ToolOmni combines supervised fine-tuning on a cold-start multi-turn dataset with Decoupled Multi-Objective GRPO to enable proactive retrieval and grounded execution, yielding +10.8% higher end-to-end tool-use success and better generalization to unseen tools.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A framework automates multi-agent system creation via LLM planning and two-stage agent recommendation, claiming higher recall than prior methods.
citing papers explorer
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ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution
ToolOmni combines supervised fine-tuning on a cold-start multi-turn dataset with Decoupled Multi-Objective GRPO to enable proactive retrieval and grounded execution, yielding +10.8% higher end-to-end tool-use success and better generalization to unseen tools.
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From Intent to Execution: Composing Agentic Workflows with Agent Recommendation
A framework automates multi-agent system creation via LLM planning and two-stage agent recommendation, claiming higher recall than prior methods.