LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents
Summary
LatentSkill converts textual skills into LoRA adapters stored in weight space, reducing context overhead while maintaining modularity and composability for LLM agents, achieving significant improvements on ALFWorld and Search-QA benchmarks.
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Paper page - LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents
Source: https://huggingface.co/papers/2606.06087 Authors:
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Abstract
LatentSkill enables efficient deployment of textual skills in agent systems by converting them into LoRA adapters stored in weight space, reducing context overhead while maintaining modularity and composability.
Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext. We present LatentSkill, a framework that converts textual skills into plug-and-playLoRA adaptersthrough a pretrainedhypernetwork. LatentSkill stores skill knowledge inweight spacerather thancontext space, removing per-step skill tokens while preserving modular loading, scaling, and composition. On ALFWorld and Search-QA, LatentSkill outperforms the corresponding in-context skill baseline while using substantially fewer prefill tokens: it improves ALFWorld success by 21.4 and 13.4 points on the seen and unseen splits with 64.1% fewer prefill tokens, and improves Search-QA exact match by 3.0 points with 72.2% lower skill-token overhead. Further analysis shows that generated skill LoRAs form a structuredsemantic geometry, can be precisely controlled via theLoRA scaling coefficient, and can be composed throughparameter-space arithmeticwhen skill components are aligned. These findings suggest that weight-space skills provide an efficient, modular, and less exposed substrate for extending LLM agents.
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