SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents
Large language model (LLM) agents increasingly rely on agent harnesses that manage context, tools, and multi-turn execution, making tools a central interface for acting in realistic digital environments. As harness-connected tool ecosystems expand to hundreds or thousands of APIs, services, and task-specific skills, exhaustive tool schema injection becomes costly and imposes a closed-world assumption that limits agents to a predefined static inventory. Retrieval-augmented tool selection offers a natural alternative, but existing one-shot retrieval methods often fail to align isolated tool descriptions with the agent's true task intention, especially in long-horizon tasks where required ca...
| Authors | Qiao Xiao, Haochen Shi, Yisen Gao et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 16 June 2026 |
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