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After an Agent Completes an Audit and Repair, Its False-Positive Rate in the Next Verification Falls by Up to 11.5 Percentage Points

New research shows that even when the content being verified is identical, prior auditing and repair experience retained in context causes several open models to flag fewer errors. Signal detection analysis found no corresponding improvement in discriminability, suggesting that the agent pipeline changes the verification threshold rather than simply making the model more accurate.

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Quipu Changes Agent Writes to Knowledge Graphs from Accept-Then-Clean to Validate-Before-Commit, with Rejected Records Replayable Across Versions

Quipu places data, trust labels, rules, and decisions on dual timelines, and checks candidate state before committing agent writes. In the authors’ injected-defect test, all six defects persisted without a gate, compared with zero under Quipu, though the evidence still comes mainly from a single deterministic evaluation designed by the system’s authors.

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CompoSkill Shows Agent Skills That Pass Individual Scans Can Still Form Risk Chains at Rates of Up to 80% When Composed

CompoSkill reframes skill security from evaluating individual packages to analyzing paths involving cross-skill data flows and side effects. Across its 1,140 test cases, the highest risk-chain formation rates for white-box and black-box attacks reached 83.3% and 80.6%, respectively, but these figures are not equivalent to real-world compromise success rates.

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Ten Thousand Agent Communities Exhibit Three Phases—Consensus, Polarization, and Apathy—With the Ising Model Predicting Opinion Evolution

A Stanford team simulated more than 10,000 communities of language-model agents and found that, over multiple rounds of communication, initially weak opinions gradually develop into consensus or polarization. Using an extended Ising model, the researchers predicted individual trajectories on previously unseen communication graphs. Political questions, however, also produced a rightward shift, showing that collaboration does not necessarily eliminate model bias.

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JailbreakSkill Packages Jailbreaking Strategies as Evolvable Skills, Raising HarmBench ASR by Another 13.4 Percentage Points

JailbreakSkill packages prompt rewriting, failure analysis, and skill evolution behind a unified interface, enabling red-team agents to generate new procedures from unsuccessful attack trajectories. Its second stage raises the cross-model average attack success rate on AdvBench and HarmBench by a further 17.5 and 13.4 percentage points, respectively, although evaluation relies on a GPT-4o judge.