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A framework for evaluating vulnerability reports via probes, executable checks of security properties, which surfaced 23 previously unreported vulnerabilities, the majority of which have been confirmed by maintainers.
BenchShield is presented, a model-backed instrumentation layer for reward integrity in LLM-agent evaluation that grounds detection in a finite lifecycle model of an evaluation's reward-relevant events and achieves 96% accuracy in detecting reward hacking from infrastructure-side evidence.
EvoSafeHarness is a safety-specific optimization framework that synthesizes a deployable harness for a frozen model in a target domain, guided by model behavior, domain specifications, and fresh-context adversarial review to reject benchmark-specific rules.
AI agents can only correctly synthesize implementations and proofs for less than two-thirds of tested multi-module software repositories, revealing significant gaps in current capabilities.
