#world-models2 articles

2 articles

AIBullisharXiv – CS AI · 4h ago4

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Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments

Researchers propose a new framework for foundation world models that enables autonomous agents to learn, verify, and adapt reliably in dynamic environments. The approach combines reinforcement learning with formal verification and adaptive abstraction to create agents that can synthesize verifiable programs and maintain correctness while adapting to novel conditions.

AIBullisharXiv – CS AI · 4h ago5

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Context and Diversity Matter: The Emergence of In-Context Learning in World Models

Researchers investigate in-context learning (ICL) in world models, identifying two core mechanisms - environment recognition and environment learning - that enable AI systems to adapt to new configurations. The study provides theoretical error bounds and empirical evidence showing that diverse environments and long context windows are crucial for developing self-adapting world models.