LLMs Know the Constraint But Do Not Use It: Activation Bottlenecks in Pragmatic Constraint Reasoning
This paper argues that when salient surface cues conflict with implicit constraints, LLM failures stem not from missing knowledge but from failing to route encoded constraints into decisions. Using a conditional constraint activation framework and experiments on 14 models, the authors show hidden-constraint failure is a routing problem, and existing prompt interventions only inflate conservative bias.
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[Submitted on 29 May 2026]
Title:LLMs Know the Constraint But Do Not Use It: Activation Bottlenecks in Pragmatic Constraint Reasoning
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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.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.12321 [cs.CL]
(or arXiv:2608.12321v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.12321
arXiv-issued DOI via DataCite
Submission history
From: Yubo Li [view email] [v1] Fri, 29 May 2026 01:42:07 UTC (102 KB)
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