Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning
arXiv:2606.27483v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-horizon tasks. Unlike humans who employ "what-if" reasoning to evaluate potential plans before commitment, standard agents lack an internal world model to simulate future outcomes. Therefore, we propose to internalize future-aware planning by training a single autoregressive model to verbalize both a p
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Computer Science > Artificial Intelligence arXiv:2606.27483 (cs) [Submitted on 25 Jun 2026] Title:Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning Authors:Xuan Zhang, Zhijian Zhou, Lingfeng Qiao, Yulei Qin, Ke Li, Xing Sun, Xiaoyu Tan, Chao Qu, Yuan Qi View a PDF of the paper titled Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning, by Xuan Zhang and 8 other authors View PDF Abstract:Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-horizon tasks. Unlike humans who employ "what-if" reasoning to evaluate potential plans before commitment, standard agents lack an internal world model to simulate future outcomes.
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