AI

From Traces to Agentic Worlds: Agentic Language World Models for Interactive Environment Simulation

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 03:18:25 UTC.

Briefings

This article presents Trace2Env, a learning-free framework that reconstructs historical interaction traces into reusable environment worldbooks for simulating LLM agents. The study tested nine environments and found that Trace2Env significantly improved next-observation fidelity and long-horizon interaction consistency compared to conventional prompt-based world models. Results indicate that simulated dynamics better preserve the consequences of earlier actions across successive turns, establishing agentic language world modeling as a viable alternative for realistic environment simulation.

Key points

• Trace2Env is a learning-free framework that reconstructs historical interaction traces into reusable environment worldbooks containing environment schemas and behavioral knowledge.
• Across nine environments, Trace2Env improved both next-observation fidelity and long-horizon interaction consistency over conventional prompt-based LLM world models.

Context

Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems may be inaccessible or impractical to reproduce. This study explores agentic language world modeling, where a world model agent serves as the environment for a task agent, supporting faithful and stateful simulation without rebuilding the original executable system.

Limitations

The study covers nine environments and uses historical interaction traces as the sole data source for reconstruction. Results are based on simulated dynamics rather than real-world validation, and the framework's performance may vary across different task types or agent behaviors.

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