HYSET: Set-Level Tool Retrieval for LLM Agents via Hyperedge Prediction
A new technique called HYSET (HYperedge-based SEt-level Tool retrieval) has been introduced by researchers for the retrieval of tools intended for large language model (LLM) agents. In contrast to traditional methods that assess tools one at a time or in a sequence, HYSET analyzes the overall effectiveness of a proposed set of tools by treating the retrieval process as query-conditioned hyperedge prediction within a tool co-invocation hypergraph. This approach accounts for tool compatibility that depends on size through interactions specific to cardinality. The research can be found on arXiv with the ID 2607.25718.
Key facts
- HYSET stands for HYperedge-based SEt-level Tool retrieval.
- Tool retrieval selects a task-relevant subset from a library of thousands of tools.
- Existing retrievers score each tool in isolation or assemble the set sequentially.
- HYSET formulates tool retrieval as query-conditioned hyperedge prediction.
- It uses a tool co-invocation hypergraph.
- The method captures size-dependent tool compatibility through cardinality-specific interactions.
- The paper is published on arXiv with ID 2607.25718.
- The approach evaluates joint utility of a candidate set as a whole.
Entities
Institutions
- arXiv