Do we REALLY need an external world model? 🤔
Standard approaches often rely on heavy external simulators.
We agree with the view: The Agent itself is the World Model.
🌍 How to align agentic world models via experience learning?
We are excited to introduce our new work: "Aligning Agentic World Models via Knowledgeable Experience Learning"(WorldMind)🚀
🚧The Problem: LLMs possess vast semantic knowledge but lack physical grounding.
→ Ask for a plan: It sounds logical.
→ Execute it: It fails physically (e.g., trying to slice without a knife). 😵💫
The agent knows *what* to do, but not *how* physical laws constrain it.
💡The Solution: WorldMind
We bridge the gap between high-level reasoning and physical reality through:
🌍 Agentic World Model: Instead of external engines, we activate the agent's internal ability to simulate environmental dynamics to guide planning.
🔹 Online Experience Learning: Eliminates the need for costly fine-tuning or retraining.
🔹 Alignment via World Knowledge: Autonomously builds a World Knowledge Repository (WKR) to ground the agent.
This unifies:
• Process Experience: Learning from step-level prediction failures ❌
• Goal Experience: Distilling shortcuts from successful trajectories ✅
🚀 Key Features:
✅ Training-Free: Aligns agents via online experience learning.
✨ Superior Performance: improvements on EB-ALFRED & EB-Habitat.
🔗 Project Page:
zjunlp.github.io/WorldMind/
📄 Paper:
huggingface.co/papers/2601.1…
Our current method is limited by today’s foundation models and cannot yet support reliable long-horizon planning.
Looking ahead, as model capacity and memory modules continue to improve, we believe agents will gradually internalize world models and achieve robust long-term embodied decision-making.
#EmbodiedAI #MultimodalAgent #ExperienceLearning #Alignment #WorldModels #LLM #Robotics #AgenticAI #NLP #WorldMind