The authors scoped their effort to Class D (fail-plausible) failures from a prior taxonomy, where internal errors produce fluent but false output to the user.
Automated observer catches fail-plausible LLM errors but misses novel ones
A two-layer pipeline with deterministic pre-filter and evidence-grounded LLM judge achieves perfect regression detection yet zero held-out recall; pre-registered live deployment fires no verdicts.
Independent
Wei Wu
Independent researcher
Research Digest··2 min read
The authors built an automated user-viewpoint observer targeting fail-plausible silent failures in a production LLM agent runtime.
Why this paper
From Independent researcher
In one line
A two-layer observer correctly identified all 6 historical fail-plausible incidents but missed all 4 novel patterns and produced 10 silent failures of its own.
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