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[ARTICLE · art-96281] src=arxiv.org ↗ pub= topic=large-language-models verified=true sentiment=· neutral

LLMs Know the Constraint But Do Not Use It: Activation Bottlenecks in Pragmatic Constraint Reasoning

A new arXiv study (2608.12321v1) finds that large language models (LLMs) often fail to use implicit feasibility constraints despite encoding them internally, identifying a routing problem rather than a knowledge gap. Testing 14 models, the authors show that probes on two open-weight models decode constraints with over 88% accuracy, yet activation patching repairs one model (+6.4 nats) but not the other (-0.07 nats). No prompted intervention fully mitigates the failure, as all inflate conservative bias through a single pathway: prerequisite mention.

read1 min views1 publishedAug 14, 2026

arXiv:2608.12321v1 Announce Type: new Abstract: When a salient surface cue competes with an implicit feasibility constraint, LLMs often fail -- but aggregate accuracy conflates genuine constraint inference with conservative defaulting. We formalize the distinction as conditional constraint activation: the constraint is internally encoded (Knowledge) symmetrically across constraint-present and -absent prompts (Symmetry), yet only sometimes routed into the decision (Routing) and repairable by a donor activation (Repair). A quartet diagnostic over 14 models reveals two failure modes; probes on two open weights decode the constraint above $88%$, yet activation patching repairs one ($+6.4$ nats) and not the other ($-0.07$). On a mitigation frontier, no prompted intervention reaches the repair corner: all inflate conservative bias through a single mediation pathway -- prerequisite mention. Hidden-constraint failure is a routing problem, not a knowledge problem.

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