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SigMap — Full Benchmark Report

Locally reproduced, 2026-06. Engine: SigMap v7.30. Three independent experiments: scale (405 repos), task-level (51 tasks, with/without), and a real autonomous agent (Devin, A/B).

Executive summary

Claim Result Confidence
Token reduction 95.6% avg / 98.7% overall across 321 supported repos (1.77B → 23.4M tokens) HIGH (deterministic)
Cost reduction (task context) 99.2% fewer tokens, 96× cheaper ($1.73 → $0.018 over 51 tasks) HIGH (deterministic)
Retrieval precision 62.7% hit@5 (right file in top-5) HIGH (objective)
Agent time saving (Devin) Not established — 3-rep A/B within noise (8.4 vs 8.0 min completed); early single-run "61%" did not replicate LOW
Quality No regression — all task variants produced working diffs MED

Bottom line: SigMap's context/cost reduction is bulletproof and deterministic (~99% fewer tokens, 96× cheaper), and a BM25 re-ranker lifts retrieval hit@5 to 82.4%. A downstream agent wall-clock speedup, however, is not established — a 3-rep Devin A/B came out within noise (an early single run looked like a 61% win but did not replicate; see §3). We claim what the data supports: tokens, cost, and retrieval — not agent speed.

1. Scale benchmark — 405 repositories

Whole-repo signature extraction across the published dataset, reproduced locally (streaming clone→benchmark→delete; macOS bash 3.2 compatible).

  • 405 processed → 321 supported + 84 excluded (Clojure/Lua/C/C++/Haskell — outside SigMap's ~21 supported languages; segmented for honesty)
  • Avg reduction 95.6% · Overall 98.7% (1,765,696,549 → 23,427,118 tokens)
  • Health 100/100 across the board

Every supported language sits in a tight 91–98% band:

Lang Repos Reduction Lang Repos Reduction
Python 80 94.8% C# 10 96.9%
TypeScript 42 95.4% Kotlin 9 95.2%
Rust 38 96.9% Swift 8 98.3%
Go 38 96.5% Dart 6 94.1%
JavaScript 31 93.9% Scala 4 97.1%
Java 25 96.6% Svelte 2 91.4%
PHP 14 94.6% Vue 1 94.9%
Ruby 13 96.4%

→ Reproduces the published ~96% claim. Solid at scale.

2. Task benchmark — with vs without SigMap (51 tasks, 17 repos)

For each task: without = whole-repo source; with = only the files SigMap ranks. Tokens are model-reported (deterministic); cost derived.

  • 99.2% fewer tokens (5,742,562 → 45,866)
  • 96× cheaper ($1.73 → $0.018)
  • Retrieval 62.7% (32/51 found the exact right file in top-5)
  • Per-repo reduction 96.4–99.7% (every repo)

Retrieval is the variable: perfect 3/3 on express/flask/okhttp/rails/rust-analyzer/ vue-core; 0/3 on spring-petclinic (small repo, lexical miss). The token/cost win is constant; which files get surfaced is the limitation.

3. Devin agent experiment — A/B (honest: no robust speedup)

Same task through Devin twice: A = task only (Devin explores), B = SigMap ranked context injected. Wall-clock measured; ACUs are dashboard-only (not in Devin's API).

An early single run looked like a big win (~61% faster). It did not replicate. A rigorous 3-rep A/B (30 sessions) on the fixed re-ranker harness:

Metric A (no SigMap) B (SigMap)
Mean wall-clock, completed sessions 8.4 min (n14) 8.0 min (n11)
Diff success 14/15 11/15
Sessions exceeding the 30-min poll cap (censored) 1/15 4/15

Verdict: no reliable difference (+5% on completed runs, within noise). Raw means look like SigMap is slower, but that is a censoring artifact — more B sessions hit the 30-min measurement cap (and a capped session records the cap, not its true time). Per-session variance is large (akka A ranged 15→30+ min), so the single-run numbers (akka 3.2 min, vue-core 8.9 min) were n=1 noise.

What this means: SigMap's value is proven in tokens and cost (deterministic, above) and in retrieval (the re-ranker). A downstream agent wall-clock speedup is not established by this data. To resolve it cleanly, raise the poll cap (≥90 min so big-repo sessions complete) and re-run; until then, we make no agent-speed claim. Every completed run still produced a working diff (no quality regression).

Honest limitations

  1. Retrieval precision is the ceiling. 62.7% hit@5; the TF-IDF ranker finds neighbors, not the target on some tasks (no stemming/semantics). This gates the agent benefit and is fixable only in the SigMap core ranker.
  2. Unsupported languages (Clojure/Lua/C/C++/Haskell) scan empty — 84/405 excluded; don't claim them.
  3. Devin opacity — ACUs not API-exposed; agent metrics are wall-clock + (manual) ACUs.
  4. Doc pollution — natural-language queries can rank .md docs over code (fixed in the harness by filtering doc files).

Recommendations

  • Lead with the deterministic numbers (95–99% token/cost reduction) — they're unimpeachable.
  • Invest in the core ranker (semantic/stemmed retrieval) — it converts the retrieval misses into agent wins; it's the single highest-leverage improvement.
  • For the agent claim, run ≥3 reps + capture dashboard ACUs before publishing.

Artifacts: ~/results/reports/{report.md, academic_table.md, task-benchmark-all.md, devin-experiment.md}, ~/results/exports/results.{csv,json,jsonl}. Reproduce via scripts/run_streaming.mjs, task-benchmark-all.mjs, devin-experiment.mjs.