879** | 2,000 | — | **2,906** |
-| grep+read | 16k | 0.293 | 3,996 | 50.0% | 9,281 |
-
-**Raw retrieval reduction:** 50.0% fewer retrieved code-context tokens vs `grep+read`.
-**End-to-end reduction (with fallback model):** 68.7% fewer expected context tokens vs `grep+read`.
+| Method | Mean tokens | Answer rate |
+|---|---:|---:|
+| semble top-3 | — | — |
+| semble top-5 | — | — |
+| semble top-10 | — | — |
+| semble top-20 | — | — |
+| grep+read | — | — |
 
-†The end-to-end column adds `(1 − answer_rate) × 7,474`, modeling a fallback search on a miss. The constant is the median tokens `grep+read` spends to surface a relevant file. The raw retrieval reduction is the model-free comparison. This benchmark measures retrieved code-context tokens, not total IDE or agent billing tokens.
+> Run `uv run python -m benchmarks.context_efficiency judge` to populate this table.
 
 **Answer rate by query category:**
 
-| Category | semble | grep+read |
+| Category | semble top-5 | grep+read |
 |---|---:|---:|
-| symbol (named entity lookup) | 0.97 | 0.86 |
-| semantic (behavior / concept) | 0.85 | 0.02 |
-| architecture (design / structure) | 0.82 | 0.00 |
+| symbol (named entity lookup) | — | — |
+| semantic (behavior / concept) | — | — |
+| architecture (design / structure) | — | — |
 
 ### Recall at fixed token budgets
 
@@ -108,9 +106,9 @@ A coarser, label-based view across the full 1,251-query benchmark: a relevant fi
 
 For each query we retrieve units in rank order — semble chunks for semble, full files in match-count order for `grep+read`, and merged context windows for `ripgrep -C 8` in the recall curve. Tokens are counted with `cl100k_base` via `tiktoken`. `grep` is run with `--fixed-strings --ignore-case` on the raw query and scoped via `--glob` to the same code-file extensions and ignored directories that semble indexes.
 
-In judge mode each context is hard-capped at its stated budget by token IDs and asserted `len(ids) ≤ budget`. Recall mode does not truncate; its curves record cumulative tokens of whole retrieved units. The judge sample is macro-balanced across categories; the recall benchmark uses all 1,251 queries.
+Recall mode does not truncate; its curves record cumulative tokens of whole retrieved units. The judge sample is macro-balanced across categories; the recall benchmark uses all 1,251 queries.
 
-GPT-5-mini sees the query and capped retrieved context and answers a strict yes/no on whether the context contains code that directly addresses the query. A relevant file is "covered" in recall once any retrieved unit comes from it; where line spans are present we require span overlap.
+GPT-5-mini sees the query and retrieved context and answers a strict yes/no on whether the context contains code that directly addresses the query. A relevant file is "covered" in recall once any retrieved unit comes from it; where line spans are present we require span overlap.
 
 </details>
 
diff --git a/benchmarks/context_efficiency.py b/benchmarks/context_efficiency.py
index 45290db..4297fdd 100644
--- a/benchmarks/context_efficiency.py
+++ b/benchmarks/context_efficiency.py
@@ -18,6 +18,7 @@
 from openai import AsyncOpenAI
 
 from benchmarks.data import (
+    RepoSpec,
     Target,
     Task,
     add_filter_args,
@@ -47,8 +48,8 @@
 _JUDGE_CONCURRENCY = 8
 _JUDGE_RETRIES = 3
 _JUDGE_DEFAULT_SAMPLE = 200
-_JUDGE_SEMBLE_BUDGET = 2000
-_JUDGE_GREP_BUDGET = 16000
+_JUDGE_TOP_KS = (3, 5, 10, 20)
+_JUDGE_CONTEXT_CAP = 32_000
 
 _IMAGES_DIR = Path(__file__).parent.parent / "assets" / "images"
 _RESULTS_DIR = Path(__file__).parent / "results"
@@ -198,6 +199,18 @@ def _retrieval_units_for_task(
     return methods
 
 
+def _retrieval_units_for_judge(
+    index: SembleIndex,
+    task: Task,
+    repo_dir: Path,
+) -> list[tuple[str, list[Chunk]]]:
+    """Return (method, units) for the judge: semble sliced at each top_k, plus grep+read."""
+    all_semble = _semble_units(index, task.query)
+    methods: list[tuple[str, list[Chunk]]] = [(f"semble-top{k}", all_semble[:k]) for k in _JUDGE_TOP_KS]
+    methods.append(("grep+read", _grep_file_units(task.query, repo_dir)))
+    return methods
+
+
 def _curve(units: list[Chunk], targets: tuple[Target, ...], enc: Any) -> Curve:
     """Cumulative (tokens, covered_target_count) after each retrieved unit, starting at (0, 0)."""
     covered = [False] * len(targets)
@@ -388,7 +401,7 @@ def _evaluate_repo_recall(
 
 def _build_pending_judge(
     tasks: list[Task],
-    repo_specs: dict[str, Any],
+    repo_specs: dict[str, RepoSpec],
     model: StaticModel,
     enc: Any,
 ) -> list[tuple[Task, str, str, int]]:
@@ -401,13 +414,8 @@ def _build_pending_judge(
         started = time.perf_counter()
         index = SembleIndex.from_path(spec.benchmark_dir, model=model)
         for task in task_list:
-            for method, units in _retrieval_units_for_task(
-                index,
-                task,
-                spec.benchmark_dir,
-            ):
-                budget = _JUDGE_SEMBLE_BUDGET if method == "semble" else _JUDGE_GREP_BUDGET
-                context, tokens = _format_context(units, budget, enc)
+            for method, units in _retrieval_units_for_judge(index, task, spec.benchmark_dir):
+                context, tokens = _format_context(units, _JUDGE_CONTEXT_CAP, enc)
                 pending.append((task, method, context, tokens))
         print(f"{repo:<22} {len(task_list):>6} {time.perf_counter() - started:>7.1f}s", file=sys.stderr)
     return pending
@@ -629,45 +637,23 @@ def run_judge(args: argparse.Namespace) -> None:
         print(f"  WARNING: {n_missing} verdicts missing — see n_failed per method.", file=sys.stderr)
 
     summary = _aggregate_judge(records, dict(attempted_per_method))
-    print(f"\n{'Method':<24} {'n':>5} {'answer_rate':>12} {'mean_tokens':>12}", file=sys.stderr)
-    print(f"{'-' * 24} {'-' * 5} {'-' * 12} {'-' * 12}", file=sys.stderr)
+    print(f"\n{'Method':<24} {'mean_tokens':>12} {'answer_rate':>12}", file=sys.stderr)
+    print(f"{'-' * 24} {'-' * 12} {'-' * 12}", file=sys.stderr)
     for method, info in summary.items():
         print(
-            f"{method:<24} {info['n']:>5} {info['answer_rate']:>12.3f} {info['mean_tokens_retrieved']:>12.0f}",
+            f"{method:<24} {info['mean_tokens_retrieved']:>12.0f} {info['answer_rate']:>12.3f}",
             file=sys.stderr,
         )
 
-    semble_retrieval = summary.get("semble", {}).get("mean_tokens_retrieved", 0.0)
-    print("\nRaw retrieval tokens (no fallback model):", file=sys.stderr)
-    raw_reductions: dict[str, float] = {}
-    for method, info in summary.items():
-        other = info["mean_tokens_retrieved"]
-        red = 1.0 - semble_retrieval / other if method != "semble" and other > 0 else 0.0
-        raw_reductions[method] = red
-        suffix = f"  reduction vs semble: {red:.1%}" if method != "semble" else ""
-        print(f"  {method:<24} {other:>9.0f}{suffix}", file=sys.stderr)
-
-    fallback = args.fallback_tokens
-    e_costs = {m: s["mean_tokens_retrieved"] + ((1.0 - s["answer_rate"]) * fallback) for m, s in summary.items()}
-    print(f"\nExpected end-to-end tokens (pessimistic fallback = {fallback:.0f} tokens):", file=sys.stderr)
-    for method, c in e_costs.items():
-        print(f"  {method:<24} {c:>9.0f}", file=sys.stderr)
-
-    if "semble" in e_costs and e_costs.get("grep+read", 0) > 0:
-        reduction = 1.0 - e_costs["semble"] / e_costs["grep+read"]
-        print(f"Reduction (semble vs grep+read, end-to-end): {reduction:.1%}", file=sys.stderr)
-
     payload = {
         "tool": "context-efficiency-judge",
         "judge_model": _JUDGE_MODEL,
         "tokenizer": _TOKENIZER_NAME,
         "sample_size": len(sample),
         "seed": args.seed,
-        "budgets": {"semble": _JUDGE_SEMBLE_BUDGET, "grep+read": _JUDGE_GREP_BUDGET},
-        "fallback_tokens": fallback,
+        "top_ks": list(_JUDGE_TOP_KS),
+        "context_cap": _JUDGE_CONTEXT_CAP,
         "summary": summary,
-        "raw_retrieval_reduction_vs_semble": {m: round(v, 4) for m, v in raw_reductions.items() if m != "semble"},
-        "expected_cost": e_costs,
     }
     records_out = _save_judge_records(records)
     payload["records_file"] = records_out.name
@@ -702,12 +688,6 @@ def _parse_args() -> argparse.Namespace:
     judge.add_argument("--sample", type=int, default=_JUDGE_DEFAULT_SAMPLE, help="Number of stratified queries.")
     judge.add_argument("--seed", type=int, default=42, help="Random seed for sampling.")
     judge.add_argument("--concurrency", type=int, default=_JUDGE_CONCURRENCY, help="Concurrent judge calls.")
-    judge.add_argument(
-        "--fallback-tokens",
-        type=float,
-        default=7474.0,
-        help="Tokens an agent spends to fall back on a miss (pessimistic default).",
-    )
     judge.set_defaults(func=run_judge)
 
     plot = sub.add_parser("plot", help="Regenerate the recall-vs-tokens plot from a saved JSON.")

From 153999857e5b0b0cf18af0f122ce70372184092a Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Wed, 29 Apr 2026 08:50:19 +0200
Subject: [PATCH 04/18] Updated benchmark

---
 assets/images/recall_vs_tokens.png            | Bin 61963 -> 54685 bytes
 benchmarks/README.md                          |  56 +--
 benchmarks/context_efficiency.py              | 358 ++--------------
 ...context-efficiency-judge-6248855cfef8.json |  46 --
 ...fficiency-judge-records-6248855cfef8.jsonl | 396 ------------------
 ...ntext-efficiency-recall-6d7dea15950e.json} | 176 +++-----
 6 files changed, 97 insertions(+), 935 deletions(-)
 delete mode 100644 benchmarks/results/context-efficiency-judge-6248855cfef8.json
 delete mode 100644 benchmarks/results/context-efficiency-judge-records-6248855cfef8.jsonl
 rename benchmarks/results/{context-efficiency-recall-6248855cfef8.json => context-efficiency-recall-6d7dea15950e.json} (57%)

diff --git a/assets/images/recall_vs_tokens.png b/assets/images/recall_vs_tokens.png
index 6a623cb6ea286fc9bd3bbb977d9006980b9a0c69..2285f3c82bc316bd186f64f5aa85515ba619376c 100644
GIT binary patch
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diff --git a/benchmarks/README.md b/benchmarks/README.md
index abc4dc1..28b5c52 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -60,55 +60,32 @@ NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best sc
 
 ## Context efficiency
 
-Coding agents often use `grep` to find candidate files and then read those files into context. We model that direct `grep + read file` workflow and compare it with semble's chunk retrieval.
+Coding agents (Claude Code, OpenCode, etc.) typically find code by running `grep` on keywords and reading the matched files. We model that exact workflow and compare it against semble's chunk retrieval across 1,251 queries.
 
 ![Recall vs. retrieved tokens](../assets/images/recall_vs_tokens.png)
 
-### Answer sufficiency (LLM-as-judge)
-
-For each query we hand the retrieved context to GPT-5-mini and ask whether it contains enough relevant code to answer the query. The sample is ~198 queries, balanced across semantic, architecture, and symbol categories.
-
-- **semble top-N** — the top-N ranked chunks returned by semble, formatted as-is (no artificial cap). Each variant models a different `top_k` setting in the MCP tool.
-- **grep+read** — the raw query string is passed to `rg --fixed-strings`; all matching files are read in full (capped at 32k tokens as a safety ceiling, rarely hit in practice).
+### Recall at fixed token budgets
 
-Both workflows search the same code-file universe: semble's indexed extensions, excluding `node_modules`, `dist`, `build`, `.venv`, and other ignored directories.
+A relevant file is "covered" once any retrieved unit comes from it.
 
-| Method | Mean tokens | Answer rate |
-|---|---:|---:|
-| semble top-3 | — | — |
-| semble top-5 | — | — |
-| semble top-10 | — | — |
-| semble top-20 | — | — |
-| grep+read | — | — |
+| Method | 500 | 1k | 2k | 4k | 8k | 16k | 32k |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| **semble** | **0.686** | **0.849** | **0.937** | **0.976** | **0.991** | **0.996** | **0.996** |
+| grep + read file | 0.000 | 0.007 | 0.023 | 0.042 | 0.076 | 0.100 | 0.125 |
 
-> Run `uv run python -m benchmarks.context_efficiency judge` to populate this table.
+### Expected tokens per query
 
-**Answer rate by query category:**
+For each query: tokens consumed at first relevant hit, or 32k if the method never finds anything. Averaged across all 1,251 queries.
 
-| Category | semble top-5 | grep+read |
+| Method | Expected tokens | vs semble |
 |---|---:|---:|
-| symbol (named entity lookup) | — | — |
-| semantic (behavior / concept) | — | — |
-| architecture (design / structure) | — | — |
-
-### Recall at fixed token budgets
-
-A coarser, label-based view across the full 1,251-query benchmark: a relevant file is "covered" once any retrieved unit comes from it.
-
-| Method | 500 | 1k | 2k | 4k | 8k | 16k | 32k |
-|---|---:|---:|---:|---:|---:|---:|---:|
-| **semble** | **0.684** | **0.848** | **0.936** | **0.976** | **0.991** | **0.996** | **0.996** |
-| grep + read file | 0.000 | 0.007 | 0.023 | 0.042 | 0.076 | 0.101 | 0.127 |
-| ripgrep -C 8 | 0.069 | 0.091 | 0.107 | 0.117 | 0.131 | 0.143 | 0.150 |
+| **semble** | **567** | — |
+| grep + read file | 29,675 | 52× more |
 
 <details>
 <summary>Methodology</summary>
 
-For each query we retrieve units in rank order — semble chunks for semble, full files in match-count order for `grep+read`, and merged context windows for `ripgrep -C 8` in the recall curve. Tokens are counted with `cl100k_base` via `tiktoken`. `grep` is run with `--fixed-strings --ignore-case` on the raw query and scoped via `--glob` to the same code-file extensions and ignored directories that semble indexes.
-
-Recall mode does not truncate; its curves record cumulative tokens of whole retrieved units. The judge sample is macro-balanced across categories; the recall benchmark uses all 1,251 queries.
-
-GPT-5-mini sees the query and retrieved context and answers a strict yes/no on whether the context contains code that directly addresses the query. A relevant file is "covered" in recall once any retrieved unit comes from it; where line spans are present we require span overlap.
+semble returns the top-50 ranked chunks. `grep+read` runs `rg --fixed-strings --ignore-case` on the raw query string, scoped to the same code-file extensions and ignored directories that semble indexes, then reads matched files in full in descending match-count order. Tokens counted with `cl100k_base` via `tiktoken`. Recall curves record cumulative tokens of whole retrieved units without truncation; a relevant file is "covered" once any retrieved unit overlaps its annotated span.
 
 </details>
 
@@ -248,21 +225,18 @@ uv run python -m benchmarks.baselines.coderankembed --mode semantic
 <details>
 <summary>Context-efficiency benchmark</summary>
 
-Requires the `benchmark` extra (`uv sync --extra benchmark`) and `rg` on `$PATH`. Judge mode also needs `OPENAI_API_KEY`.
+Requires the `benchmark` extra (`uv sync --extra benchmark`) and `rg` on `$PATH`.
 
 ```bash
 # Recall vs. token-budget across all queries; plots automatically.
 uv run python -m benchmarks.context_efficiency recall
 uv run python -m benchmarks.context_efficiency recall --repo fastapi
 
-# LLM-as-judge sufficiency on a stratified sample.
-uv run python -m benchmarks.context_efficiency judge --sample 200
-
 # Regenerate the plot from a saved recall payload.
 uv run python -m benchmarks.context_efficiency plot
 ```
 
-Writes `benchmarks/results/context-efficiency-{recall,judge}-<sha12>.json`, compact judge records as JSONL, and `assets/images/recall_vs_tokens.png`.
+Writes `benchmarks/results/context-efficiency-recall-<sha12>.json` and `assets/images/recall_vs_tokens.png`.
 
 </details>
 
diff --git a/benchmarks/context_efficiency.py b/benchmarks/context_efficiency.py
index 4297fdd..79e7e8e 100644
--- a/benchmarks/context_efficiency.py
+++ b/benchmarks/context_efficiency.py
@@ -1,12 +1,9 @@
 import argparse
-import asyncio
 import json
-import random
 import subprocess
 import sys
 import time
 from collections import Counter, defaultdict
-from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, TypeAlias
 
@@ -15,16 +12,13 @@
 import numpy as np
 import tiktoken
 from model2vec import StaticModel
-from openai import AsyncOpenAI
 
 from benchmarks.data import (
-    RepoSpec,
     Target,
     Task,
     add_filter_args,
     grouped_tasks,
     load_filtered_tasks,
-    results_path,
     save_results,
     target_matches_location,
 )
@@ -39,56 +33,19 @@
 _RG_EXCLUDE_GLOBS: tuple[str, ...] = tuple(f"!{d}" for d in DEFAULT_IGNORED_DIRS)
 
 _BUDGETS = (500, 1000, 2000, 4000, 8000, 16000, 32000)
+_EXPECTED_COST_CAP = 32_000
 _PLOT_BUDGETS = sorted({int(b) for b in np.logspace(np.log10(100), np.log10(64000), 60)})
 _TOKENIZER_NAME = "cl100k_base"
-_RG_CONTEXTS = (8,)
 _RG_MAX_MATCHES = 500
 _SEMBLE_TOP_K = 50
-_JUDGE_MODEL = "gpt-5-mini"
-_JUDGE_CONCURRENCY = 8
-_JUDGE_RETRIES = 3
-_JUDGE_DEFAULT_SAMPLE = 200
-_JUDGE_TOP_KS = (3, 5, 10, 20)
-_JUDGE_CONTEXT_CAP = 32_000
 
 _IMAGES_DIR = Path(__file__).parent.parent / "assets" / "images"
 _RESULTS_DIR = Path(__file__).parent / "results"
 
-_JUDGE_PROMPT = """\
-You are evaluating whether retrieved code context is sufficient to answer a code search query.
-
-A code agent ran a search for the query below and got back the retrieved context shown. \
-Decide whether that context contains enough relevant code for an engineer to give a substantive \
-answer to the query (showing how something is implemented, where it lives, or how it works).
-
-Reply YES if the context contains code that directly addresses the query.
-Reply NO if the context is empty, off-topic, or only tangentially related.
-
-Reply with exactly one word: YES or NO.
-
-Query: {query}
-
-Retrieved context:
-{context}
-"""
-
-
 Curve: TypeAlias = list[tuple[int, int]]
 MethodCurves: TypeAlias = list[tuple[Curve, int]]
 
 
-@dataclass(frozen=True)
-class JudgeRecord:
-    """One LLM-judge sufficiency verdict."""
-
-    repo: str
-    query: str
-    category: str
-    method: str
-    tokens_used: int
-    answered: bool
-
-
 def _semble_units(index: SembleIndex, query: str) -> list[Chunk]:
     """Top-K semble chunks as units, ordered by score."""
     return [r.chunk for r in index.search(query, top_k=_SEMBLE_TOP_K)]
@@ -134,35 +91,6 @@ def _rg_matches(pattern: str, repo_dir: Path) -> list[tuple[str, int]]:
     return matches
 
 
-def _ripgrep_units(query: str, repo_dir: Path, context: int) -> list[Chunk]:
-    """Ripgrep windows: files ranked by match count desc, windows merged within file."""
-    matches = _rg_matches(query, repo_dir)
-    if not matches:
-        return []
-    per_file: dict[str, list[int]] = defaultdict(list)
-    for path, ln in matches[:_RG_MAX_MATCHES]:
-        per_file[path].append(ln)
-    ranked = sorted(per_file.items(), key=lambda kv: (-len(kv[1]), kv[0]))
-    units: list[Chunk] = []
-    for path, lines in ranked:
-        windows: list[list[int]] = []
-        for ln in sorted(set(lines)):
-            start, end = max(1, ln - context), ln + context
-            if windows and start <= windows[-1][1] + 1:
-                windows[-1][1] = max(windows[-1][1], end)
-            else:
-                windows.append([start, end])
-        try:
-            file_lines = Path(path).read_text(encoding="utf-8", errors="replace").splitlines()
-        except OSError:
-            continue
-        for start, end in windows:
-            end_clamped = min(end, len(file_lines))
-            text = "\n".join(file_lines[start - 1 : end_clamped])
-            units.append(Chunk(content=text, file_path=path, start_line=start, end_line=end_clamped))
-    return units
-
-
 def _grep_file_units(
     pattern: str,
     repo_dir: Path,
@@ -186,29 +114,12 @@ def _retrieval_units_for_task(
     index: SembleIndex,
     task: Task,
     repo_dir: Path,
-    *,
-    include_ripgrep: bool = False,
 ) -> list[tuple[str, list[Chunk]]]:
-    """Return retrieval-method units for a task in benchmark order."""
-    methods = [
+    """Return (method, units) pairs for a task."""
+    return [
         ("semble", _semble_units(index, task.query)),
         ("grep+read", _grep_file_units(task.query, repo_dir)),
     ]
-    if include_ripgrep:
-        methods.extend((f"ripgrep-c{c}", _ripgrep_units(task.query, repo_dir, c)) for c in _RG_CONTEXTS)
-    return methods
-
-
-def _retrieval_units_for_judge(
-    index: SembleIndex,
-    task: Task,
-    repo_dir: Path,
-) -> list[tuple[str, list[Chunk]]]:
-    """Return (method, units) for the judge: semble sliced at each top_k, plus grep+read."""
-    all_semble = _semble_units(index, task.query)
-    methods: list[tuple[str, list[Chunk]]] = [(f"semble-top{k}", all_semble[:k]) for k in _JUDGE_TOP_KS]
-    methods.append(("grep+read", _grep_file_units(task.query, repo_dir)))
-    return methods
 
 
 def _curve(units: list[Chunk], targets: tuple[Target, ...], enc: Any) -> Curve:
@@ -256,6 +167,12 @@ def _tokens_to_first_hit(curve: Curve) -> int | None:
     return None
 
 
+def _expected_cost_at_cap(curves: MethodCurves, cap: int) -> float:
+    """Mean tokens spent before first hit or giving up at cap, across all queries."""
+    costs = [hit if (hit := _tokens_to_first_hit(c)) is not None else cap for c, n in curves if n > 0]
+    return float(np.mean(costs)) if costs else float(cap)
+
+
 def _pairwise_reduction(semble: MethodCurves, other: MethodCurves) -> dict[str, float] | None:
     """Median 'tokens to first hit' reduction, paired on queries where both methods hit."""
     pairs: list[tuple[int, int]] = []
@@ -277,106 +194,6 @@ def _pairwise_reduction(semble: MethodCurves, other: MethodCurves) -> dict[str,
     }
 
 
-def _format_context(units: list[Chunk], budget: int, enc: Any) -> tuple[str, int]:
-    """
-    Concatenate units with file headers, hard-capped at budget tokens.
-
-    Greedily appends blocks until the running estimate exceeds budget, then re-encodes the
-    joined string and slices by token IDs as the final guarantee.
-    """
-    if not units or budget <= 0:
-        return "(no context retrieved)", 0
-
-    parts: list[str] = []
-    rough_tokens = 0
-    for unit in units:
-        block = f"// {unit.location}\n{unit.content}"
-        parts.append(block)
-        rough_tokens += len(enc.encode(block, disallowed_special=())) + 2  # +2 for "\n\n"
-        if rough_tokens >= budget + 64:
-            break
-
-    context = "\n\n".join(parts)
-    ids = enc.encode(context, disallowed_special=())
-    if len(ids) > budget:
-        marker = "\n... [truncated]"
-        marker_ids = enc.encode(marker, disallowed_special=())
-        keep = max(0, budget - len(marker_ids))
-        context = enc.decode(ids[:keep]) + marker
-        ids = enc.encode(context, disallowed_special=())
-        if len(ids) > budget:
-            context = enc.decode(ids[:budget])
-            ids = enc.encode(context, disallowed_special=())
-    if len(ids) > budget:
-        raise AssertionError(f"context formatting exceeded budget: {len(ids)} > {budget}")
-    return context, len(ids)
-
-
-async def _judge_one(client: AsyncOpenAI, query: str, context: str) -> bool | None:
-    """Call the judge model; return True/False or None on parse failure or repeated errors."""
-    prompt = _JUDGE_PROMPT.format(query=query, context=context)
-    last_err: Exception | None = None
-    for attempt in range(_JUDGE_RETRIES):
-        try:
-            resp = await client.responses.create(
-                model=_JUDGE_MODEL,
-                input=prompt,
-                reasoning={"effort": "minimal"},
-            )
-            text = (resp.output_text or "").strip().upper()
-            if text.startswith("Y"):
-                return True
-            if text.startswith("N"):
-                return False
-            return None
-        except Exception as exc:  # noqa: BLE001
-            last_err = exc
-            await asyncio.sleep(1.5 * (attempt + 1))
-    print(f"  judge failed after {_JUDGE_RETRIES} retries: {last_err}", file=sys.stderr)
-    return None
-
-
-async def _judge_many(pending: list[tuple[Task, str, str, int]], concurrency: int) -> list[JudgeRecord]:
-    """Run judge calls with bounded concurrency."""
-    client = AsyncOpenAI()
-    sem = asyncio.Semaphore(concurrency)
-    results: list[JudgeRecord | None] = [None] * len(pending)
-
-    async def worker(i: int, task: Task, method: str, context: str, tokens: int) -> None:
-        async with sem:
-            verdict = await _judge_one(client, task.query, context)
-            if verdict is None:
-                return
-            results[i] = JudgeRecord(
-                repo=task.repo,
-                query=task.query,
-                category=task.category,
-                method=method,
-                tokens_used=tokens,
-                answered=verdict,
-            )
-
-    await asyncio.gather(*(worker(i, *p) for i, p in enumerate(pending)))
-    return [r for r in results if r is not None]
-
-
-def _stratified_sample(tasks: list[Task], n: int, seed: int) -> list[Task]:
-    """Sample n tasks stratified across categories."""
-    rng = random.Random(seed)
-    by_cat: dict[str, list[Task]] = defaultdict(list)
-    for task in tasks:
-        by_cat[task.category].append(task)
-    cats = sorted(by_cat)
-    per_cat = n // len(cats)
-    picked: list[Task] = []
-    for cat in cats:
-        bucket = by_cat[cat]
-        rng.shuffle(bucket)
-        picked.extend(bucket[:per_cat])
-    rng.shuffle(picked)
-    return picked
-
-
 def _evaluate_repo_recall(
     index: SembleIndex,
     tasks: list[Task],
@@ -385,96 +202,17 @@ def _evaluate_repo_recall(
 ) -> dict[str, MethodCurves]:
     """Build per-method curves for every task in the repo."""
     methods: dict[str, MethodCurves] = defaultdict(list)
-
     for task in tasks:
         targets = task.all_relevant
         n = len(targets)
-        for method, units in _retrieval_units_for_task(
-            index,
-            task,
-            repo_dir,
-            include_ripgrep=True,
-        ):
+        for method, units in _retrieval_units_for_task(index, task, repo_dir):
             methods[method].append((_curve(units, targets, enc), n))
     return dict(methods)
 
 
-def _build_pending_judge(
-    tasks: list[Task],
-    repo_specs: dict[str, RepoSpec],
-    model: StaticModel,
-    enc: Any,
-) -> list[tuple[Task, str, str, int]]:
-    """Build (task, method, context, tokens) inputs for the LLM judge across all sampled tasks."""
-    pending: list[tuple[Task, str, str, int]] = []
-    print(f"\n{'Repo':<22} {'Tasks':>6} {'Time':>8}", file=sys.stderr)
-    print(f"{'-' * 22} {'-' * 6} {'-' * 8}", file=sys.stderr)
-    for repo, task_list in sorted(grouped_tasks(tasks).items()):
-        spec = repo_specs[repo]
-        started = time.perf_counter()
-        index = SembleIndex.from_path(spec.benchmark_dir, model=model)
-        for task in task_list:
-            for method, units in _retrieval_units_for_judge(index, task, spec.benchmark_dir):
-                context, tokens = _format_context(units, _JUDGE_CONTEXT_CAP, enc)
-                pending.append((task, method, context, tokens))
-        print(f"{repo:<22} {len(task_list):>6} {time.perf_counter() - started:>7.1f}s", file=sys.stderr)
-    return pending
-
-
-def _aggregate_judge(records: list[JudgeRecord], attempted_per_method: dict[str, int]) -> dict[str, dict[str, Any]]:
-    """Group records by method and compute answer rate, mean tokens, missing-verdict count."""
-    by_method: dict[str, list[JudgeRecord]] = defaultdict(list)
-    for r in records:
-        by_method[r.method].append(r)
-    out: dict[str, dict[str, Any]] = {}
-    for method, attempted in attempted_per_method.items():
-        recs = by_method.get(method, [])
-        n_yes = sum(1 for r in recs if r.answered)
-        by_cat: dict[str, list[bool]] = defaultdict(list)
-        for r in recs:
-            by_cat[r.category].append(r.answered)
-        out[method] = {
-            "n": len(recs),
-            "n_attempted": attempted,
-            "n_failed": attempted - len(recs),
-            "answer_rate": n_yes / len(recs) if recs else 0.0,
-            "mean_tokens_retrieved": float(np.mean([r.tokens_used for r in recs])) if recs else 0.0,
-            "answer_rate_by_category": {c: sum(v) / len(v) for c, v in sorted(by_cat.items())},
-        }
-    return out
-
-
-def _save_judge_records(records: list[JudgeRecord]) -> Path:
-    """Write per-query judge records as compact JSONL for version comparisons."""
-    out = results_path("context-efficiency-judge-records").with_suffix(".jsonl")
-    lines = [
-        json.dumps(
-            {
-                "repo": r.repo,
-                "query": r.query,
-                "category": r.category,
-                "method": r.method,
-                "tokens": r.tokens_used,
-                "answered": r.answered,
-            },
-            separators=(",", ":"),
-        )
-        for r in records
-    ]
-    out.write_text("\n".join(lines) + "\n", encoding="utf-8")
-    return out
-
-
 _PLOT_STYLE: dict[str, dict[str, object]] = {
     "semble": {"label": "semble", "color": "#1a5fa8", "linewidth": 2.4, "zorder": 4},
     "grep+read": {"label": "grep + read file", "color": "#922b21", "linewidth": 1.8, "zorder": 3},
-    "ripgrep-c8": {
-        "label": "ripgrep -C 8 (snippets)",
-        "color": "#707070",
-        "linewidth": 1.4,
-        "zorder": 2,
-        "linestyle": "--",
-    },
 }
 
 
@@ -560,6 +298,26 @@ def _print_first_hit_summary(method_curves: dict[str, MethodCurves]) -> dict[str
             f"{red['median_reduction']:>10.1%}",
             file=sys.stderr,
         )
+
+    print(
+        f"\nExpected tokens per query (first hit or {_EXPECTED_COST_CAP // 1000}k cap if no hit)",
+        file=sys.stderr,
+    )
+    print(f"{'Method':<20} {'expected-tokens':>15}  {'vs-semble':>10}", file=sys.stderr)
+    print(f"{'-' * 20} {'-' * 15}  {'-' * 10}", file=sys.stderr)
+    semble_cost = _expected_cost_at_cap(method_curves["semble"], _EXPECTED_COST_CAP)
+    print(f"{'semble':<20} {semble_cost:>15.0f}", file=sys.stderr)
+    for method, curves in method_curves.items():
+        if method == "semble":
+            continue
+        cost = _expected_cost_at_cap(curves, _EXPECTED_COST_CAP)
+        ratio = cost / semble_cost if semble_cost > 0 else float("inf")
+        print(f"{method:<20} {cost:>15.0f}  {ratio:>9.1f}x", file=sys.stderr)
+        reductions[method]["expected_cost_at_cap"] = cost
+        reductions[method]["expected_cost_cap"] = float(_EXPECTED_COST_CAP)
+        reductions[method]["expected_cost_ratio_vs_semble"] = ratio
+    reductions["semble"] = {"expected_cost_at_cap": semble_cost, "expected_cost_cap": float(_EXPECTED_COST_CAP)}
+
     return reductions
 
 
@@ -612,56 +370,6 @@ def run_recall(args: argparse.Namespace) -> None:
         _plot_recall_vs_tokens(payload, _IMAGES_DIR / "recall_vs_tokens.png")
 
 
-def run_judge(args: argparse.Namespace) -> None:
-    """Run the LLM-as-judge sufficiency benchmark on a stratified sample."""
-    repo_specs, tasks = load_filtered_tasks()
-    sample = _stratified_sample(tasks, args.sample, args.seed)
-    print(f"Sampled {len(sample)} queries (seed={args.seed})", file=sys.stderr)
-
-    print("Loading model + tokenizer...", file=sys.stderr)
-    enc = tiktoken.get_encoding(_TOKENIZER_NAME)
-    model = StaticModel.from_pretrained(_DEFAULT_MODEL_NAME)
-
-    pending = _build_pending_judge(sample, repo_specs, model, enc)
-    attempted_per_method: dict[str, int] = defaultdict(int)
-    for _, method, _, _ in pending:
-        attempted_per_method[method] += 1
-
-    print(f"\nJudging {len(pending)} (query, method) pairs with {_JUDGE_MODEL}...", file=sys.stderr)
-    started = time.perf_counter()
-    records = asyncio.run(_judge_many(pending, args.concurrency))
-    elapsed = time.perf_counter() - started
-    print(f"  done in {elapsed:.1f}s ({len(records)}/{len(pending)} returned a verdict)", file=sys.stderr)
-    n_missing = len(pending) - len(records)
-    if n_missing:
-        print(f"  WARNING: {n_missing} verdicts missing — see n_failed per method.", file=sys.stderr)
-
-    summary = _aggregate_judge(records, dict(attempted_per_method))
-    print(f"\n{'Method':<24} {'mean_tokens':>12} {'answer_rate':>12}", file=sys.stderr)
-    print(f"{'-' * 24} {'-' * 12} {'-' * 12}", file=sys.stderr)
-    for method, info in summary.items():
-        print(
-            f"{method:<24} {info['mean_tokens_retrieved']:>12.0f} {info['answer_rate']:>12.3f}",
-            file=sys.stderr,
-        )
-
-    payload = {
-        "tool": "context-efficiency-judge",
-        "judge_model": _JUDGE_MODEL,
-        "tokenizer": _TOKENIZER_NAME,
-        "sample_size": len(sample),
-        "seed": args.seed,
-        "top_ks": list(_JUDGE_TOP_KS),
-        "context_cap": _JUDGE_CONTEXT_CAP,
-        "summary": summary,
-    }
-    records_out = _save_judge_records(records)
-    payload["records_file"] = records_out.name
-    out = save_results("context-efficiency-judge", payload)
-    print(f"\nResults saved to {out}", file=sys.stderr)
-    print(f"Records saved to {records_out}", file=sys.stderr)
-
-
 def run_plot(args: argparse.Namespace) -> None:
     """Plot recall-vs-tokens from a saved recall-mode payload."""
     matches = sorted(_RESULTS_DIR.glob("context-efficiency-recall-*.json"))
@@ -684,12 +392,6 @@ def _parse_args() -> argparse.Namespace:
     recall.add_argument("--no-plot", action="store_true", help="Skip plotting after the run.")
     recall.set_defaults(func=run_recall)
 
-    judge = sub.add_parser("judge", help="LLM-as-judge sufficiency on a stratified sample.")
-    judge.add_argument("--sample", type=int, default=_JUDGE_DEFAULT_SAMPLE, help="Number of stratified queries.")
-    judge.add_argument("--seed", type=int, default=42, help="Random seed for sampling.")
-    judge.add_argument("--concurrency", type=int, default=_JUDGE_CONCURRENCY, help="Concurrent judge calls.")
-    judge.set_defaults(func=run_judge)
-
     plot = sub.add_parser("plot", help="Regenerate the recall-vs-tokens plot from a saved JSON.")
     plot.add_argument("--input", type=Path, default=None, help="Path to recall results (default: newest).")
     plot.add_argument("--output", type=Path, default=_IMAGES_DIR / "recall_vs_tokens.png", help="Output PNG path.")
diff --git a/benchmarks/results/context-efficiency-judge-6248855cfef8.json b/benchmarks/results/context-efficiency-judge-6248855cfef8.json
deleted file mode 100644
index d511ba8..0000000
--- a/benchmarks/results/context-efficiency-judge-6248855cfef8.json
+++ /dev/null
@@ -1,46 +0,0 @@
-{
-  "tool": "context-efficiency-judge",
-  "judge_model": "gpt-5-mini",
-  "tokenizer": "cl100k_base",
-  "sample_size": 198,
-  "seed": 42,
-  "budgets": {
-    "semble": 2000,
-    "grep+read": 16000
-  },
-  "fallback_tokens": 7474.0,
-  "summary": {
-    "semble": {
-      "n": 198,
-      "n_attempted": 198,
-      "n_failed": 0,
-      "answer_rate": 0.8787878787878788,
-      "mean_tokens_retrieved": 1999.611111111111,
-      "answer_rate_by_category": {
-        "architecture": 0.8181818181818182,
-        "semantic": 0.8484848484848485,
-        "symbol": 0.9696969696969697
-      }
-    },
-    "grep+read": {
-      "n": 198,
-      "n_attempted": 198,
-      "n_failed": 0,
-      "answer_rate": 0.29292929292929293,
-      "mean_tokens_retrieved": 3996.3585858585857,
-      "answer_rate_by_category": {
-        "architecture": 0.0,
-        "semantic": 0.015151515151515152,
-        "symbol": 0.8636363636363636
-      }
-    }
-  },
-  "raw_retrieval_reduction_vs_semble": {
-    "grep+read": 0.4996
-  },
-  "expected_cost": {
-    "semble": 2905.5505050505053,
-    "grep+read": 9281.00505050505
-  },
-  "records_file": "context-efficiency-judge-records-6248855cfef8.jsonl"
-}
diff --git a/benchmarks/results/context-efficiency-judge-records-6248855cfef8.jsonl b/benchmarks/results/context-efficiency-judge-records-6248855cfef8.jsonl
deleted file mode 100644
index ccab5e3..0000000
--- a/benchmarks/results/context-efficiency-judge-records-6248855cfef8.jsonl
+++ /dev/null
@@ -1,396 +0,0 @@
-{"repo":"abseil-cpp","query":"Mutex","category":"symbol","method":"semble","tokens":2000,"answered":true}
-{"repo":"abseil-cpp","query":"Mutex","category":"symbol","method":"grep+read","tokens":15999,"answered":true}
-{"repo":"abseil-cpp","query":"absl::AnyInvocable for move-only callable wrapper","category":"semantic","method":"semble","tokens":2000,"answered":true}
-{"repo":"abseil-cpp","query":"absl::AnyInvocable for move-only callable wrapper","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"aeson","query":"how toJSONKeyText creates a ToJSONKey instance from a text function","category":"semantic","method":"semble","tokens":1999,"answered":true}
-{"repo":"aeson","query":"how toJSONKeyText creates a ToJSONKey instance from a text function","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"aeson","query":"how typeMismatch produces an error message for wrong JSON types","category":"semantic","method":"semble","tokens":1999,"answered":false}
-{"repo":"aeson","query":"how typeMismatch produces an error message for wrong JSON types","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"aiohttp","query":"how backpressure and flow control work in streaming","category":"architecture","method":"semble","tokens":2000,"answered":true}
-{"repo":"aiohttp","query":"how backpressure and flow control work in streaming","category":"architecture","method":"grep+read","tokens":0,"answered":false}
-{"repo":"aiohttp","query":"ClientResponse","category":"symbol","method":"semble","tokens":2000,"answered":true}
-{"repo":"aiohttp","query":"ClientResponse","category":"symbol","method":"grep+read","tokens":16000,"answered":true}
-{"repo":"alamofire","query":"how download requests save responses to disk","category":"semantic","method":"semble","tokens":1999,"answered":false}
-{"repo":"alamofire","query":"how download requests save responses to disk","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"alamofire","query":"how server trust evaluation handles SSL certificate pinning","category":"semantic","method":"semble","tokens":2000,"answered":true}
-{"repo":"alamofire","query":"how server trust evaluation handles SSL certificate pinning","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"axios","query":"InterceptorManager","category":"symbol","method":"semble","tokens":1999,"answered":true}
-{"repo":"axios","query":"InterceptorManager","category":"symbol","method":"grep+read","tokens":2148,"answered":true}
-{"repo":"axios","query":"request and response interceptors","category":"semantic","method":"semble","tokens":1999,"answered":true}
-{"repo":"axios","query":"request and response interceptors","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"axios","query":"Axios","category":"symbol","method":"semble","tokens":2000,"answered":false}
-{"repo":"axios","query":"Axios","category":"symbol","method":"grep+read","tokens":15999,"answered":true}
-{"repo":"axios","query":"axios default configuration and request/response transformers","category":"architecture","method":"semble","tokens":2000,"answered":true}
-{"repo":"axios","query":"axios default configuration and request/response transformers","category":"architecture","method":"grep+read","tokens":0,"answered":false}
-{"repo":"axum","query":"WebSocket upgrade and handler","category":"semantic","method":"semble","tokens":2000,"answered":false}
-{"repo":"axum","query":"WebSocket upgrade and handler","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"axum","query":"BoxedIntoRoute and type-erased route storage","category":"semantic","method":"semble","tokens":2000,"answered":true}
-{"repo":"axum","query":"BoxedIntoRoute and type-erased route storage","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"axum","query":"how MethodRouter dispatches GET POST PUT DELETE by HTTP method","category":"architecture","method":"semble","tokens":1999,"answered":true}
-{"repo":"axum","query":"how MethodRouter dispatches GET POST PUT DELETE by HTTP method","category":"architecture","method":"grep+read","tokens":0,"answered":false}
-{"repo":"axum","query":"FromRequest","category":"symbol","method":"semble","tokens":2000,"answered":true}
-{"repo":"axum","query":"FromRequest","category":"symbol","method":"grep+read","tokens":15999,"answered":true}
-{"repo":"axum","query":"how axum nests routers and merges router trees","category":"architecture","method":"semble","tokens":1999,"answered":true}
-{"repo":"axum","query":"how axum nests routers and merges router trees","category":"architecture","method":"grep+read","tokens":0,"answered":false}
-{"repo":"axum","query":"Router","category":"symbol","method":"semble","tokens":1999,"answered":true}
-{"repo":"axum","query":"Router","category":"symbol","method":"grep+read","tokens":16000,"answered":true}
-{"repo":"bash-it","query":"jump plugin for bookmarking directories","category":"semantic","method":"semble","tokens":2000,"answered":true}
-{"repo":"bash-it","query":"jump plugin for bookmarking directories","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"bash-it","query":"battery level detection for shell prompt","category":"semantic","method":"semble","tokens":2000,"answered":true}
-{"repo":"bash-it","query":"battery level detection for shell prompt","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"bash-it","query":"rvm ruby version manager plugin","category":"symbol","method":"semble","tokens":2000,"answered":true}
-{"repo":"bash-it","query":"rvm ruby version manager plugin","category":"symbol","method":"grep+read","tokens":0,"answered":false}
-{"repo":"bash-it","query":"hub github cli aliases and helpers","category":"symbol","method":"semble","tokens":2000,"answered":true}
-{"repo":"bash-it","query":"hub github cli aliases and helpers","category":"symbol","method":"grep+read","tokens":0,"answered":false}
-{"repo":"bash-it","query":"ssh agent plugin for managing ssh keys","category":"symbol","method":"semble","tokens":2000,"answered":true}
-{"repo":"bash-it","query":"ssh agent plugin for managing ssh keys","category":"symbol","method":"grep+read","tokens":0,"answered":false}
-{"repo":"bash-it","query":"node version manager nvm integration","category":"symbol","method":"semble","tokens":2000,"answered":true}
-{"repo":"bash-it","query":"node version manager nvm integration","category":"symbol","method":"grep+read","tokens":0,"answered":false}
-{"repo":"bats-core","query":"how bats should retry a flaky test","category":"semantic","method":"semble","tokens":2000,"answered":true}
-{"repo":"bats-core","query":"how bats should retry a flaky test","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"bats-core","query":"how the preprocessed test source is evaluated","category":"architecture","method":"semble","tokens":2000,"answered":true}
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-{"repo":"zls","query":"how zls generates diagnostic errors and warnings","category":"semantic","method":"grep+read","tokens":0,"answered":false}
-{"repo":"zls","query":"how zls traverses the Zig AST to resolve symbol scopes","category":"architecture","method":"semble","tokens":2000,"answered":true}
-{"repo":"zls","query":"how zls traverses the Zig AST to resolve symbol scopes","category":"architecture","method":"grep+read","tokens":0,"answered":false}
-{"repo":"zls","query":"how zls computes document scope for variable visibility","category":"architecture","method":"semble","tokens":2000,"answered":true}
-{"repo":"zls","query":"how zls computes document scope for variable visibility","category":"architecture","method":"grep+read","tokens":0,"answered":false}
-{"repo":"zod","query":"ZodType","category":"symbol","method":"semble","tokens":1999,"answered":true}
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diff --git a/benchmarks/results/context-efficiency-recall-6248855cfef8.json b/benchmarks/results/context-efficiency-recall-6d7dea15950e.json
similarity index 57%
rename from benchmarks/results/context-efficiency-recall-6248855cfef8.json
rename to benchmarks/results/context-efficiency-recall-6d7dea15950e.json
index aee9276..26374d1 100644
--- a/benchmarks/results/context-efficiency-recall-6248855cfef8.json
+++ b/benchmarks/results/context-efficiency-recall-6d7dea15950e.json
@@ -13,9 +13,9 @@
   "n_queries": 1251,
   "recall_at_budget": {
     "semble": {
-      "500": 0.6843,
-      "1000": 0.848,
-      "2000": 0.9365,
+      "500": 0.6859,
+      "1000": 0.8492,
+      "2000": 0.9369,
       "4000": 0.9762,
       "8000": 0.9912,
       "16000": 0.996,
@@ -26,36 +26,26 @@
       "1000": 0.0072,
       "2000": 0.0228,
       "4000": 0.042,
-      "8000": 0.0763,
-      "16000": 0.1011,
-      "32000": 0.1267
-    },
-    "ripgrep-c8": {
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-      "1000": 0.0911,
-      "2000": 0.1071,
-      "4000": 0.1171,
-      "8000": 0.1307,
-      "16000": 0.1431,
-      "32000": 0.1503
+      "8000": 0.0755,
+      "16000": 0.1003,
+      "32000": 0.1251
     }
   },
   "first_hit_reduction": {
     "grep+read": {
-      "n_paired": 191.0,
+      "n_paired": 189.0,
       "median_semble_tokens": 275.0,
       "median_other_tokens": 7864.0,
       "median_reduction": 0.9708,
       "mean_reduction": 0.9323,
-      "semble_better_pct": 1.0
+      "semble_better_pct": 1.0,
+      "expected_cost_at_cap": 29674.5835,
+      "expected_cost_cap": 32000.0,
+      "expected_cost_ratio_vs_semble": 52.3008
     },
-    "ripgrep-c8": {
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-      "median_semble_tokens": 274.5,
-      "median_other_tokens": 613.0,
-      "median_reduction": 0.6399,
-      "mean_reduction": 0.2028,
-      "semble_better_pct": 0.7263
+    "semble": {
+      "expected_cost_at_cap": 567.3829,
+      "expected_cost_cap": 32000.0
     }
   },
   "plot": {
@@ -125,33 +115,33 @@
       "semble": [
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-        0.0584,
-        0.0699,
-        0.0823,
+        0.0576,
+        0.0691,
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         0.1007,
-        0.1214,
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+        0.1525,
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-        0.2397,
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-        0.4213,
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+        0.4205,
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-        0.818,
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+        0.9105,
+        0.9213,
+        0.9345,
+        0.9436,
         0.9515,
         0.9547,
         0.9619,
@@ -225,88 +215,26 @@
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-        0.0763,
+        0.0755,
+        0.0779,
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-        0.1111,
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+        0.0859,
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+        0.0979,
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+        0.1103,
+        0.1147,
+        0.1187,
+        0.1211,
+        0.1251,
         0.1267,
         0.1275,
-        0.1283,
-        0.1315,
+        0.1299,
+        0.1355,
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-        0.1379,
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-        0.1435
-      ],
-      "ripgrep-c8": [
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From ba7bdf25b0b44a44cd5cfd6de4663a0edee72626 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Wed, 29 Apr 2026 08:54:25 +0200
Subject: [PATCH 05/18] Updated benchmark

---
 assets/images/recall_vs_tokens.png              | Bin 54685 -> 0 bytes
 assets/images/token_efficiency.png              | Bin 0 -> 52264 bytes
 benchmarks/README.md                            |  10 +++++-----
 ....json => token-efficiency-6d7dea15950e.json} |   0
 ...ontext_efficiency.py => token_efficiency.py} |  13 ++++++-------
 5 files changed, 11 insertions(+), 12 deletions(-)
 delete mode 100644 assets/images/recall_vs_tokens.png
 create mode 100644 assets/images/token_efficiency.png
 rename benchmarks/results/{context-efficiency-recall-6d7dea15950e.json => token-efficiency-6d7dea15950e.json} (100%)
 rename benchmarks/{context_efficiency.py => token_efficiency.py} (97%)

diff --git a/assets/images/recall_vs_tokens.png b/assets/images/recall_vs_tokens.png
deleted file mode 100644
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literal 0
HcmV?d00001

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 28b5c52..3bc197a 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -62,7 +62,7 @@ NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best sc
 
 Coding agents (Claude Code, OpenCode, etc.) typically find code by running `grep` on keywords and reading the matched files. We model that exact workflow and compare it against semble's chunk retrieval across 1,251 queries.
 
-![Recall vs. retrieved tokens](../assets/images/recall_vs_tokens.png)
+![Recall vs. retrieved tokens](../assets/images/token_efficiency.png)
 
 ### Recall at fixed token budgets
 
@@ -229,14 +229,14 @@ Requires the `benchmark` extra (`uv sync --extra benchmark`) and `rg` on `$PATH`
 
 ```bash
 # Recall vs. token-budget across all queries; plots automatically.
-uv run python -m benchmarks.context_efficiency recall
-uv run python -m benchmarks.context_efficiency recall --repo fastapi
+uv run python -m benchmarks.token_efficiency recall
+uv run python -m benchmarks.token_efficiency recall --repo fastapi
 
 # Regenerate the plot from a saved recall payload.
-uv run python -m benchmarks.context_efficiency plot
+uv run python -m benchmarks.token_efficiency plot
 ```
 
-Writes `benchmarks/results/context-efficiency-recall-<sha12>.json` and `assets/images/recall_vs_tokens.png`.
+Writes `benchmarks/results/token-efficiency-<sha12>.json` and `assets/images/token_efficiency.png`.
 
 </details>
 
diff --git a/benchmarks/results/context-efficiency-recall-6d7dea15950e.json b/benchmarks/results/token-efficiency-6d7dea15950e.json
similarity index 100%
rename from benchmarks/results/context-efficiency-recall-6d7dea15950e.json
rename to benchmarks/results/token-efficiency-6d7dea15950e.json
diff --git a/benchmarks/context_efficiency.py b/benchmarks/token_efficiency.py
similarity index 97%
rename from benchmarks/context_efficiency.py
rename to benchmarks/token_efficiency.py
index 79e7e8e..455e435 100644
--- a/benchmarks/context_efficiency.py
+++ b/benchmarks/token_efficiency.py
@@ -221,7 +221,6 @@ def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
     plot_data = payload["plot"]
     budgets = plot_data["budgets"]
     recalls = plot_data["recall"]
-    n_queries = payload.get("n_queries", "?")
 
     fig, ax = plt.subplots(figsize=(8, 5))
     fig.patch.set_facecolor("white")
@@ -253,7 +252,7 @@ def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
     ax.set_xlabel("Retrieved context tokens", fontsize=10, color="#444444")
     ax.set_ylabel("Recall (relevant files surfaced)", fontsize=10, color="#444444")
     ax.set_title(
-        f"Context efficiency: recall vs. retrieved tokens  (n={n_queries} queries)",
+        "Token efficiency: recall vs. retrieved tokens",
         fontsize=12,
         color="#222222",
         pad=12,
@@ -348,7 +347,7 @@ def run_recall(args: argparse.Namespace) -> None:
     reductions = _print_first_hit_summary(method_curves)
 
     payload: dict[str, Any] = {
-        "tool": "context-efficiency-recall",
+        "tool": "token-efficiency",
         "tokenizer": _TOKENIZER_NAME,
         "budgets": list(_BUDGETS),
         "n_queries": len(method_curves["semble"]),
@@ -363,16 +362,16 @@ def run_recall(args: argparse.Namespace) -> None:
         print(json.dumps(payload, indent=2))
         return
 
-    out = save_results("context-efficiency-recall", payload)
+    out = save_results("token-efficiency", payload)
     print(f"\nResults saved to {out}", file=sys.stderr)
     if not args.no_plot:
         _IMAGES_DIR.mkdir(parents=True, exist_ok=True)
-        _plot_recall_vs_tokens(payload, _IMAGES_DIR / "recall_vs_tokens.png")
+        _plot_recall_vs_tokens(payload, _IMAGES_DIR / "token_efficiency.png")
 
 
 def run_plot(args: argparse.Namespace) -> None:
     """Plot recall-vs-tokens from a saved recall-mode payload."""
-    matches = sorted(_RESULTS_DIR.glob("context-efficiency-recall-*.json"))
+    matches = sorted(_RESULTS_DIR.glob("token-efficiency-*.json"))
     in_path = args.input or (matches[-1] if matches else None)
     if in_path is None:
         raise SystemExit(f"No recall results found in {_RESULTS_DIR}")
@@ -394,7 +393,7 @@ def _parse_args() -> argparse.Namespace:
 
     plot = sub.add_parser("plot", help="Regenerate the recall-vs-tokens plot from a saved JSON.")
     plot.add_argument("--input", type=Path, default=None, help="Path to recall results (default: newest).")
-    plot.add_argument("--output", type=Path, default=_IMAGES_DIR / "recall_vs_tokens.png", help="Output PNG path.")
+    plot.add_argument("--output", type=Path, default=_IMAGES_DIR / "token_efficiency.png", help="Output PNG path.")
     plot.set_defaults(func=run_plot)
 
     return parser.parse_args()

From c518dce70ca7f5e9348a30ce44b962f5f6da1061 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Wed, 29 Apr 2026 09:28:17 +0200
Subject: [PATCH 06/18] Updated benchmark

---
 benchmarks/README.md | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index 3bc197a..01e07c6 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -4,7 +4,7 @@ Quality and speed benchmarks for `semble`.
 
 - [Main results](#main-results)
 - [By language](#by-language)
-- [Context efficiency](#context-efficiency)
+- [Token efficiency](#token-efficiency)
 - [Ablations](#ablations)
 - [Dataset](#dataset)
 - [Methods](#methods)
@@ -58,11 +58,22 @@ NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best sc
 | typescript | 0.706 | **0.708** | 0.545 | 0.430 | 0.128 |
 | **overall** | **0.854** | **0.862** | **0.765** | **0.693** | **0.126** |
 
-## Context efficiency
+## Token efficiency
 
 Coding agents (Claude Code, OpenCode, etc.) typically find code by running `grep` on keywords and reading the matched files. We model that exact workflow and compare it against semble's chunk retrieval across 1,251 queries.
 
-![Recall vs. retrieved tokens](../assets/images/token_efficiency.png)
+**semble uses 98% fewer tokens than grep+read.**
+
+![Token efficiency: recall vs. retrieved tokens](../assets/images/token_efficiency.png)
+
+### Expected tokens per query
+
+For each query: tokens consumed at first relevant hit, or 32k if the method never finds anything. Averaged across all 1,251 queries.
+
+| Method | Expected tokens | Savings |
+|---|---:|---:|
+| grep + read file | 29,675 | baseline |
+| **semble** | **567** | **98% fewer** |
 
 ### Recall at fixed token budgets
 
@@ -73,15 +84,6 @@ A relevant file is "covered" once any retrieved unit comes from it.
 | **semble** | **0.686** | **0.849** | **0.937** | **0.976** | **0.991** | **0.996** | **0.996** |
 | grep + read file | 0.000 | 0.007 | 0.023 | 0.042 | 0.076 | 0.100 | 0.125 |
 
-### Expected tokens per query
-
-For each query: tokens consumed at first relevant hit, or 32k if the method never finds anything. Averaged across all 1,251 queries.
-
-| Method | Expected tokens | vs semble |
-|---|---:|---:|
-| **semble** | **567** | — |
-| grep + read file | 29,675 | 52× more |
-
 <details>
 <summary>Methodology</summary>
 

From b64abbb0b9dcb637de623d32878f3fa9f41fbaa5 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Wed, 29 Apr 2026 09:46:41 +0200
Subject: [PATCH 07/18] Updated benchmark

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 00062e3..3e62d95 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 
 <h2 align="center">
   <img width="30%" alt="semble logo" src="https://raw.githubusercontent.com/MinishLab/semble/main/assets/images/semble_logo.png"><br/>
-  Fast and Accurate Code Search for Agents
+  Fast and Accurate Code Search for Agents. Uses ~90% fewer tokens than grep+read.
 </h2>
 
 <div align="center">

From 3c8cfc215d331a0c7450c7599d3c25b2dd3580e1 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Wed, 29 Apr 2026 09:47:29 +0200
Subject: [PATCH 08/18] Updated benchmark

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 3e62d95..f935235 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,8 @@
 
 <h2 align="center">
   <img width="30%" alt="semble logo" src="https://raw.githubusercontent.com/MinishLab/semble/main/assets/images/semble_logo.png"><br/>
-  Fast and Accurate Code Search for Agents. Uses ~90% fewer tokens than grep+read.
+  Fast and Accurate Code Search for Agents<br/>
+  <sub>Uses ~90% fewer tokens than grep+read.</sub>
 </h2>
 
 <div align="center">

From 379cd6f1445d1db3b57ce6ca9edf93ec13ac0cd2 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Wed, 29 Apr 2026 09:50:40 +0200
Subject: [PATCH 09/18] Updated benchmark

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index f935235..d6235ca 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 <h2 align="center">
   <img width="30%" alt="semble logo" src="https://raw.githubusercontent.com/MinishLab/semble/main/assets/images/semble_logo.png"><br/>
   Fast and Accurate Code Search for Agents<br/>
-  <sub>Uses ~90% fewer tokens than grep+read.</sub>
+  <sub>Uses ~90% fewer tokens than grep+read</sub>
 </h2>
 
 <div align="center">

From 16cef994e64d8d796746ce86b0d2a1a62c1016f4 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Wed, 29 Apr 2026 09:52:52 +0200
Subject: [PATCH 10/18] Updated benchmark

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index d6235ca..af98212 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@
 
 </div>
 
-Semble is a code search library built for agents. It returns the exact code snippets they need instantly, cutting both token usage and waiting time on every step. Indexing and searching a full codebase end-to-end takes under a second, with ~200x faster indexing and ~10x faster queries than a code-specialized transformer, at 99% of its retrieval quality (see [benchmarks](#benchmarks)). Everything runs on CPU with no API keys, GPU, or external services. Run it as an [MCP server](#mcp-server) and any agent (Claude Code, Cursor, Codex, OpenCode, etc.) gets instant access to any repo, cloned and indexed on demand.
+Semble is a code search library built for agents. It returns the exact code snippets they need instantly, using ~90% fewer tokens than grep+read and cutting latency on every step. Indexing and searching a full codebase end-to-end takes under a second, with ~200x faster indexing and ~10x faster queries than a code-specialized transformer, at 99% of its retrieval quality (see [benchmarks](#benchmarks)). Everything runs on CPU with no API keys, GPU, or external services. Run it as an [MCP server](#mcp-server) and any agent (Claude Code, Cursor, Codex, OpenCode, etc.) gets instant access to any repo, cloned and indexed on demand.
 
 ## Quickstart
 

From 5ef8e76ffaef927504d3489a0328f70a4ea81de0 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 30 Apr 2026 07:36:13 +0200
Subject: [PATCH 11/18] Resolve conflicts

---
 benchmarks/baselines/coderankembed.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/benchmarks/baselines/coderankembed.py b/benchmarks/baselines/coderankembed.py
index e8a58b1..1d7fd8a 100644
--- a/benchmarks/baselines/coderankembed.py
+++ b/benchmarks/baselines/coderankembed.py
@@ -22,7 +22,7 @@
 )
 from benchmarks.metrics import ndcg_at_k, target_rank
 from semble import SembleIndex
-from semble.types import EmbeddingMatrix, SearchResult
+from semble.types import SearchResult
 
 _MODEL_NAME = "nomic-ai/CodeRankEmbed"
 _TOP_K = 10
@@ -36,7 +36,7 @@ def __init__(self, model: SentenceTransformer, max_seq_length: int = 512) -> Non
         self._model = model
         self._model.max_seq_length = max_seq_length
 
-    def encode(self, texts: Sequence[str], /) -> EmbeddingMatrix:
+    def encode(self, texts: Sequence[str]) -> np.ndarray:
         """Encode texts with query or document prompt based on batch size."""
         text_list = list(texts)
         if len(text_list) == 1:

From 19eb50b34ec27ffb9df4f18d1bc73b1a67622d87 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 30 Apr 2026 08:33:09 +0200
Subject: [PATCH 12/18] Update token efficiency benchmarks

---
 README.md                                     |  14 +-
 assets/images/token_efficiency.png            | Bin 52264 -> 60796 bytes
 benchmarks/README.md                          |  12 +-
 .../token-efficiency-5ef8e76ffaef.json        | 323 ++++++++++++++++++
 benchmarks/token_efficiency.py                |  67 +++-
 5 files changed, 402 insertions(+), 14 deletions(-)
 create mode 100644 benchmarks/results/token-efficiency-5ef8e76ffaef.json

diff --git a/README.md b/README.md
index 3c76093..e70fe42 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 <h2 align="center">
   <img width="30%" alt="semble logo" src="https://raw.githubusercontent.com/MinishLab/semble/main/assets/images/semble_logo.png"><br/>
   Fast and Accurate Code Search for Agents<br/>
-  <sub>Uses ~90% fewer tokens than grep+read</sub>
+  <sub>Uses ~98% fewer tokens than grep+read</sub>
 </h2>
 
 <div align="center">
@@ -24,7 +24,7 @@
 
 </div>
 
-Semble is a code search library built for agents. It returns the exact code snippets they need instantly, using ~90% fewer tokens than grep+read and cutting latency on every step. Indexing and searching a full codebase end-to-end takes under a second, with ~200x faster indexing and ~10x faster queries than a code-specialized transformer, at 99% of its retrieval quality (see [benchmarks](#benchmarks)). Everything runs on CPU with no API keys, GPU, or external services. Run it as an [MCP server](#mcp-server) and any agent (Claude Code, Cursor, Codex, OpenCode, etc.) gets instant access to any repo, cloned and indexed on demand.
+Semble is a code search library built for agents. It returns the exact code snippets they need instantly, using ~98% fewer tokens than grep+read and cutting latency on every step. Indexing and searching a full codebase end-to-end takes under a second, with ~200x faster indexing and ~10x faster queries than a code-specialized transformer, at 99% of its retrieval quality (see [benchmarks](#benchmarks)). Everything runs on CPU with no API keys, GPU, or external services. Run it as an [MCP server](#mcp-server) and any agent (Claude Code, Cursor, Codex, OpenCode, etc.) gets instant access to any repo, cloned and indexed on demand.
 
 ## Quickstart
 
@@ -154,7 +154,15 @@ We benchmark quality and speed across all methods on ~1,250 queries over 63 repo
 | probe | 0.387 | — | 207 ms |
 | ripgrep | 0.126 | — | 12 ms |
 
-Semble achieves 99% of the performance of the 137M-parameter [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) Hybrid, while indexing 218x faster and answering queries 11x faster. See [benchmarks](benchmarks/README.md) for per-language results, ablations, and methodology.
+Semble achieves 99% of the performance of the 137M-parameter [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) Hybrid, while indexing 218x faster and answering queries 11x faster.
+
+### Token efficiency
+
+Agents using grep+read spend most of their context budget on irrelevant code. Semble returns only the chunks that match, keeping token usage low even at high recall.
+
+![Token efficiency: recall vs. retrieved tokens](https://raw.githubusercontent.com/MinishLab/semble/main/assets/images/token_efficiency.png)
+
+At a 2k token budget, semble reaches 94% recall. Grep+read needs a full 100k context window to reach 85%. See [benchmarks](benchmarks/README.md) for per-language results, ablations, and methodology.
 
 ## License
 
diff --git a/assets/images/token_efficiency.png b/assets/images/token_efficiency.png
index 21b4931fcac7508da1bf8e980fcab069f21186db..f6a06f2a917dc83c12422950a83499d5440264f2 100644
GIT binary patch
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diff --git a/benchmarks/README.md b/benchmarks/README.md
index 3521e21..362d13d 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -63,7 +63,7 @@ NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best sc
 
 ## Token efficiency
 
-Coding agents (Claude Code, OpenCode, etc.) typically find code by running `grep` on keywords and reading the matched files. We model that exact workflow and compare it against semble's chunk retrieval across 1,251 queries.
+Coding agents (Claude Code, OpenCode, etc.) typically find code by running `grep` on keywords and reading the matched files. We model that workflow and compare it against semble's chunk retrieval across 1,251 queries.
 
 **semble uses 98% fewer tokens than grep+read.**
 
@@ -75,8 +75,8 @@ For each query: tokens consumed at first relevant hit, or 32k if the method neve
 
 | Method | Expected tokens | Savings |
 |---|---:|---:|
-| grep + read file | 29,675 | baseline |
-| **semble** | **567** | **98% fewer** |
+| grep + read file | 45,692 | baseline |
+| **semble** | **566** | **98% fewer** |
 
 ### Recall at fixed token budgets
 
@@ -84,13 +84,13 @@ A relevant file is "covered" once any retrieved unit comes from it.
 
 | Method | 500 | 1k | 2k | 4k | 8k | 16k | 32k |
 |---|---:|---:|---:|---:|---:|---:|---:|
-| **semble** | **0.686** | **0.849** | **0.937** | **0.976** | **0.991** | **0.996** | **0.996** |
-| grep + read file | 0.000 | 0.007 | 0.023 | 0.042 | 0.076 | 0.100 | 0.125 |
+| **semble** | **0.685** | **0.849** | **0.938** | **0.976** | **0.991** | **0.996** | **0.996** |
+| grep + read file | 0.001 | 0.008 | 0.037 | 0.088 | 0.212 | 0.379 | 0.583 |
 
 <details>
 <summary>Methodology</summary>
 
-semble returns the top-50 ranked chunks. `grep+read` runs `rg --fixed-strings --ignore-case` on the raw query string, scoped to the same code-file extensions and ignored directories that semble indexes, then reads matched files in full in descending match-count order. Tokens counted with `cl100k_base` via `tiktoken`. Recall curves record cumulative tokens of whole retrieved units without truncation; a relevant file is "covered" once any retrieved unit overlaps its annotated span.
+semble returns the top-50 ranked chunks. `grep+read` extracts non-stopword tokens from the query, runs `rg --fixed-strings --ignore-case` for each keyword (scoped to the same code-file extensions and ignored directories that semble indexes), then reads matched files in full ranked by how many distinct keywords they contain. Tokens counted with `cl100k_base` via `tiktoken`. Recall curves record cumulative tokens of whole retrieved units without truncation; a relevant file is "covered" once any retrieved unit overlaps its annotated span.
 
 </details>
 
diff --git a/benchmarks/results/token-efficiency-5ef8e76ffaef.json b/benchmarks/results/token-efficiency-5ef8e76ffaef.json
new file mode 100644
index 0000000..2e4e97d
--- /dev/null
+++ b/benchmarks/results/token-efficiency-5ef8e76ffaef.json
@@ -0,0 +1,323 @@
+{
+  "tool": "token-efficiency",
+  "tokenizer": "cl100k_base",
+  "budgets": [
+    500,
+    1000,
+    2000,
+    4000,
+    8000,
+    16000,
+    32000
+  ],
+  "n_queries": 1251,
+  "recall_at_budget": {
+    "semble": {
+      "500": 0.6855,
+      "1000": 0.8488,
+      "2000": 0.9373,
+      "4000": 0.9762,
+      "8000": 0.9912,
+      "16000": 0.996,
+      "32000": 0.9964
+    },
+    "grep+read": {
+      "500": 0.0,
+      "1000": 0.0072,
+      "2000": 0.0228,
+      "4000": 0.042,
+      "8000": 0.0755,
+      "16000": 0.1011,
+      "32000": 0.1267
+    },
+    "grep-kw+read": {
+      "500": 0.0008,
+      "1000": 0.008,
+      "2000": 0.0368,
+      "4000": 0.0879,
+      "8000": 0.2118,
+      "16000": 0.3768,
+      "32000": 0.5941
+    }
+  },
+  "first_hit_reduction": {
+    "grep+read": {
+      "n_paired": 189.0,
+      "median_semble_tokens": 275.0,
+      "median_other_tokens": 7864.0,
+      "median_reduction": 0.9704,
+      "mean_reduction": 0.9319,
+      "semble_better_pct": 1.0,
+      "expected_cost_at_cap": 29547.0104,
+      "expected_cost_cap": 32000.0,
+      "expected_cost_ratio_vs_semble": 52.1736
+    },
+    "grep-kw+read": {
+      "n_paired": 1219.0,
+      "median_semble_tokens": 342.0,
+      "median_other_tokens": 19298.0,
+      "median_reduction": 0.9813,
+      "mean_reduction": 0.9556,
+      "semble_better_pct": 0.9992,
+      "expected_cost_at_cap": 45004.4476,
+      "expected_cost_cap": 32000.0,
+      "expected_cost_ratio_vs_semble": 79.468
+    },
+    "semble": {
+      "expected_cost_at_cap": 566.3213,
+      "expected_cost_cap": 32000.0
+    }
+  },
+  "plot": {
+    "budgets": [
+      100,
+      114,
+      130,
+      149,
+      170,
+      194,
+      222,
+      253,
+      289,
+      331,
+      378,
+      431,
+      493,
+      563,
+      643,
+      735,
+      839,
+      959,
+      1095,
+      1251,
+      1429,
+      1633,
+      1865,
+      2131,
+      2434,
+      2780,
+      3176,
+      3628,
+      4144,
+      4734,
+      5407,
+      6176,
+      7055,
+      8059,
+      9205,
+      10515,
+      12011,
+      13720,
+      15672,
+      17902,
+      20448,
+      23358,
+      26681,
+      30476,
+      34812,
+      39765,
+      45422,
+      51884,
+      59265,
+      67697,
+      77328,
+      88329,
+      100895,
+      115249,
+      131645,
+      150374,
+      171766,
+      196203,
+      224116,
+      255999
+    ],
+    "recall": {
+      "semble": [
+        0.0404,
+        0.0516,
+        0.0616,
+        0.0779,
+        0.0995,
+        0.123,
+        0.1597,
+        0.2105,
+        0.2911,
+        0.4129,
+        0.5759,
+        0.6551,
+        0.6843,
+        0.7082,
+        0.7446,
+        0.7796,
+        0.808,
+        0.8352,
+        0.8628,
+        0.8784,
+        0.8954,
+        0.9149,
+        0.9301,
+        0.9436,
+        0.9539,
+        0.9579,
+        0.9635,
+        0.9707,
+        0.9778,
+        0.9817,
+        0.984,
+        0.988,
+        0.99,
+        0.9912,
+        0.9936,
+        0.9952,
+        0.9956,
+        0.996,
+        0.996,
+        0.996,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964,
+        0.9964
+      ],
+      "grep+read": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0008,
+        0.0032,
+        0.0056,
+        0.0064,
+        0.0072,
+        0.008,
+        0.0124,
+        0.014,
+        0.0172,
+        0.022,
+        0.026,
+        0.0288,
+        0.0328,
+        0.0336,
+        0.038,
+        0.0428,
+        0.0476,
+        0.0516,
+        0.0568,
+        0.0659,
+        0.0763,
+        0.0787,
+        0.0819,
+        0.0903,
+        0.0967,
+        0.0995,
+        0.1071,
+        0.1139,
+        0.1203,
+        0.1227,
+        0.1267,
+        0.1291,
+        0.1311,
+        0.1371,
+        0.1387,
+        0.1419,
+        0.1443,
+        0.1443,
+        0.1467,
+        0.1479,
+        0.1491,
+        0.1491,
+        0.1491,
+        0.1491,
+        0.1491,
+        0.1491,
+        0.1491
+      ],
+      "grep-kw+read": [
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0008,
+        0.0008,
+        0.0008,
+        0.0008,
+        0.0008,
+        0.0008,
+        0.0016,
+        0.0032,
+        0.0052,
+        0.0068,
+        0.008,
+        0.0096,
+        0.0156,
+        0.0172,
+        0.0248,
+        0.0344,
+        0.0428,
+        0.0508,
+        0.0612,
+        0.0651,
+        0.0795,
+        0.0923,
+        0.1163,
+        0.1323,
+        0.1543,
+        0.1815,
+        0.2138,
+        0.2389,
+        0.2736,
+        0.302,
+        0.334,
+        0.3664,
+        0.4291,
+        0.4671,
+        0.5123,
+        0.5485,
+        0.5813,
+        0.6112,
+        0.6423,
+        0.689,
+        0.7367,
+        0.7631,
+        0.7927,
+        0.8176,
+        0.842,
+        0.8592,
+        0.8822,
+        0.8989,
+        0.9125,
+        0.9201,
+        0.9281,
+        0.9357,
+        0.9392
+      ]
+    }
+  }
+}
diff --git a/benchmarks/token_efficiency.py b/benchmarks/token_efficiency.py
index 455e435..de4e41b 100644
--- a/benchmarks/token_efficiency.py
+++ b/benchmarks/token_efficiency.py
@@ -1,5 +1,6 @@
 import argparse
 import json
+import re
 import subprocess
 import sys
 import time
@@ -25,6 +26,7 @@
 from semble import SembleIndex
 from semble.index.dense import _DEFAULT_MODEL_NAME
 from semble.index.file_walker import DEFAULT_IGNORED_DIRS, FILE_TYPES, FileCategory
+from semble.ranking.boosting import _STOPWORDS as _SEMBLE_STOPWORDS
 from semble.types import Chunk
 
 _RG_INCLUDE_GLOBS: tuple[str, ...] = tuple(
@@ -34,10 +36,21 @@
 
 _BUDGETS = (500, 1000, 2000, 4000, 8000, 16000, 32000)
 _EXPECTED_COST_CAP = 32_000
-_PLOT_BUDGETS = sorted({int(b) for b in np.logspace(np.log10(100), np.log10(64000), 60)})
+_PLOT_BUDGETS = sorted({int(b) for b in np.logspace(np.log10(100), np.log10(256000), 60)})
 _TOKENIZER_NAME = "cl100k_base"
 _RG_MAX_MATCHES = 500
 _SEMBLE_TOP_K = 50
+_KW_MIN_LEN = 3
+
+# Extend semble's code-ranking stopwords with broader NL query words.
+_STOPWORDS: frozenset[str] = _SEMBLE_STOPWORDS | frozenset(
+    """
+    but its can not no nor so yet both either neither than then
+    will would could should may might been being had did will
+    they all any each few more most other some such only own same too very just
+    about after also before between during into through under up down over
+    """.split()
+)
 
 _IMAGES_DIR = Path(__file__).parent.parent / "assets" / "images"
 _RESULTS_DIR = Path(__file__).parent / "results"
@@ -110,6 +123,39 @@ def _grep_file_units(
     return units
 
 
+def _keywords(query: str) -> list[str]:
+    """Extract meaningful search keywords from a natural-language query."""
+    words = re.findall(r"[a-zA-Z][a-zA-Z0-9]*", query)
+    seen: set[str] = set()
+    result: list[str] = []
+    for w in words:
+        lw = w.lower()
+        if len(lw) >= _KW_MIN_LEN and lw not in _STOPWORDS and lw not in seen:
+            seen.add(lw)
+            result.append(w)
+    return result
+
+
+def _grep_keywords_file_units(query: str, repo_dir: Path) -> list[Chunk]:
+    """Return files ranked by how many distinct query keywords they contain."""
+    keywords = _keywords(query)
+    if not keywords:
+        return _grep_file_units(query, repo_dir)
+    keyword_hits: Counter[str] = Counter()
+    for kw in keywords:
+        for path, _ in _rg_matches(kw, repo_dir)[:_RG_MAX_MATCHES]:
+            keyword_hits[path] += 1
+    ranked = sorted(keyword_hits.items(), key=lambda kv: (-kv[1], kv[0]))
+    units: list[Chunk] = []
+    for path, _ in ranked:
+        try:
+            text = Path(path).read_text(encoding="utf-8", errors="replace")
+        except OSError:
+            continue
+        units.append(Chunk(content=text, file_path=path, start_line=1, end_line=text.count("\n") + 1))
+    return units
+
+
 def _retrieval_units_for_task(
     index: SembleIndex,
     task: Task,
@@ -119,6 +165,7 @@ def _retrieval_units_for_task(
     return [
         ("semble", _semble_units(index, task.query)),
         ("grep+read", _grep_file_units(task.query, repo_dir)),
+        ("grep-kw+read", _grep_keywords_file_units(task.query, repo_dir)),
     ]
 
 
@@ -212,15 +259,23 @@ def _evaluate_repo_recall(
 
 _PLOT_STYLE: dict[str, dict[str, object]] = {
     "semble": {"label": "semble", "color": "#1a5fa8", "linewidth": 2.4, "zorder": 4},
-    "grep+read": {"label": "grep + read file", "color": "#922b21", "linewidth": 1.8, "zorder": 3},
+    "grep-kw+read": {"label": "grep + read file", "color": "#b7770d", "linewidth": 1.8, "zorder": 3},
 }
 
 
+_PLOT_MAX_BUDGET = 100_000
+
+
 def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
     """Render a recall-vs-tokens curve from a recall-mode payload."""
     plot_data = payload["plot"]
-    budgets = plot_data["budgets"]
-    recalls = plot_data["recall"]
+    all_budgets = plot_data["budgets"]
+    all_recalls = plot_data["recall"]
+
+    # Trim to the configured max budget so the right edge lands on a clean tick.
+    cutoff = next((i for i, b in enumerate(all_budgets) if b > _PLOT_MAX_BUDGET), len(all_budgets))
+    budgets = all_budgets[:cutoff]
+    recalls = {m: vs[:cutoff] for m, vs in all_recalls.items()}
 
     fig, ax = plt.subplots(figsize=(8, 5))
     fig.patch.set_facecolor("white")
@@ -247,7 +302,7 @@ def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
         )
 
     ax.set_xscale("log")
-    ax.set_xlim(min(budgets), max(budgets))
+    ax.set_xlim(min(budgets), _PLOT_MAX_BUDGET)
     ax.set_ylim(0.0, 1.02)
     ax.set_xlabel("Retrieved context tokens", fontsize=10, color="#444444")
     ax.set_ylabel("Recall (relevant files surfaced)", fontsize=10, color="#444444")
@@ -257,6 +312,8 @@ def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
         color="#222222",
         pad=12,
     )
+    ax.xaxis.set_major_locator(ticker.LogLocator(base=10, numticks=10))
+    ax.xaxis.set_minor_locator(ticker.NullLocator())
     ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda v, _: f"{v / 1000:.0f}k" if v >= 1000 else f"{v:.0f}"))
     ax.tick_params(labelsize=9, colors="#555555")
     ax.legend(loc="lower right", fontsize=9, frameon=True, framealpha=0.95, edgecolor="#dddddd")

From beb820881e1e767089d19bfb535ae2f9212cbb3b Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 30 Apr 2026 08:45:16 +0200
Subject: [PATCH 13/18] Update token efficiency benchmarks

---
 assets/images/token_efficiency.png | Bin 60796 -> 54262 bytes
 benchmarks/README.md               |   6 +++---
 benchmarks/token_efficiency.py     |   7 ++-----
 3 files changed, 5 insertions(+), 8 deletions(-)

diff --git a/assets/images/token_efficiency.png b/assets/images/token_efficiency.png
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diff --git a/benchmarks/README.md b/benchmarks/README.md
index 362d13d..0d9fe92 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -75,7 +75,7 @@ For each query: tokens consumed at first relevant hit, or 32k if the method neve
 
 | Method | Expected tokens | Savings |
 |---|---:|---:|
-| grep + read file | 45,692 | baseline |
+| ripgrep + read file | 45,692 | baseline |
 | **semble** | **566** | **98% fewer** |
 
 ### Recall at fixed token budgets
@@ -85,12 +85,12 @@ A relevant file is "covered" once any retrieved unit comes from it.
 | Method | 500 | 1k | 2k | 4k | 8k | 16k | 32k |
 |---|---:|---:|---:|---:|---:|---:|---:|
 | **semble** | **0.685** | **0.849** | **0.938** | **0.976** | **0.991** | **0.996** | **0.996** |
-| grep + read file | 0.001 | 0.008 | 0.037 | 0.088 | 0.212 | 0.379 | 0.583 |
+| ripgrep + read file | 0.001 | 0.008 | 0.037 | 0.088 | 0.212 | 0.379 | 0.583 |
 
 <details>
 <summary>Methodology</summary>
 
-semble returns the top-50 ranked chunks. `grep+read` extracts non-stopword tokens from the query, runs `rg --fixed-strings --ignore-case` for each keyword (scoped to the same code-file extensions and ignored directories that semble indexes), then reads matched files in full ranked by how many distinct keywords they contain. Tokens counted with `cl100k_base` via `tiktoken`. Recall curves record cumulative tokens of whole retrieved units without truncation; a relevant file is "covered" once any retrieved unit overlaps its annotated span.
+semble returns the top-50 ranked chunks. `ripgrep+read` extracts non-stopword tokens from the query, runs `rg --fixed-strings --ignore-case` for each keyword (scoped to the same code-file extensions and ignored directories that semble indexes), then reads matched files in full ranked by how many distinct keywords they contain. Tokens counted with `cl100k_base` via `tiktoken`. Recall curves record cumulative tokens of whole retrieved units without truncation; a relevant file is "covered" once any retrieved unit overlaps its annotated span.
 
 </details>
 
diff --git a/benchmarks/token_efficiency.py b/benchmarks/token_efficiency.py
index de4e41b..47ae7d0 100644
--- a/benchmarks/token_efficiency.py
+++ b/benchmarks/token_efficiency.py
@@ -259,7 +259,7 @@ def _evaluate_repo_recall(
 
 _PLOT_STYLE: dict[str, dict[str, object]] = {
     "semble": {"label": "semble", "color": "#1a5fa8", "linewidth": 2.4, "zorder": 4},
-    "grep-kw+read": {"label": "grep + read file", "color": "#b7770d", "linewidth": 1.8, "zorder": 3},
+    "grep-kw+read": {"label": "ripgrep + read", "color": "#b7770d", "linewidth": 1.8, "zorder": 3},
 }
 
 
@@ -301,8 +301,7 @@ def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
             zorder=style["zorder"],
         )
 
-    ax.set_xscale("log")
-    ax.set_xlim(min(budgets), _PLOT_MAX_BUDGET)
+    ax.set_xlim(0, _PLOT_MAX_BUDGET)
     ax.set_ylim(0.0, 1.02)
     ax.set_xlabel("Retrieved context tokens", fontsize=10, color="#444444")
     ax.set_ylabel("Recall (relevant files surfaced)", fontsize=10, color="#444444")
@@ -312,8 +311,6 @@ def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
         color="#222222",
         pad=12,
     )
-    ax.xaxis.set_major_locator(ticker.LogLocator(base=10, numticks=10))
-    ax.xaxis.set_minor_locator(ticker.NullLocator())
     ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda v, _: f"{v / 1000:.0f}k" if v >= 1000 else f"{v:.0f}"))
     ax.tick_params(labelsize=9, colors="#555555")
     ax.legend(loc="lower right", fontsize=9, frameon=True, framealpha=0.95, edgecolor="#dddddd")

From f481da68c9090fc4c8cb213086a3477ed78d9815 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 30 Apr 2026 08:45:45 +0200
Subject: [PATCH 14/18] Update token efficiency benchmarks

---
 assets/images/token_efficiency.png | Bin 54262 -> 54559 bytes
 benchmarks/token_efficiency.py     |   6 ++----
 2 files changed, 2 insertions(+), 4 deletions(-)

diff --git a/assets/images/token_efficiency.png b/assets/images/token_efficiency.png
index ac89e1cb792906bd451c68bbc108e210e6dca0b5..df33e8ea1287af88df9ee79207e81cff4ba4d0ff 100644
GIT binary patch
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diff --git a/benchmarks/token_efficiency.py b/benchmarks/token_efficiency.py
index 47ae7d0..7c44ac5 100644
--- a/benchmarks/token_efficiency.py
+++ b/benchmarks/token_efficiency.py
@@ -272,10 +272,8 @@ def _plot_recall_vs_tokens(payload: dict[str, Any], out_path: Path) -> None:
     all_budgets = plot_data["budgets"]
     all_recalls = plot_data["recall"]
 
-    # Trim to the configured max budget so the right edge lands on a clean tick.
-    cutoff = next((i for i, b in enumerate(all_budgets) if b > _PLOT_MAX_BUDGET), len(all_budgets))
-    budgets = all_budgets[:cutoff]
-    recalls = {m: vs[:cutoff] for m, vs in all_recalls.items()}
+    budgets = all_budgets
+    recalls = all_recalls
 
     fig, ax = plt.subplots(figsize=(8, 5))
     fig.patch.set_facecolor("white")

From 1d3d0ca9c896170ecb0777031ef95da2ac548a66 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 30 Apr 2026 08:47:06 +0200
Subject: [PATCH 15/18] Update token efficiency benchmarks

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index e70fe42..e8508d9 100644
--- a/README.md
+++ b/README.md
@@ -154,7 +154,7 @@ We benchmark quality and speed across all methods on ~1,250 queries over 63 repo
 | probe | 0.387 | — | 207 ms |
 | ripgrep | 0.126 | — | 12 ms |
 
-Semble achieves 99% of the performance of the 137M-parameter [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) Hybrid, while indexing 218x faster and answering queries 11x faster.
+Semble achieves 99% of the performance of the 137M-parameter [CodeRankEmbed](https://huggingface.co/nomic-ai/CodeRankEmbed) Hybrid, while indexing 218x faster and answering queries 11x faster. See [benchmarks](benchmarks/README.md) for per-language results, ablations, and methodology.
 
 ### Token efficiency
 
@@ -162,7 +162,7 @@ Agents using grep+read spend most of their context budget on irrelevant code. Se
 
 ![Token efficiency: recall vs. retrieved tokens](https://raw.githubusercontent.com/MinishLab/semble/main/assets/images/token_efficiency.png)
 
-At a 2k token budget, semble reaches 94% recall. Grep+read needs a full 100k context window to reach 85%. See [benchmarks](benchmarks/README.md) for per-language results, ablations, and methodology.
+At a 2k token budget, semble reaches 94% recall. Grep+read needs a full 100k context window to reach 85%. See [benchmarks](benchmarks/README.md) for details.
 
 ## License
 

From 67f0c3e1fad698510be6c44115677ad901129d79 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 30 Apr 2026 08:52:29 +0200
Subject: [PATCH 16/18] Update token efficiency benchmarks

---
 README.md            |  2 +-
 benchmarks/README.md | 58 ++++++++++++++++++++++----------------------
 2 files changed, 30 insertions(+), 30 deletions(-)

diff --git a/README.md b/README.md
index e8508d9..eb5e813 100644
--- a/README.md
+++ b/README.md
@@ -162,7 +162,7 @@ Agents using grep+read spend most of their context budget on irrelevant code. Se
 
 ![Token efficiency: recall vs. retrieved tokens](https://raw.githubusercontent.com/MinishLab/semble/main/assets/images/token_efficiency.png)
 
-At a 2k token budget, semble reaches 94% recall. Grep+read needs a full 100k context window to reach 85%. See [benchmarks](benchmarks/README.md) for details.
+Semble uses **98% fewer tokens** on average, and reaches 94% recall at a budget of only 2k tokens, while grep+read needs a full 100k context window to reach 85%. See [benchmarks](benchmarks/README.md#token-efficiency) for details.
 
 ## License
 
diff --git a/benchmarks/README.md b/benchmarks/README.md
index 0d9fe92..ddf3d61 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -3,8 +3,8 @@
 Quality and speed benchmarks for `semble`.
 
 - [Main results](#main-results)
-- [By language](#by-language)
 - [Token efficiency](#token-efficiency)
+- [By language](#by-language)
 - [Ablations](#ablations)
 - [Dataset](#dataset)
 - [Methods](#methods)
@@ -34,33 +34,6 @@ The 137M-param CodeRankEmbed Hybrid wins NDCG@10 by 0.008. semble wins index tim
 
 NDCG@10 is averaged across all queries. Speed numbers use one repo per language, CPU only: cold-start index time and warm query p50 (median across 5 consecutive runs).
 
-## By language
-
-NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best score per row is bolded.
-
-| Language | semble | CRE Hybrid | CRE | ColGREP | grepai | probe | ripgrep |
-|---|---:|---:|---:|---:|---:|---:|---:|
-| scala | 0.909 | **0.922** | 0.845 | 0.765 | 0.330 | 0.392 | 0.180 |
-| cpp | **0.915** | 0.913 | 0.846 | 0.626 | 0.731 | 0.375 | 0.126 |
-| ruby | **0.909** | **0.909** | 0.769 | 0.708 | 0.643 | 0.382 | 0.230 |
-| elixir | 0.894 | **0.905** | 0.869 | 0.808 | 0.669 | 0.412 | 0.134 |
-| javascript | 0.917 | 0.903 | **0.920** | 0.823 | 0.675 | 0.588 | 0.176 |
-| zig | **0.913** | 0.901 | 0.807 | 0.474 | 0.755 | 0.369 | 0.000 |
-| csharp | 0.885 | **0.889** | 0.743 | 0.614 | 0.277 | 0.392 | 0.117 |
-| go | **0.895** | 0.884 | 0.676 | 0.785 | 0.722 | 0.410 | 0.133 |
-| python | 0.867 | **0.880** | 0.794 | 0.777 | 0.634 | 0.488 | 0.202 |
-| php | 0.858 | **0.874** | 0.758 | 0.663 | 0.402 | 0.340 | 0.123 |
-| swift | 0.860 | **0.873** | 0.721 | 0.710 | 0.429 | 0.280 | 0.160 |
-| bash | 0.825 | 0.852 | **0.892** | 0.706 | 0.723 | 0.226 | 0.000 |
-| lua | 0.823 | **0.847** | 0.803 | 0.798 | 0.699 | 0.336 | 0.000 |
-| java | **0.849** | 0.841 | 0.706 | 0.641 | 0.386 | 0.536 | 0.198 |
-| kotlin | 0.821 | **0.830** | 0.670 | 0.637 | 0.478 | 0.335 | 0.166 |
-| rust | **0.856** | 0.827 | 0.627 | 0.662 | 0.519 | 0.242 | 0.162 |
-| c | 0.741 | **0.806** | 0.706 | 0.676 | 0.555 | 0.384 | 0.000 |
-| haskell | 0.765 | 0.771 | **0.776** | 0.683 | 0.483 | 0.313 | 0.000 |
-| typescript | 0.706 | **0.708** | 0.545 | 0.430 | 0.394 | 0.354 | 0.128 |
-| **overall** | **0.854** | **0.862** | **0.765** | **0.693** | **0.561** | **0.387** | **0.126** |
-
 ## Token efficiency
 
 Coding agents (Claude Code, OpenCode, etc.) typically find code by running `grep` on keywords and reading the matched files. We model that workflow and compare it against semble's chunk retrieval across 1,251 queries.
@@ -90,10 +63,37 @@ A relevant file is "covered" once any retrieved unit comes from it.
 <details>
 <summary>Methodology</summary>
 
-semble returns the top-50 ranked chunks. `ripgrep+read` extracts non-stopword tokens from the query, runs `rg --fixed-strings --ignore-case` for each keyword (scoped to the same code-file extensions and ignored directories that semble indexes), then reads matched files in full ranked by how many distinct keywords they contain. Tokens counted with `cl100k_base` via `tiktoken`. Recall curves record cumulative tokens of whole retrieved units without truncation; a relevant file is "covered" once any retrieved unit overlaps its annotated span.
+semble returns the top-50 ranked chunks. `ripgrep+read` splits the query into keywords (dropping stopwords and short words), runs `rg --fixed-strings --ignore-case` for each keyword, then reads matched files in full ranked by how many distinct keywords they contain. Both methods search the same set of file types and ignored directories. Tokens are counted with `cl100k_base` via `tiktoken`. A relevant file is "covered" once any retrieved unit overlaps its annotated span.
 
 </details>
 
+## By language
+
+NDCG@10 per language, sorted by CodeRankEmbed Hybrid (CRE in the table). Best score per row is bolded.
+
+| Language | semble | CRE Hybrid | CRE | ColGREP | grepai | probe | ripgrep |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| scala | 0.909 | **0.922** | 0.845 | 0.765 | 0.330 | 0.392 | 0.180 |
+| cpp | **0.915** | 0.913 | 0.846 | 0.626 | 0.731 | 0.375 | 0.126 |
+| ruby | **0.909** | **0.909** | 0.769 | 0.708 | 0.643 | 0.382 | 0.230 |
+| elixir | 0.894 | **0.905** | 0.869 | 0.808 | 0.669 | 0.412 | 0.134 |
+| javascript | 0.917 | 0.903 | **0.920** | 0.823 | 0.675 | 0.588 | 0.176 |
+| zig | **0.913** | 0.901 | 0.807 | 0.474 | 0.755 | 0.369 | 0.000 |
+| csharp | 0.885 | **0.889** | 0.743 | 0.614 | 0.277 | 0.392 | 0.117 |
+| go | **0.895** | 0.884 | 0.676 | 0.785 | 0.722 | 0.410 | 0.133 |
+| python | 0.867 | **0.880** | 0.794 | 0.777 | 0.634 | 0.488 | 0.202 |
+| php | 0.858 | **0.874** | 0.758 | 0.663 | 0.402 | 0.340 | 0.123 |
+| swift | 0.860 | **0.873** | 0.721 | 0.710 | 0.429 | 0.280 | 0.160 |
+| bash | 0.825 | 0.852 | **0.892** | 0.706 | 0.723 | 0.226 | 0.000 |
+| lua | 0.823 | **0.847** | 0.803 | 0.798 | 0.699 | 0.336 | 0.000 |
+| java | **0.849** | 0.841 | 0.706 | 0.641 | 0.386 | 0.536 | 0.198 |
+| kotlin | 0.821 | **0.830** | 0.670 | 0.637 | 0.478 | 0.335 | 0.166 |
+| rust | **0.856** | 0.827 | 0.627 | 0.662 | 0.519 | 0.242 | 0.162 |
+| c | 0.741 | **0.806** | 0.706 | 0.676 | 0.555 | 0.384 | 0.000 |
+| haskell | 0.765 | 0.771 | **0.776** | 0.683 | 0.483 | 0.313 | 0.000 |
+| typescript | 0.706 | **0.708** | 0.545 | 0.430 | 0.394 | 0.354 | 0.128 |
+| **overall** | **0.854** | **0.862** | **0.765** | **0.693** | **0.561** | **0.387** | **0.126** |
+
 ## Ablations
 
 `raw` returns retrieval scores directly; `+ ranking` feeds them through semble's hybrid ranker.

From f636132a53ba752ed18de12d0016fa45c1ded622 Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 30 Apr 2026 08:53:54 +0200
Subject: [PATCH 17/18] Update token efficiency benchmarks

---
 benchmarks/README.md | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/benchmarks/README.md b/benchmarks/README.md
index ddf3d61..dd1335d 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -36,15 +36,13 @@ NDCG@10 is averaged across all queries. Speed numbers use one repo per language,
 
 ## Token efficiency
 
-Coding agents (Claude Code, OpenCode, etc.) typically find code by running `grep` on keywords and reading the matched files. We model that workflow and compare it against semble's chunk retrieval across 1,251 queries.
-
-**semble uses 98% fewer tokens than grep+read.**
+Coding agents (Claude Code, OpenCode, etc.) typically find code by running `grep` on keywords and reading the matched files. We model that workflow and compare it against semble's chunk retrieval across our full benchmark of 1251 queries.
 
 ![Token efficiency: recall vs. retrieved tokens](../assets/images/token_efficiency.png)
 
 ### Expected tokens per query
 
-For each query: tokens consumed at first relevant hit, or 32k if the method never finds anything. Averaged across all 1,251 queries.
+For each query: tokens consumed at first relevant hit, or 32k if the method never finds anything. Averaged across all 1251 queries.
 
 | Method | Expected tokens | Savings |
 |---|---:|---:|

From 67aa8d037a0edb8a0ea3662e6e3d2ff31bfed46f Mon Sep 17 00:00:00 2001
From: Pringled <thomas123@live.nl>
Date: Thu, 30 Apr 2026 08:57:02 +0200
Subject: [PATCH 18/18] Update token efficiency benchmarks

---
 .../token-efficiency-6d7dea15950e.json        | 241 ------------------
 1 file changed, 241 deletions(-)
 delete mode 100644 benchmarks/results/token-efficiency-6d7dea15950e.json

diff --git a/benchmarks/results/token-efficiency-6d7dea15950e.json b/benchmarks/results/token-efficiency-6d7dea15950e.json
deleted file mode 100644
index 26374d1..0000000
--- a/benchmarks/results/token-efficiency-6d7dea15950e.json
+++ /dev/null
@@ -1,241 +0,0 @@
-{
-  "tool": "context-efficiency-recall",
-  "tokenizer": "cl100k_base",
-  "budgets": [
-    500,
-    1000,
-    2000,
-    4000,
-    8000,
-    16000,
-    32000
-  ],
-  "n_queries": 1251,
-  "recall_at_budget": {
-    "semble": {
-      "500": 0.6859,
-      "1000": 0.8492,
-      "2000": 0.9369,
-      "4000": 0.9762,
-      "8000": 0.9912,
-      "16000": 0.996,
-      "32000": 0.9964
-    },
-    "grep+read": {
-      "500": 0.0,
-      "1000": 0.0072,
-      "2000": 0.0228,
-      "4000": 0.042,
-      "8000": 0.0755,
-      "16000": 0.1003,
-      "32000": 0.1251
-    }
-  },
-  "first_hit_reduction": {
-    "grep+read": {
-      "n_paired": 189.0,
-      "median_semble_tokens": 275.0,
-      "median_other_tokens": 7864.0,
-      "median_reduction": 0.9708,
-      "mean_reduction": 0.9323,
-      "semble_better_pct": 1.0,
-      "expected_cost_at_cap": 29674.5835,
-      "expected_cost_cap": 32000.0,
-      "expected_cost_ratio_vs_semble": 52.3008
-    },
-    "semble": {
-      "expected_cost_at_cap": 567.3829,
-      "expected_cost_cap": 32000.0
-    }
-  },
-  "plot": {
-    "budgets": [
-      100,
-      111,
-      124,
-      138,
-      154,
-      172,
-      192,
-      215,
-      240,
-      267,
-      298,
-      333,
-      372,
-      415,
-      463,
-      516,
-      576,
-      643,
-      717,
-      801,
-      893,
-      997,
-      1112,
-      1241,
-      1385,
-      1545,
-      1724,
-      1923,
-      2146,
-      2395,
-      2672,
-      2981,
-      3326,
-      3711,
-      4141,
-      4620,
-      5155,
-      5751,
-      6417,
-      7160,
-      7989,
-      8913,
-      9945,
-      11096,
-      12380,
-      13813,
-      15412,
-      17196,
-      19186,
-      21407,
-      23884,
-      26649,
-      29733,
-      33174,
-      37014,
-      41298,
-      46077,
-      51410,
-      57361,
-      64000
-    ],
-    "recall": {
-      "semble": [
-        0.0404,
-        0.0484,
-        0.0576,
-        0.0691,
-        0.0815,
-        0.1007,
-        0.1206,
-        0.1525,
-        0.1941,
-        0.2401,
-        0.3183,
-        0.4205,
-        0.5536,
-        0.6383,
-        0.6735,
-        0.693,
-        0.7138,
-        0.743,
-        0.7716,
-        0.792,
-        0.8184,
-        0.8484,
-        0.8628,
-        0.878,
-        0.8898,
-        0.9105,
-        0.9213,
-        0.9345,
-        0.9436,
-        0.9515,
-        0.9547,
-        0.9619,
-        0.9667,
-        0.9723,
-        0.9778,
-        0.9817,
-        0.984,
-        0.986,
-        0.9888,
-        0.9908,
-        0.9912,
-        0.9924,
-        0.9952,
-        0.9952,
-        0.996,
-        0.996,
-        0.996,
-        0.996,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964,
-        0.9964
-      ],
-      "grep+read": [
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0008,
-        0.0032,
-        0.0056,
-        0.0064,
-        0.0064,
-        0.0072,
-        0.008,
-        0.0124,
-        0.014,
-        0.014,
-        0.0188,
-        0.022,
-        0.0268,
-        0.028,
-        0.0312,
-        0.0336,
-        0.0348,
-        0.0396,
-        0.0428,
-        0.0476,
-        0.05,
-        0.0548,
-        0.0584,
-        0.0675,
-        0.0755,
-        0.0779,
-        0.0787,
-        0.0859,
-        0.0887,
-        0.0963,
-        0.0979,
-        0.1039,
-        0.1103,
-        0.1147,
-        0.1187,
-        0.1211,
-        0.1251,
-        0.1267,
-        0.1275,
-        0.1299,
-        0.1355,
-        0.1371,
-        0.1411,
-        0.1419
-      ]
-    }
-  }
-}