MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Reranking
MagicSelector is a framework designed to improve tool retrieval in AI agents by decomposing ambiguous user instructions into atomic subtasks and applying counterfactual rewards, progressive reranking, and dynamic Top-K selection. The method is evaluated on the new MTDTool benchmark for mobile multi-turn interactions, where it demonstrates significant improvements over state-of-the-art methods in tool retrieval accuracy, out-of-domain generalization, and token efficiency.
Why it matters: This work addresses a key challenge in AI agent development by improving the reliability and precision of tool selection, especially when handling ambiguous instructions and noisy retrieval scenarios.
Full story at: arXiv Information Retrieval ↗