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...
| Authors | Paul Simpson, John Kozak, Lisa Doake |
|---|---|
| Primary category | cs.AI |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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