arXiv AI
Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents
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arXiv AI
Jul 3
Repair the Amplifier, Not the Symptom: Stable World-Model Correction for Agent Rollouts
arXiv:2607. 01767v1 Announce Type: new Abstract: As agent planning moves from short tool chains toward persistent workflows with thousands or tens of thousands of steps, failures will occur inside large planning graphs rather than in isolated predictions.
arXiv AI
Oct 9
Plan-and-Patch: Diffusion Language Models for Agentic Planning
Plan-and-Patch is a plan‑and‑act framework that uses a diffusion language model to generate structured, program‑like plans and then repairs them by filling in selected regions while keeping the surrounding steps fixed. The approach avoids regenerating entire plans, reducing unnecessary changes. Experiments show that on the Natural Plan benchmark, diffusion planners achieve a 53.7% plan‑repair success rate versus 27.0% for autoregressive planners, and after task‑specific training on ALFWorld and TextCraft, diffusion planners match autoregressive planners in plan generation success while cutting mean plan‑generation latency by 39–46%.
arXiv AI
Jul 22
PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents
arXiv:2607. 18859v1 Announce Type: new Abstract: While Large Language Models have greatly advanced automated issue resolution, existing agent-based methods exhibit a fundamental limitation in their insufficient exploration of repair strategies.
arXiv AI
Sep 30
One Readout, Many Repairs: Diffusion-Guided Hierarchical Search for Tool-Agent Repair
arXiv:2609.34879v2 Announce Type: replace Abstract: Tool agents use large language models to act through external tools, yet successfully executed calls can still leave user requests unfulfilled. Too...
arXiv AI
Jul 16
Experience Memory Graph: One-Shot Error Correction for Agents
arXiv:2607. 13884v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, actions, and observations.
arXiv AI
Aug 10
Coupling Planning with Episodic Memory in LLM Agents for Software Issue Resolution
arXiv:2608. 06811v1 Announce Type: cross Abstract: Resolving a real software issue with a large language model (LLM) agent is a long repair episode, often tens to hundreds of steps spanning exploration, hypothesis, implementation, and verification.