Can the swe-errors keyword rules be improved on 30 hand labels and still help on the other 30?

What it found

A two-fold held-out check of a mechanical refit (best rule order, then up to six greedily added keywords) of the swe-errors rules against swe-errlabel’s 60 hand labels. Averaged over 200 random run-level splits the refit agrees with the hand label on 68.5% of entries against 66.7% for the shipped rules (+1.1 entries, range −4 to +7), while fitted and scored on all 60 it reaches 85%: most of an in-sample improvement is memorisation. Preliminary: one labeller, 60 entries.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

More in The software-engineering loop

• Did research brief v1 change how runs go?Did research brief v1 change how runs go? Same-days comparison of v0 (342 runs) and v1 (67) on 15 to 18 October: no detectable difference in cost, time or success; the lower v1 error rate (7.5% vs 17.0%) vanishes within unlabelled-task runs (8.3% vs 9.6%); v1 records more doc-friction entries (1.24 vs 0.72 per run, p = 0.0001). Observational.
• Does Opus pay for itself on this portfolio's runs? Cost ratio by track, and the sample needed to find outDoes Opus pay for itself on this portfolio's runs? An Opus run costs about 8 to 9 times a Sonnet run in the same track; with 12 priced Opus runs the ledger cannot compare value per dollar, and a 50% difference in projects per run needs about 129 runs per arm (preliminary; cost only).
• Do agent runs that record a deviation from the brief cost more or end worse?Do agent runs that record a deviation from the brief cost more or end worse? Of 1,564 run records 203 list one; within track they take ~1.6x as long and cost ~1.2x as much, but the cost gap rests mainly on the process track, and they end partial 5 points more often. Observational, preliminary.
• How well does a run's tool-call count predict what it costs?How well does a run's tool-call count predict its cost? On 882 priced run records cost is about $0.03 per call, a log-log fit has R² 0.91 and a held-out median error of 9%; the pooled slope 1.22 is partly group mixing (1.10 within Sonnet). Preliminary.
• Where does an agent run's cost go, and why does it grow faster than its tool calls?Where does a run's cost go? On 893 run records with per-token-kind usage, cache reads are a median 45% of the API-equivalent cost and 1-hour cache writes 41%, output only 13%; the starting context (about 60,000 tokens) is re-read on every request, which makes cost grow faster than linearly in tool calls (preliminary).
• Do agent runs that add no tests end worse?Do agent runs that add no tests end worse? 84 of 621 counted run records add none; within track they end partial 4/84 against 16/537 (+2.6 points, p = 0.23, no detectable effect) and cost less, probably because they do less. Preliminary.

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Other research topics

Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.