Closed-World Resolution Against Tool Hallucination in LLM Agents
Solving the persistent issue of LLM agents hallucinating non-existent tool calls and schema violations.
Tool-augmented large language model (LLM) agents fail in a way no tool-selection or tool-security method addresses: they call tools that do not exist and pass arguments no schema declares. Existing defenses either pick the right tool (selection) or constrain what an agent may do ...
ArXiv CS.AI
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This research addresses the critical stability gap in LLM agents by preventing them from hallucinating non-existent tools and malformed parameters. By establishing a closed-world framework, developers can ensure agents adhere strictly to defined capabilities, significantly improving reliability in enterprise workflows.
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