Skip to content

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

Jul 2026 · arXiv.org · Vol abs/2607.12986 · 0 citations · 35 references
Computer Science

Abstract

Plan evaluators can reward a strategic plan for becoming less explicit. This paper studies that failure in a staged expected-value scorer for LLM-generated venture routes. Proposition 1 gives the score change from deleting an interior transition while retargeting its predecessor and retaining downstream value: Delta_k = (prod_{i<k} p_i)[c_k + (1 - p_k)R_{k+1}]. On a frozen 26-route cohort, all 57 admissible deletions matched the analytic identity and threshold sign, and every route had at least one score-improving deletion. A score-seeking optimizer, allowed to restructure routes but not told the exploit mechanism, found baseline-beating uncovered structures in 21/26 routes. GATE refused score release for 26/26 silenced routes with 0/26 honest suspensions; after refusal, 47/54 next revisions repaired to a covered structure, and strict covered improvement rose from 1/26 to 13/26. An adaptive compiler-aware co-author exposed the registry-provenance boundary: obligation-channel evasions remained 6/6 across all four v1/v1.5 conditions, while delta-indexed cost floors reduced beat-honest routes from 6/6 to 3/6 and fundability-by-silence from 5/6 to 0/6 without establishing semantic completeness. If a plan scores better only because it omits necessary work, the plan did not improve; the evaluation created an omission incentive. PCSC detects and neutralizes post-hoc omission splices over model-mediated typed-state records. In the cooperative setting tested, GATE acts as a deterministic search-shaping constraint, not merely a post-hoc filter. It does not verify the semantic completeness or real-world quality of arbitrary LLM-generated strategies.

View source

Similar papers

#artificial intelligence Preprint Sep 2026

How Do Agent Harnesses Create Value? Planning Information and Release Control in Stateful LLM Agents

Which component matters more depends on the loss assigned to erroneous acceptance: at low liability the planning gain dominates; at high liability the verifier's avoided false passes dominate; and a standalone verifier captures nearly all the false-pass benefit of the full planning-plus-verification stack at a fraction...

Yu-Kun Zhang, Ke-Mu Xu, Yi-Shen Chen · 2 citations
Preprint Aug 2026

The Replay Gap: Static Evaluation of Model Switching in LLM Agents Scores the Wrong World

This work forks live SWE-bench agent trajectories at controlled points, rebuild the environment, continue each fork with a different model, and compare against same-model control forks that isolate sampling and replay noise, finding configuration-dependent temperature-0"determinism" is configuration-dependent.

Ashritha Gonuguntla · 8 citations · ⚡2
Book Open access Aug 2026

Pre-Balancing Structural Risk Screening for Crafting Economy Design

A claim-bounded pre-balancing method that separates what pre-numeric structure and categorical source/sink annotations can exclude, what they can only flag, and what requires later numeric analysis is presented.

G. Park · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.