One Readout, Many Repairs: Diffusion-Guided Hierarchical Search for Tool-Agent Repair

arXiv:2609.34879 · cs.AI, cs.CL · Submitted 2026-09-28 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-09-28

Updated: 2026-09-29

Code: https://github.com/X-Xia0828/ReCommit

Terminology

• Pruning — 31× in this paper · explained in 4 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Grounding — 9× in this paper · explained in 3 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Diffusion Models — 4× in this paper · explained in 6 episodes
• Abstraction — 3× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic Workflows — 1× in this paper · explained in 1 episode
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
• Qwen3 Technical Report
• Beyond Function Calling: Benchmarking Tool-Using Agents under Tool-Environment Unreliability
• Dream 7B: Diffusion Large Language Models
• When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents
• Where LLM Agents Fail and How They can Learn From Failures

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