Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
Skill usage can significantly enhance the ability of modern agent systems to complete complex tasks. However, the growing scale of skill libraries makes accurate skill selection increasingly challenging. In real-world scenarios, ambiguous semantic matching often arises between a specific task requirement and multiple generic yet semantically similar candidate skills. Moreover, existing methods tend to overlook the dynamic influence of task difficulty and skill applicability when selecting the optimal target skill set. To address these issues, we propose SkillReranker, an inference-time reranki
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- PossiblePossibly related (embedding) · 63%zjunlp/SkillX →
- PossiblePossibly related (embedding) · 53%activeloopai/hivemind →
- PossiblePossibly related (embedding) · 50%agenthatch/agenthatch →
- PossiblePossibly related (embedding) · 50%mehaksharma2949/India_runs_data_and_ai_challenge →
- PossiblePossibly related (embedding) · 49%ratel-ai/ratel →
- FuzzySimilar title/name (fuzzy) · 59%AgentCore-8B →
“Fuzzy title match (0.73): “Task Decomposition-Guided Reranking for Adaptive Agent Skill” ≈ “AgentCore-8B””
- LinkedLinked via arxiv author · 85%Yanping Chen →
“Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval”
- LinkedLinked via arxiv author · 85%Weijie Shi →
“Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval”