模型评估奖励删减步骤的策略,反而诱导隐藏关键工作。
Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation
- 发现删除中间步骤可提升评分,因评分机制存在非单调性漏洞。
- 26条路线中每条都有评分提升的删减方案,21条被优化器找到更优结构。
- 新机制能识别并阻止事后补漏,适合改进智能体规划评估系统。
本文研究了基于预期价值的分阶段评分器在评估大模型生成的创业路径时存在的缺陷。理论推导出删除内部转移步骤后的评分变化公式 Δ_k = (prod_{i<k} p_i)[c_k + (1 - p_k)R_{k+1}]。在26条固定路线的测试集中,57次可行删减均符合分析公式与符号阈值;所有路线至少存在一次评分提升的删减。允许重构但不知机制的评分优化器,在21/26条路线中发现了优于基线的隐藏结构。GATE机制对全部26条被静默的路线拒绝评分,0次诚实暂停;拒评后47/54次后续修订修复为可见结构,严格可见改进从1/26升至13/26。自适应编译器感知合作者揭示了注册溯源边界:义务通道规避在四组v1/v1.5条件下始终为6/6;而基于Δ索引的成本下限使击败诚实路线的比例从6/6降至3/6,按沉默获得可行性比例从5/6降至0/6,但未实现语义完备性。若计划仅因省略必要工作而得分更高,则该计划并未真正改进,评估本身制造了省略激励。PCSC可检测并消除模型媒介的类型状态记录中的事后删减。在协作测试中,GATE表现为确定性搜索塑造约束,而非事后过滤器。其不验证任意大模型生成策略的语义完备性或现实质量。
原文摘要 · Abstract (English)
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.
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