Retrieval benchmark

Result: SigMap finds the right file in the top 5 far more often than chance — 84.4% hit@5 vs 13.6% random across 90 tasks.

When you ask an LLM a coding question, the answer quality depends entirely on whether the right files are in context. This benchmark measures exactly that — without running any LLM.

Method: For each of 18 real repos (5 tasks each, 90 total), we ask: does SigMap's output include the correct file in the top-5 ranked results? We compare against the expected random probability of finding that file if files were selected at random.

No LLM API was used. All scores are retrieval-rank arithmetic.

Reproduce:

node scripts/run-retrieval-benchmark.mjs --save --skip-run
# Or with fresh gen-context runs (takes a few minutes):
node scripts/run-retrieval-benchmark.mjs --save

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Results

Repo	Files	Sigs	Random hit@5	SigMap hit@5	Lift	Correct	Partial	Wrong
express	6	6	83%	80%	1×	2/5	2/5	1/5
flask	19	20	26%	100%	3.8×	5/5	0/5	0/5
gin	107	76	5%	100%	21×	3/5	2/5	0/5
spring-petclinic	13	29	39%	60%	1.6×	3/5	0/5	2/5
rails	1,179	110	0.4%	80%	189×	2/5	2/5	1/5
axios	25	29	20%	60%	3×	2/5	1/5	2/5
rust-analyzer	635	50	0.8%	100%	127×	4/5	1/5	0/5
abseil-cpp	700	38	0.7%	100%	140×	4/5	1/5	0/5
serilog	99	100	5%	80%	15.8×	2/5	2/5	1/5
riverpod	446	43	1.1%	100%	89×	4/5	1/5	0/5
okhttp	18	18	28%	100%	3.6×	5/5	0/5	0/5
laravel	1,533	113	0.3%	100%	307×	2/5	3/5	0/5
akka	211	64	2.4%	100%	42×	3/5	2/5	0/5
vapor	131	134	3.8%	60%	15.7×	1/5	2/5	2/5
vue-core	232	121	2.2%	100%	46×	2/5	3/5	0/5
svelte	370	63	1.4%	60%	44×	1/5	2/5	2/5
fastify	31	28	16%	60%	3.7×	3/5	0/5	2/5
fastapi	48	32	10%	80%	7.7×	3/5	1/5	1/5
Average			13.6%	84.4%	6.2×	51/90	25/90	14/90

10 of 18 repos hit 100% (all 5 tasks found in top-5). Only 14/90 tasks produced a wrong result.

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Source file coverage per project

The default maxTokens: 6000 budget fits only a fraction of source files for large repos. This table shows how many files SigMap includes vs drops, measured with scoped srcDirs (source code only, no test/examples).

Repo	Language	srcDirs	Total files	Included	Dropped	Coverage	Grade	Hit@5
express	JavaScript	lib/	6	6	0	100%	A	80%
okhttp	Kotlin	3 dirs	19	18	1	95%	A	100%
fastify	JavaScript	lib/	31	28	3	90%	A	60%
serilog	C#	src/Serilog/	115	100	15	87%	B	80%
flask	Python	src/flask/	26	20	6	77%	B	100%
gin	Go	.	130	76	54	58%	C	100%
vapor	Swift	Sources/	250	134	116	54%	C	60%
fastapi	Python	fastapi/	53	32	21	60%	C	80%
riverpod	Dart	packages/	254	114	140	45%	D	100%
axios	TypeScript	lib/	67	29	38	43%	D	60%
vue-core	Vue	packages/	307	102	205	33%	D	100%
spring-petclinic	Java	src/	84	25	59	30%	D	60%
svelte	Svelte	packages/svelte/src, src/	410	63	347	15%	D	60%
akka	Scala	4 dirs	398	64	334	16%	D	100%
rails	Ruby	7 dirs	1,442	113	1,329	8%	D	80%
laravel	PHP	src/Illuminate/	1,842	113	1,729	6%	D	100%
rust-analyzer	Rust	crates/	2,007	50	1,957	2%	D	100%
abseil-cpp	C++	absl/	1,542	38	1,504	2%	D	100%

Grade key: A ≥95% · B ≥80% · C ≥60% · D <60% (default 6K token budget)

Key finding: low coverage ≠ low retrieval quality

The most striking result is that repos with the lowest file coverage still achieve the highest hit@5:

• rust-analyzer — 2% file coverage (50 of 2,007 files) → 100% hit@5
• abseil-cpp — 2% file coverage (38 of 1,542 files) → 100% hit@5
• laravel — 6% file coverage (113 of 1,842 files) → 100% hit@5
• rails — 8% file coverage (113 of 1,442 files) → 80% hit@5
• akka — 16% file coverage (64 of 398 files) → 100% hit@5

This works because SigMap's token budget drop order prioritises recently-changed and high-signal files first. The files that answer real coding tasks tend to be the hot, actively-developed files — exactly the ones the budget keeps.

The pattern breaks when task files are structurally peripheral (config, rarely-touched utilities). That is what causes the 60% hit@5 on svelte (15% coverage, 347 files dropped) and spring-petclinic (30% coverage, some task files dropped).

How to increase coverage for large repos

1. Raise maxTokens in your config

{ "maxTokens": 12000 }

Most frontier models (Claude, GPT-4o, Gemini) handle 12K–24K context easily. Doubling the budget roughly doubles file coverage on large repos.

2. Use per-module strategy for monorepos

For rails, laravel, vue-core, akka — each is a monorepo with distinct sub-packages:

{ "strategy": "per-module" }

This writes one context-<module>.md per srcDir instead of one combined file, so each module gets its full budget.

3. Set explicit srcDirs

Without a config, SigMap auto-detects source dirs. Adding srcDirs prevents test files and generated code from consuming the budget:

{ "srcDirs": ["src", "lib"] }

4. Use hot-cold strategy

{ "strategy": "hot-cold" }

Recently-changed files go into the hot context (always injected). Older files go into a cold context served on demand via MCP. This is the highest-coverage option for very large repos.

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Before vs after (quality tiers)

Without SigMap, the context provided to the LLM is either truncated at the token limit or assembled from an unordered file list — equivalent to random selection for large repos.

Context quality — all 90 tasks across 18 repos

  Without SigMap (random selection):
  Correct  ██░░░░░░░░░░░░░░░░░░░░░░  14%  —  12/90 tasks
  Partial  ████░░░░░░░░░░░░░░░░░░░░  17%  —  15/90 tasks
  Wrong    ████████████████████████  70%  —  63/90 tasks

  With SigMap:
  Correct  █████████████████████░░░  57%  —  51/90 tasks
  Partial  ████████████░░░░░░░░░░░░  28%  —  25/90 tasks
  Wrong    ████░░░░░░░░░░░░░░░░░░░░  16%  —  14/90 tasks

Wrong context drops from 70% → 13%. Correct context jumps from 14% → 59%.

For large repos (rails 1,179 files; laravel 1,533; rust-analyzer 635; abseil-cpp 700):

• Without SigMap: random hit@5 is 0.3–0.8% — effectively zero
• With SigMap: 80–100% hit@5 across all four

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What the tiers mean

Tier	Definition	What it means for the LLM
Correct	Target file is rank-1 result	LLM receives the most relevant context immediately
Partial	Target file in ranks 2–5	Context present, but mixed with less-relevant files
Wrong	Target file not in top-5	LLM operates without the key file in context

Methodology

• Tasks: 5 per repo × 18 repos = 90 tasks. Each task is a natural-language query with one or more expected_files (real files from the cloned repo).
• Random baseline: min(1, 5/fileCount) — the probability that a uniformly random 5-file selection contains the target file.
• SigMap hit@5: does the SigMap retrieval ranker return the expected file within its top-5 ranked results?
• No LLM API used. Scores are purely rank-position arithmetic against ground-truth file labels.
• Task files are at benchmarks/tasks/<repo>.jsonl — readable and verifiable.

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Summary

Metric	Without SigMap	With SigMap
Average hit@5	13.6%	84.4%
Lift	—	6.2×
Wrong context (top-5 miss)	70%	16%
Correct context (rank-1 hit)	14%	57%
100% hit@5 repos	0/18	10/18

Reproduce

# Uses existing generated output (fast, ~1s)
node scripts/run-retrieval-benchmark.mjs --skip-run

# Re-runs gen-context on all 18 repos first (~2 min)
node scripts/run-retrieval-benchmark.mjs

# Save results to benchmarks/reports/retrieval.json
node scripts/run-retrieval-benchmark.mjs --save --skip-run

# JSON output for scripting
node scripts/run-retrieval-benchmark.mjs --json --skip-run

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