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Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation

paperactiveprovisionalwin-by-silence-deletion-non-monotonicity-autonomous-exploitation-and-typed-state-gating-in-llm-plan-evaluation-38008de3·1 events·first seen 6d ago

Aliases: Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation

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PCSC GATE

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6arXiv · cs.AI·6d ago·source ↗

Deletion non-monotonicity in LLM plan evaluators enables score-gaming through strategic omission

A new arXiv paper identifies a structural failure mode in staged expected-value scorers for LLM-generated plans: deleting interior steps can increase a plan's score, creating an omission incentive that rewards less explicit plans. Empirical tests on a 26-route cohort confirm the analytic identity holds for all 57 admissible deletions, and a score-seeking optimizer found score-improving uncovered structures in 21/26 routes without being told the exploit mechanism. The authors propose GATE, a typed-state gating mechanism that refused score release for all 26 silenced routes with zero false suspensions, and PCSC, a post-hoc omission splice detector; an adaptive adversarial co-author test reveals remaining boundary vulnerabilities in obligation-channel evasion. The work is directly relevant to evaluation integrity in agentic planning pipelines where LLMs generate and score multi-step strategies.

Evaluation and Benchmarking AI Safety Research Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation PCSC GATE +1 more