Beyond Top-k Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
DSR reranks LLM agent skills with Determinantal Point Processes to favor complementary, non-redundant sets, improving multi-skill query coverage.
The paper proposes Diverse Skill Routing (DSR), a diversity-aware reranking framework for LLM agent skill routing that uses a Determinantal Point Process to balance query relevance and non-redundancy across large skill registries. DSR introduces a query-residual diversity kernel that penalizes redundant skill overlap while avoiding penalties arising only from shared query relevance. On the SkillRouter benchmark, DSR improves recall and full coverage over a strong pointwise reranking baseline, with the largest gains on multi-skill queries. The authors argue skill routing should be treated as complementary set selection, not just relevance ranking.
- DSR uses Determinantal Point Processes to balance relevance and skill non-redundancy.
- Query-residual diversity kernel penalizes redundant overlap without penalizing shared query relevance.
- Improves recall and full coverage over pointwise reranking on SkillRouter benchmark.
- Largest gains seen on multi-skill queries requiring complementary skill sets.
From the original article
1 min read at huggingface.co
Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redundant while complex tasks often require complementary skill sets. Existing skill routers typically rank candidates independently by query relevance, which can waste context budget on redundant skills.
By Wang Wei, Tiankai Yang, Samyadeep Basu · huggingface.co
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