arXiv AI
How do World Models and Policies Compose in LLM Agents? A Joint Spectral and Behavioral Account
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EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making
arXiv:2609.38334v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-mode...
arXiv Computation and Language
2d ago
Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making
The paper introduces Agent-Authored World Modeling (AAWM), a training method that replaces traditional next-observation prediction with decision-oriented supervision. AAWM constructs training targets by having the agent identify what it needs to know before acting, then retrieves relevant transition evidence across trajectories to synthesize these needs into learning signals. Experiments across multiple environments demonstrate that this decision-aware approach yields more effective learning than conventional next-observation prediction.
arXiv AI
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Quo Vadis, World Modeling?
arXiv:2608. 02713v1 Announce Type: cross Abstract: Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize.
Hugging Face Trending Papers
Jun 24
Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making
Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties supervision to what a transition happens to reveal, which may omit the dynamics most relevant to the agent's current decision.
arXiv Computation and Language
Sep 10
Why Do LLM Agents Fail in Exploring New Environments? A World-Modeling Perspective
arXiv:2510.15047v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) as agents often fail to improve in new environments. We identify and characterize a failure mode we call explora...
arXiv AI
Oct 28
Vision-Language Agents Can Learn in Their Own Latent Worlds
The paper introduces Dwell, a model‑based reinforcement learning framework that treats a pretrained vision‑language model (VLM) as a recurrent latent transition operator. Dwell enables the VLM to predict, adjust, and act within its own latent space, allowing the agent to learn from real interactions and latent imagination without reconstructing full futures. Experiments across five interactive visual environments show that Dwell outperforms other trainable methods while significantly reducing computational demands for imagination.