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Plan-and-Patch: Diffusion Language Models for Agentic Planning
Publication: cs.AI updates on arXiv.org
Author: Syamantak Kumar, Jiang Guo, Hassan Hamad, Hideo Kobayashi, Yi Xiang, Yezhou Yang, Yanjun Qi, Daniele Bonadiman, Jiarong Jiang
Source published: 09/10/2026, 04:00:00 UTC
Briefing created: 09/10/2026, 04:31:59 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 04:03:24 UTC.
Briefings
Plan-and-Patch introduces a diffusion language model framework for repairing long-horizon agent plans by preserving context and regenerating only affected regions. The study compares DreamReasoner-8B and Qwen3-8B on Natural Plan without training, where diffusion achieved 53.7% repair success versus AR's 27.0%. After task-specific training on agentic benchmarks, both models showed similar generation success, but diffusion reduced mean generation latency by 39-46% relative to autoregressive planning.
Key points
- Plan-and-Patch uses a diffusion language model to generate structured plans via parallel unmasking and targeted repairs while preserving surrounding context.
- On Natural Plan without training, diffusion planning achieved 53.7% plan repair success rate compared to 27.0% for autoregressive planning.
Context and limitations
Results are from a preprint (arXiv:2610.10786v1) and are limited to Natural Plan without task-specific training; task-specific training showed similar generation success but diffusion still reduced latency by 39-46%.
Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 09/10/2026, 04:31:59 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.
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