LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

LatentSkill is a novel framework that transforms textual agent skills into modular LoRA adapters using a pretrained hypernetwork, shifting reusable procedural knowledge from context space to weight space for LLM agents. This approach eliminates the substantial context overhead and plaintext exposure associated with injecting skills directly into prompts. Evaluated on ALFWorld and Search-QA benchmarks using a Qwen3-8B backbone, LatentSkill significantly outperforms in-context skill baselines. It boosts ALFWorld success by 21.4 and 13.4 points on seen and unseen splits, respectively, while reducing prefill tokens by 64.1%. On Search-QA, it improves exact match by 3.0 points with 72.2% lower skill-token overhead. The framework also demonstrates that generated skill LoRAs exhibit a structured semantic geometry, are precisely controllable via the LoRA scaling coefficient α, and can be composed through parameter-space arithmetic when skill components are aligned. Furthermore, LatentSkill shows enhanced robustness against skill text perturbations and prompt-level attacks.

Key takeaway

For AI Engineers developing LLM agents with extensive skill sets, you should consider adopting LatentSkill's weight-space skill representation. This approach significantly reduces inference costs by eliminating repeated skill text in prompts and enhances security against prompt injection. You can also dynamically control skill influence with the LoRA scaling coefficient and compose complex behaviors by aligning skill components in parameter space.

Key insights

LatentSkill converts textual agent skills into modular LoRA adapters via a hypernetwork, storing procedural knowledge in weight space for efficiency and control.

Principles

• Weight-space skills reduce context overhead and plaintext exposure.
• Generated skill LoRAs form structured semantic geometry.
• Skill injection strength is precisely controllable via α.

Method

A hypernetwork-based skill compiler maps textual skills to LoRA adapters. This compiler is pretrained on skill documents and fine-tuned with teacher trajectories. At inference, generated adapters are mounted on the LLM, with an optional scaling coefficient α.

In practice

• Implement agent skills as LoRA adapters to minimize prompt token usage.
• Dynamically adjust skill influence using the LoRA scaling coefficient α.
• For complex behaviors, decompose skills into components for parameter-space composition.

Topics

• LLM Agents
• LoRA Adapters
• Hypernetworks
• Context Window Optimization
• Skill Composition
• Prompt Security

Code references

• yuaofan0-oss/LatentSkill

Best for: Research Scientist, AI Architect, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

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• LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents — LatentSkill converts LLM agent textual skills into efficient, modular, and composable weight-space LoRA adapters, reducing context overhead.
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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.