arXiv:2606.30639v1 Announce Type: new Abstract: World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution. However, unreliable foresight can be ignored, misused, or even degrade downstream decision-making. In this paper, we introduce WorldEvolver, a self-evolving world model framework that revises its deployment-time context while keeping the downstream agent and all model parameters frozen. WorldEvolver integrates three modules: (i) Episodic Memory, which exploits real action transitions through retrieval-based sim
The rapid advancement of large language models is transitioning from static models to dynamic, agentic systems, necessitating robust planning capabilities.
Reliable foresight for long-horizon AI agents is critical for their autonomous functionality and integration into complex real-world workflows.
This research introduces a self-evolving mechanism for world models, allowing LLM agents to improve foresight and decision-making on the fly without retraining core models.
- · AI Agent developers
- · Enterprises adopting AI Agents
- · Applied AI researchers
- · Legacy process automation providers
- · Centralized model retraining services
LLM agents will be able to perform more complex, multi-step tasks with reduced human oversight.
The improved reliability of agentic behavior will accelerate their deployment across various industries, collapsing existing white-collar workflows.
As agents become more autonomous and self-improving, the nature of human-AI collaboration will shift towards more high-level directive oversight rather than detailed task delegation.
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Read at arXiv cs.AI