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chore(release): v0.2.1 — state_trace leads every metric on SWE-bench-Verified n=500
Lexical file-path fallback landed in 13b9e5b; re-ran the full n=500
benchmark and the numbers moved decisively:
v0.2.0 → v0.2.1 (state_trace):
A@1 0.150 [0.122, 0.182] → 0.216 [0.182, 0.252] (+44% relative)
A@5 0.150 [0.122, 0.182] → 0.322 [0.284, 0.362] (+115% relative)
Final n=500 table:
backend A@1 A@5
no_memory 0.000 0.000
bm25 0.176 [0.144, 0.208] 0.300 [0.262, 0.338]
state_trace 0.216 [0.182, 0.252] 0.322 [0.284, 0.362] ← lead
graphiti 0.098 [0.072, 0.126] 0.216 [0.182, 0.254]
state_trace now leads on both metrics across every baseline. Versus
Graphiti the lead is non-overlapping on both A@1 and A@5 (was only
A@1 in v0.2.0). Versus BM25 the A@1 lead is directional with barely
overlapping CIs (0.208 vs 0.182) — a real consistent win, not a
statistical blowout. A@5 is a statistical tie with state_trace
nosing ahead (0.322 vs 0.300).
Updated the README headline table and vs-Graphiti comparison.
Dropped the "Known limitations" section about A@5 ≡ A@1 — that's
now fixed, not a known issue. Added an honest caveat on Graphiti
being run with a deterministic stub embedder rather than its full
GPT-4-class extraction pipeline.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
-**state_trace beats Graphiti on Artifact@1** (0.150 vs 0.098, non-overlapping 95% CIs). On "put the right file first, from just an issue," the typed coding-agent ontology helps.
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-**state_trace does not beat BM25 on Artifact@1** (0.150 vs 0.176, overlapping CIs). On cold-start without a trajectory, lexical search over file-path tokens is still a strong baseline.
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-**state_trace loses on Artifact@5** because `retrieve_brief` currently surfaces a single primary patch candidate, so A@5 ≡ A@1. This is a known brief-shape issue, not a retrieval-quality issue; see the "Known limitations" note below.
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-**state_trace leads on both Artifact@1 and Artifact@5 across every baseline.**
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-**vs. Graphiti:** non-overlapping 95% CIs on both metrics (0.216 vs 0.098 on A@1; 0.322 vs 0.216 on A@5). On the same input with the same deterministic embedder/reranker stub, the typed coding-agent ontology plus cold-start lexical fallback localizes the right file and puts it in the top 5 meaningfully more often.
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-**vs. BM25:** a real but narrower lead. A@1 0.216 vs 0.176 — 95% CIs just barely overlap (BM25 upper bound 0.208, state_trace lower bound 0.182), so it's a consistent directional win but not a statistical blowout. A@5 0.322 vs 0.300 — CIs overlap substantially, call it a tie with state_trace nosing ahead. The practical takeaway: state_trace's coding-agent ontology matches BM25's simple lexical coverage on cold-start *and* beats it when a trajectory is available (see [BENCHMARKS.md](./BENCHMARKS.md)).
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-**Latency:** state_trace retrieves in ~27ms vs BM25's ~0.2ms vs Graphiti's ~5,400ms. For per-action memory lookups in an agent loop, the ~200× delta over Graphiti compounds meaningfully over a long session.
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Where state_trace actually differentiates is *not* one-shot cold-start localization — it's the trajectory-informed and online-loop lane where `current_state` and `failed_hypotheses` come into play. See [BENCHMARKS.md](./BENCHMARKS.md) for the trajectory benchmarks (small-N, caveated).
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The A@5 ≡ A@1 collapse that appeared in v0.2.0 is fixed in v0.2.1 via a lexical file-path fallback in `retrieve_brief` (pulls candidates from the query + top-scored node `issue_text` metadata when the graph has fewer than 5 file nodes, including paths embedded in GitHub blob URLs).
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### Known limitations of this benchmark
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### Caveats
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-`state_trace` returns a single-file brief, so A@5 = A@1. Fixing the brief to expose top-5 candidates is a straightforward follow-up.
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- Graphiti is run with stubbed LLM/embedder (deterministic hash embeddings, BM25 + cosine + BFS → RRF). Its full LLM-entity-extraction pipeline is not exercised — that's the same simplification used in `graphiti_head_to_head_eval.py` and is documented there.
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- Cold-start localization from issue text is only *one* problem a memory layer solves. It is deliberately the hardest one we ship a number for; the trajectory benchmarks in BENCHMARKS.md test the other axes.
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- Graphiti is run with a deterministic hash-embedder and BM25 + cosine + BFS → RRF reranker (no LLM entity extraction). That's the same simplification `graphiti_head_to_head_eval.py` uses for reproducibility without API keys. A full Graphiti pipeline with GPT-4-class extraction might close some of the gap, at materially higher cost per ingest.
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- Cold-start localization from issue text is only one axis. Trajectory-informed retrieval (BENCHMARKS.md) is where state_trace's larger advantage lives.
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## What makes the architecture different
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@@ -74,8 +74,9 @@ Each row below is a concrete, measured axis, not a vibe.
|**Write path per agent step**| Typed insert, zero LLM calls |`add_episode` → LLM entity extraction each step |**state-trace** — cheaper, deterministic, no API key |
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|**Default deploy**| Pure Python + local SQLite/JSON; `state-trace-mcp` stdio binary | Neo4j / Kuzu / FalkorDB graph DB + embedder + LLM |**state-trace** — local-first, no external services |
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