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Trace2Env framework simulates LLM agent environments from interaction traces

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Trace2Env, a novel framework for agentic language world modeling. This approach allows a world model agent to simulate interactive environments for task agents, even when the original system is inaccessible. Trace2Env reconstructs historical interaction traces into a "worldbook" containing environment schemas and behavioral knowledge, enabling more faithful and stateful simulations than traditional prompt-based methods. The framework has been evaluated across nine diverse environments, demonstrating improved fidelity and consistency in long-horizon interactions. AI

IMPACT Enables training and evaluation of LLM agents in simulated environments derived from historical data, potentially reducing reliance on real-world systems.

RANK_REASON This is a research paper detailing a new framework for agentic language world modeling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Trace2Env framework simulates LLM agent environments from interaction traces

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This is a research paper detailing a new framework for agentic language world modeling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

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

    Realistic environment replicas are increasingly valuable for training and evaluating LLM agents, yet the original systems may be inaccessible or impractical to reproduce. We explore agentic language world modeling: rather than rebuilding an executable environment, a world model a…

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