Aligning Agentic World Models via Knowledgeable Experience Learning

arXiv:2601.13247 · cs.CL, cs.AI, cs.CV, cs.LG, cs.MM · Submitted 2026-01-19 · Read on arXiv

cs.CL, cs.AI, cs.CV, cs.LG, cs.MM

Submitted: 2026-01-19

Updated: 2026-08-28

Comments: EMNLP 2026 Findings

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• World Model — 76× in this paper · explained in 7 episodes
• Alignment — 15× in this paper · explained in 1 episode
• Benchmark — 13× in this paper · explained in 2 episodes
• Hallucination — 11× in this paper · explained in 3 episodes
• Success Rate — 10× in this paper
• World Knowledge Repository — 10× in this paper
• Accuracy — 6× in this paper · explained in 1 episode
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Grounding — 5× in this paper · explained in 3 episodes
• Abstraction — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Transferability — 4× in this paper · explained in 4 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Agentic AI — 3× in this paper · explained in 1 episode
• Benchmarking — 3× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• Predictive Coding — 3× in this paper · explained in 2 episodes
• Verification — 3× in this paper · explained in 2 episodes
• Verifier — 3× in this paper · explained in 2 episodes
• World Knowledge Model — 3× in this paper
• AI Agents — 2× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Action Space — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Agentic System — 2× in this paper · explained in 2 episodes
• Embodied AI — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Goal-Conditioned Success — 2× in this paper
• Interpretability — 2× in this paper · explained in 15 episodes
• Multi-Agent System — 2× in this paper · explained in 5 episodes
• Multi-agent Collaboration — 2× in this paper · explained in 2 episodes
• Self-Evolving Agents — 2× in this paper · explained in 2 episodes
• Agentic Problem — 1× in this paper · explained in 1 episode
• Agentic Task — 1× in this paper · explained in 1 episode
• Agentic Tasks — 1× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Memory Augmentation — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Physical Constraints — 1× in this paper · explained in 7 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Task Planning — 1× in this paper · explained in 2 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Agentic Learner with Grow-and-Refine Multimodal Semantic Memory
• Building Self-Evolving Agents via Experience-Driven Lifelong Learning: A Framework and Benchmark
• FLEX: Continuous Agent Evolution via Forward Learning from Experience
• Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution
• CWM: An Open-Weights LLM for Research on Code Generation with World Models
• AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents
• WorldVLA: Towards Autoregressive Action World Model
• Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation
• Web World Models
• Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
• SkyReels-V2: Infinite-length Film Generative Model
• Why Do LLM Agents Fail in Exploring New Environments? A World-Modeling Perspective
• WoW: Towards a World omniscient World model Through Embodied Interaction
• Embodied AI Agents: Modeling the World
• A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
• General Agentic Planning Through Simulative Reasoning with World Models
• Search, Verify and Feedback: Towards Next Generation Post-training Paradigm of Foundation Models via Verifier Engineering
• EvaLearn: Quantifying the Learning Capability and Efficiency of LLMs via Sequential Problem Solving
• World Models
• WorldScore: A Unified Evaluation Benchmark for World Generation

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