这篇论文揭露了GPT-5.4和Claude在资格评估规则中会莫名崩溃，还给了个能稳定执行的神经符号方案，搞合规或应用LLM的得看看。
论文《Confidently Wrong》揭示前沿LLM在嵌套条件规则（如“要求A，除非B，除非C覆盖B”）的错误模式——异常链崩溃。GPT-5.4在建筑保险场景的准确率从96.6%突然升至100%（同一提示和测试框架），表明模型准确率边界不可控。作者提出Aethis Eligibility Module，由LLM从权威源编写规则、SMT层确定性执行。在225个跨领域场景基准中复现该模式，20个对抗场景中引擎与GPT-5.4（低推理努力）均获20/20，而其他三个前沿模型失败。在949个LegalBench任务中，引擎比三个前沿模型显著更准确（组合McNemar p≤0.003），最高领先Anthropic模型41个百分点。
Confidently Wrong: Exception Chain Collapse in Frontier LLM Rule Evaluation
We document a failure class in frontier large language models -- exception chain collapse -- observed in eligibility evaluation under nested conditional rules of the form "A is required UNLESS B applies, UNLESS C overrides B". The failure reproduces at first observation, but its empirical surface is unstable: between March and April 2026 several failure cells closed silently under the same model alias, with no version bump (GPT-5.4 on construction insurance moved from 96.6% to 100%, same prompt and harness). For regulated workflows, frontier-model accuracy is a moving compliance boundary that shifts without notice. We present the Aethis Eligibility Module, a neuro-symbolic architecture in which LLMs author rules from authoritative sources and an SMT-based layer executes them deterministically, consistent with the authored specification regardless of model drift, reasoning-effort defaults, or prompt format. Three evidence bases: (i) a controlled benchmark of 225 scenarios across four regulatory domains documents the pattern and, in replication, the drift that partially closed it; (ii) a 20-scenario adversarial extension on construction insurance, where the engine scores 20/20, as does one of four frontier configurations (GPT-5.4 at low reasoning effort), while the other three, including Anthropic's strongest model at evaluation time, fail the same coverage-gap edge case; (iii) external validation on nine peer-reviewed LegalBench tasks, 949 held-out cases, where the engine is significantly more accurate than all three frontier models (combined McNemar's p <= 0.003), with margins up to +41 points on the curated multi-prong tasks against the Anthropic models. The contribution is to relocate uncertainty from the inference boundary, where it is silent, to the specification boundary, where it is deliberate and audited. All scenarios, rule encodings, and results are public and reproducible.