RubricRefine: Improving Tool-Use Agent Reliability with Training-Free Pre-Execution Refinement

Abstract

Iterative self-refinement is a popular inference-time reliability technique, but its effectiveness in code-mode tool use depends heavily on the structure of the feedback signal: unstructured critique helps inconsistently across models, and even revision with real execution feedback improves only modestly ($0.75$ vs. $0.65$ baseline). The dominant failures are inter-tool contract violations (wrong output shape, incorrect tool routing, broken argument provenance) that run to completion without raising errors, making runtime feedback insufficient. We introduce RubricRefine, a training-free method for pre-execution semantic contract verification that generates task- and registry-specific rubrics, scores candidate code against explicit contract checks, and iteratively repairs failures before any execution occurs. RubricRefine reaches $0.86$, averaged across seven models, on M3ToolEval with zero execution attempts, improving over prior inference-time baselines with up to $2.6\times$ lower latency. Performance remains flat on the predominantly single-step API-Bank, consistent with the method's reliance on inter-tool contract structure. A rubric-category ablation and calibration analysis further characterize when and why the method works.