Language models fail at extended rule following
Large language models are highly capable of answering difficult questions by retrieving, recombining, and attending to information in long contexts. For agentic tasks, an additional capability is required: the preservation of an exact state while repeatedly applying rules. We find that this reliability is absent across language models. To demonstrate, we query 126 leading model variants with the task of counting a long string of repeated characters, and we find they all cannot accurately count above a model-dependent, syntax-sensitive counting capacity threshold. Failures are abrupt and persist even with increasing model size, inference time computation, and external tool. Mechanistic pro...
| Authors | Tianxiang Dai, Jonathan Fan |
|---|---|
| Primary category | cs.CL |
| Published | 3 May 2026 |
| Last updated at source | 16 May 2026 |
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