DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance
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. Experiments on TaskBench and API-Bank show that DiG-Plan outperforms AR baselines by a 10% relative margin, with the largest gains on complex compositional tasks.
BibTeX
@inproceedings{ijcai2026_digplanmitigatin,
title = {DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance},
author = {Yansi Li and Zhuosheng Zhang},
booktitle = {IJCAI 2026},
year = {2026}
}