Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback

• URL: http://arxiv.org/abs/2512.22336v1
• Date: Fri, 26 Dec 2025 18:54:14 GMT
• Title: Agent2World: Learning to Generate Symbolic World Models via Adaptive Multi-Agent Feedback
• Authors: Mengkang Hu, Bowei Xia, Yuran Wu, Ailing Yu, Yude Zou, Qiguang Chen, Shijian Wang, Jiarui Jin, Kexin Li, Wenxiang Jiao, Yuan Lu, Ping Luo,
• Abstract summary: Agent2World is a tool-augmented multi-agent framework that achieves strong inference-time world-model generation.<n>It also serves as a data engine for supervised fine-tuning, by grounding generation in multi-agent feedback.
• Score: 51.22403664895878
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Symbolic world models (e.g., PDDL domains or executable simulators) are central to model-based planning, but training LLMs to generate such world models is limited by the lack of large-scale verifiable supervision. Current approaches rely primarily on static validation methods that fail to catch behavior-level errors arising from interactive execution. In this paper, we propose Agent2World, a tool-augmented multi-agent framework that achieves strong inference-time world-model generation and also serves as a data engine for supervised fine-tuning, by grounding generation in multi-agent feedback. Agent2World follows a three-stage pipeline: (i) A Deep Researcher agent performs knowledge synthesis by web searching to address specification gaps; (ii) A Model Developer agent implements executable world models; And (iii) a specialized Testing Team conducts adaptive unit testing and simulation-based validation. Agent2World demonstrates superior inference-time performance across three benchmarks spanning both Planning Domain Definition Language (PDDL) and executable code representations, achieving consistent state-of-the-art results. Beyond inference, Testing Team serves as an interactive environment for the Model Developer, providing behavior-aware adaptive feedback that yields multi-turn training trajectories. The model fine-tuned on these trajectories substantially improves world-model generation, yielding an average relative gain of 30.95% over the same model before training. Project page: https://agent2world.github.io.

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