arXiv: cs.AI preprintsInternational6 November 2026
SKIM: Pruning Large Language Model Agents via Selective Knowledge Informed Masking
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arXiv:2611.07558v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly deployed under structured generation, where grammar constraints enforce output format at inference time. Whereas existing compression methods target unconstrained generation, we first identify a fundamental mismatch: under structured generation, grammar constraints reduce the model's output distribution to valid tokens, and the logits matter most at the small set of positions that decide the agent's action. Motivated by this finding, we propose SKIM (Selective Knowledge-Informed Masking), the f
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