这篇论文告诉你，你以为可靠的GPT-4o评估器可能几周后就失效了，它还给了你一个EPC框架来自己检查。
论文提出EPC诊断框架，包含多模态偏好崩溃指数(MPCI)、评估器耦合矩阵和JS散度(JSD)。在8种实验条件下（N=122次重复）发现耦合系数均值范围0.00-1.18，其中GPT-4o May、GPT-4o-mini、Qwen3.7-plus和DashScope 30r四个条件显示强耦合（N=36），而GPT-4o June、qwen-plus N=30等四个条件崩溃到近零。特别地，GPT-4o从5月到6月的版本漂移（N=8重复实验）导致结论反转，自评估条件97%为零（JSD=0.003）。
A Diagnostic Framework and Multi-Evaluator Audit of Evaluator-Driven Preference Dynamics in Self-Adapting LLM Agents
Measurements of proprietary LLM evaluators can become invalid within weeks -- we document one case and provide the diagnostic framework to detect it. We introduce EPC -- comprising the Multimodal Preference Collapse Index (MPCI), evaluator-indexed coupling matrix, and Jensen-Shannon divergence (JSD) -- and apply it across eight experimental conditions (N=112 main + N=10 ablation = 122 unique repetitions, all reported). Coupling coefficients range from 0.00 to 1.18 across per-condition means (CV approx 0.9, n=8 conditions). Four conditions show strong coupling (N=36; GPT-4o May, GPT-4o-mini, Qwen3.7-plus, DashScope 30r); four collapse to near-zero (N=76; GPT-4o June, qwen-plus N=30, symmetric LR, DeepSeek self-eval). The May-to-June GPT-4o drift -- an N=8 re-replication inverting the study's conclusion -- is the most informative measurement: a diagnostic instrument detecting its own instability demonstrates the fragility it was designed to measure. Self-evaluation (97% zero, JSD=0.003) consistently collapses, though floor effects are possible. Output-format confound analysis finds per-strategy aggregate rho=0.89 but per-instance rho=0.219 (p=0.093); PCI reported as preference-convergence metric. We release EPC with all data. The finding is not any single coupling magnitude but the pattern of version-conditional instability that makes single-snapshot evaluator studies unreliable.