ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution (2026.acl-long)
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| Challenge: | Existing methods for large language models struggle to align user intent with tool semantics or generalize to unseen tools. |
| Approach: | They propose a framework that enables LLMs for open-world tool use by proactive retrieval and grounded execution within a reasoning loop. |
| Outcome: | The proposed framework surpasses baselines in retrieval and execution efficiency by +10.8%. |
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