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New paper recasts world modeling for agent-centric feedback

A new paper proposes Agent-Centric Interactive World Proxies, shifting the focus of world modeling from physical state prediction to agent-usable information transitions. This framework categorizes world proxies into six functional forms—dynamics, spatial, execution, memory/experience, skill, and reward/verification—to provide versatile feedback for continually improving agents. The paper outlines three progressive levels of agent empowerment through these proxies: inference-time guidance, training-time optimization, and agent-proxy co-evolution, aiming to establish a roadmap for agents that can plan better, learn faster, and evolve continually. AI

IMPACT Establishes a new paradigm for world modeling, potentially enabling more adaptable and efficient AI agents.

RANK_REASON The cluster contains an academic paper detailing a new conceptual framework for world modeling in AI.

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New paper recasts world modeling for agent-centric feedback

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yu Yang, Xuemeng Yang, Licheng Wen, Lingdong Kong, Xiaobin Hu, Dongyue Lu, Wei Chow, Xiyan Huang, Yuxiang Feng, Yue Liao, Jianbiao Mei, Daocheng Fu, Rong Wu, Pinlong Cai, Ran Yi, Ying Tai, Jiangning Zhang, Botian Shi, Yong Liu, Shuicheng Yan ·

    Quo Vadis, World Modeling?

    arXiv:2608.02713v1 Announce Type: cross Abstract: Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize. World modeling offers a natural intermediate pr…

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

    Quo Vadis, World Modeling?

    Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to parallelize. World modeling offers a natural intermediate proxy that allows agents to query lower-cost, more c…