RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinement
This work addresses the frequent failures of tool-calling agents in code mode caused by inter-tool contract violations—such as incorrect output formats, routing errors, or broken parameter provenance—which often lack runtime exceptions and thus hinder execution-based self-correction. The authors propose a training-free, pre-execution reliability enhancement method that introduces, for the first time, a scoring-rule-based contract validation mechanism. By adaptively generating scoring rules from task and tool registries, the approach performs static contract checks on candidate code and iteratively repairs violations—all without any actual execution. Evaluated on the M3ToolEval benchmark, this method achieves an average accuracy of 0.86, surpassing all existing reasoning-time baselines, while incurring only 38% of the latency of the strongest non-iterative alternative and demonstrating consistent effectiveness across seven diverse models.