Agent-Editing World Model: Rethinking World Modeling for LLM Agents

arXiv:2609.28416 · cs.CL, cs.AI, cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.CL, cs.AI, cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

Code: https://github.com/AweAI-Team/AweAgent

Terminology

• World Model — 132× in this paper · explained in 7 episodes
• Agent-Editing World Model — 88× in this paper
• Action Judge — 70× in this paper
• Benchmark — 60× in this paper · explained in 2 episodes
• Verification — 46× in this paper · explained in 2 episodes
• State Revision — 44× in this paper
• Fine-Tuning — 15× in this paper · explained in 5 episodes
• Verifier — 15× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Ablation — 11× in this paper · explained in 1 episode
• Alignment — 10× in this paper · explained in 1 episode
• Information Gain — 10× in this paper · explained in 2 episodes
• Adaptation — 8× in this paper · explained in 1 episode
• Core — 7× in this paper · explained in 2 episodes
• Reinforcement Learning — 6× in this paper · explained in 4 episodes
• Redundancy — 5× in this paper · explained in 2 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Supervised Fine-Tuning — 4× in this paper
• Generalization — 3× in this paper · explained in 13 episodes
• Information Leakage — 3× in this paper · explained in 2 episodes
• Two-stage training — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Scaffolding — 2× in this paper · explained in 3 episodes
• Supervised Fine-Tuning (SFT) — 2× in this paper · explained in 8 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Agent Interactions — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Embodied AI — 1× in this paper · explained in 2 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Provenance — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
• ClawGym: A Scalable Framework for Building Effective Claw Agents
• Toward Autonomous Long-Horizon Engineering for ML Research
• BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?
• Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence
• DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
• SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
• NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents
• CWM: An Open-Weights LLM for Research on Code Generation with World Models
• GLM-5: from Vibe Coding to Agentic Engineering
• World Modelling Improves Language Model Agents
• DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agents
• Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
• Discriminative World Models for Web Agents
• Simulating Environments with Reasoning Models for Agent Training
• CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks
• Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
• World-Model-Augmented Web Agents with Action Correction
• ECHO: Terminal Agents Learn World Models for Free

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