CauterRule v0.3.1 Fixes Token-Matching Bug, Boosts AI Agent Rule Pass Rate by 40 Points

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Open-source tool CauterRule has released version 0.3.1, addressing a critical flaw in how its scoring system evaluated standing rules learned from repeated AI agent failures. The bug caused the scorer to treat any success sharing a keyword token — such as 'git' — with a failure rule as a broken success, even when the two were functionally unrelated. This lexical over-matching inflated broken-success counts and caused valid, net-positive rules to be incorrectly downgraded as inconclusive. The fix introduced a semantic precision threshold, requiring a rule to prevent more failures than it breaks successes before being promoted. Tested across 4,742 trajectory-runs on two cloud models, the update raised pass rates from roughly 8–10% to approximately 50–52%.

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