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METHODS / RESULTS / LIMITATIONS

Evidence you can inspect.

AgentArena challenges generated patches with executable tests. This page separates recorded model results from hand-written fixture checks. A source citation alone never proves a defect.

01 / SUPPLIED HISTORICAL MODEL RUNS

Four bugs. Two configurations. No demonstrated Tavily uplift.

Recorded October 21, 2026. We reviewed the supplied assertion output against each seeded defect. These historical runs were not rerun during this update.

ConfigurationCasesKnown bugs reproducedMean timeCorrected controls
baseline44/414.91sNot evaluated
tavily44/417.33sNot evaluated

02 / CURATED FIXTURE SANITY CHECKS

Does each control distinguish buggy and corrected code?

These hand-written controls validate fixtures and corrected references; they do not measure an AI model or Tavily benefit. Runner: local pytest on trusted checked-in fixtures. Model calls: 0. Tavily calls: 0.

ScenarioOriginal tests, buggyBoundary control, buggyBoundary control, correctedFixture check
discountPassFailPassExpected difference observed
authenticationPassFailPassExpected difference observed
inventoryPassFailPassExpected difference observed
shippingPassFailPassExpected difference observed
redirectPassFailPassExpected difference observed
callbackPassFailPassExpected difference observed
Download actual control output ↗

03 / DOCUMENTATION-INFORMED SECURITY REVIEW

When URL parsing and security intent disagree.

The new Safe Redirect Review checks a post-login redirect helper. Its contract requires every returned URL to remain on the application’s origin. Tavily searches primary Python 3.11 documentation; Nemotron proposes tests with source IDs and exact supporting excerpts.

AgentArena verifies whether each quoted excerpt occurs in the retrieved source, then executes the same generated suite on both implementations. A quote match establishes provenance, not semantic correctness. A failed corrected control makes the result inconclusive.

No live Nemotron/Tavily result for this new case is claimed in this package. Use the live workbench after configuring the backend, or run the paired evaluation command in the README. Model knowledge may already cover the issue, so an improvement over baseline is not assumed.