Researchers have developed SkillPager, a novel two-stage framework designed to improve the efficiency of large language model (LLM) agents when processing long procedural documents. The system parses skill documents into semantic nodes offline and then uses Maximal Marginal Relevance (MMR) for query-conditioned node selection online. This approach significantly reduces the number of tokens required for prompting while maintaining high context sufficiency, outperforming existing graph-based baselines. AI
IMPACT Enhances LLM agent efficiency in handling complex documents, potentially reducing inference costs and improving task completion.
RANK_REASON This is a research paper detailing a new method for improving LLM agent performance. [lever_c_demoted from research: ic=1 ai=1.0]
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