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This work introduces ToolCompass, a post-training framework that guides tool trialing by organizing tool-call representations according to shared functions and jointly reduces intra-function variation across domains and increases inter-function separation.
Filtering out noise from reasoning traces can drastically improve hallucination detection in large reasoning models, leading to more reliable AI outputs.