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arXiv · 2606.05728

DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance

Yansi Li·Zhuosheng Zhang
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Abstract

Generating executable tool plans requires selecting appropriate subsets from tool libraries, a combinatorial search problem with an exponentially large solution space. However, we identify a critical misalignment in predominant approaches: standard autoregressive (AR) decoding suffers from early commitment, where initial token choices rigidly constrain the search trajectory. A controlled study shows that masked denoising raises Pass@10 solution coverage from 0.320 to 0.943 over AR sampling under matched compute. Motivated by this, we propose DiG-Plan, a framework that decouples combinatorial exploration from structural refinement. DiG-Plan employs a diffusion-based proposer to generate diverse tool sets via iterative refinement, followed by an AR refiner for dependency prediction. On TaskBench, DiG-Plan improves over AR baselines by a 10% relative margin, with the largest gains on complex compositional tasks; API-Bank results show that the propose-refine-select design remains effective across domains. Code is available at https://github.com/puddingyeah/DiG-Plan.

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Yansi Li, Zhuosheng Zhang. 2026-06-04. DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance. https://arxiv.org/abs/2606.05728

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