Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models
Summary
SKIM is an adaptive multi-resolution soft token compression framework that compresses procedural skills for LLMs, maintaining task performance while reducing prefill cost and latency.
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Paper page - Adaptive Multi-Resolution Procedural Knowledge Compression for Large Language Models
Source: https://huggingface.co/papers/2606.12203
Abstract
SKIM is an adaptive multi-resolution soft token compression framework that efficiently compresses procedural skills while maintaining task performance and enabling lightweight offline compression for frequently updated community skills.
Large language models(LLMs) are widely used to tackle complex tasks with autonomous workflows. Recently,reusable natural language skillshave emerged as a popular paradigm to injectprocedural knowledgeinto LLM applications. Since popular skills are often invoked repeatedly, placing their full text in every context significantly increases prefill cost and latency. Whiletext compressiontechniques have the potential to solve this problem, most existing methods are designed to compress factual knowledge in documents instead ofprocedural knowledge, making them insufficient forskill compression. In this paper, we argue that an effectiveskill compressionmethod should: 1) preserve logical dependencies among workflows and tool protocols, 2) enable lightweight, offline compression for frequently updated community skills, and 3) be adaptable to varying complexities across skills. To address this, we present SKIM (SKIll coMpression), an adaptive multi-resolution soft token compression framework for procedural skills. Depending on the complexity of each skill, SKIM creates different numbers ofsoft tokensthat not only improve the efficiency ofLLM inference, but also preserve the effectiveness of skill usage. Experiments indicate that SKIM compresses skills to 30 to 60 percent of their original token length while preservingtask performancebetter than existing compression methods.We have released our code at https://github.com/bebr2/SKIM .
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