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

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, extended

Summary

DiG-Plan, a framework for tool-graph planning, addresses the "early commitment" problem in autoregressive (AR) decoding for combinatorial tool selection. A controlled study demonstrated that masked denoising significantly improved Pass@10 solution coverage from 0.320 to 0.943 over AR sampling under matched compute. Motivated by this, DiG-Plan decouples combinatorial exploration from structural refinement, employing a diffusion-based proposer to generate diverse tool sets via iterative refinement, followed by an AR refiner for dependency prediction. The framework also incorporates a judge-free value function for optimal candidate selection. On TaskBench, DiG-Plan achieved a 10% relative improvement over AR baselines, with the largest gains observed on complex compositional tasks, and showed cross-domain effectiveness on API-Bank.

Key takeaway

For AI Scientists or Machine Learning Engineers developing tool-augmented LLMs, recognize that standard autoregressive decoding limits combinatorial tool-set exploration due to early commitment. You should consider adopting a propose-refine-select framework like DiG-Plan. This involves using diffusion models for diverse tool-set generation and autoregressive models for dependency prediction. Implement a judge-free value function to select optimal plans, improving performance by 10% on complex tasks.

Key insights

Autoregressive decoding's "early commitment" limits tool-set exploration; diffusion models offer superior combinatorial search.

Principles

• Autoregressive decoding struggles with combinatorial search due to early commitment.
• Decoupling tool-set exploration from dependency prediction improves planning.
• Diffusion models enhance diversity in combinatorial search spaces.

Method

DiG-Plan uses a diffusion proposer for diverse tool sets, an AR refiner for dependency edges, and a judge-free value function to select the best plan.

In practice

• Use diffusion models for diverse candidate tool-set generation.
• Employ AR models for structured dependency prediction given fixed tool sets.
• Implement a judge-free value function for optimal plan selection.

Topics

• Tool-Augmented LLMs
• Diffusion Models
• Autoregressive Decoding
• Tool-Graph Planning
• Combinatorial Search
• TaskBench

Code references

• puddingyeah/DiG-Plan

Best for: Research Scientist, NLP Engineer, AI Scientist, Machine Learning Engineer, AI Engineer

Related on AIssential

• DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance — DiG-Plan uses diffusion guidance to overcome early commitment in tool-graph planning, improving solution diversity and performance.
• From Heuristic Selection to Automated Algorithm Design: LLMs Benefit from Strong Priors — Integrating benchmark code examples into prompts significantly enhances LLM-driven algorithm optimization performance and robustness.
• Can I Have Your Order? Monte-Carlo Tree Search for Slot Filling Ordering in Diffusion Language Models — MCTS-guided slot ordering in diffusion models significantly improves reasoning task performance by balancing sequential and non-sequential generation.
• AutoTool: Dynamic Tool Selection and Integration for Agentic Reasoning — LLM agents can achieve dynamic tool selection and superior generalization by optimizing tool preferences via Plackett–Luce ranking.
• TiDAR: Think in Diffusion, Talk in Autoregression (Paper Analysis) — TiDAR accelerates LLM inference by leveraging unused GPU capacity for parallel diffusion-based token drafting and autoregressive validation.
• QueryWeaver: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation — QueryWeaver transforms natural language into structured graphs for reliable multi-tool query execution using a deterministic, dependency-resolving planner.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.