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New frameworks advance agent-based world modeling for scalability and social science

Researchers have developed two distinct frameworks for agent-based world modeling. The first, Khora, focuses on scalability by decoupling world-state evolution from visual rendering, allowing for an arbitrary number of agents during inference without retraining. The second, MAWM, introduces a multilingual agent-based world modeling framework designed for social science research, enabling cross-lingual interactions among generative agents and supporting analysis of global public opinion and media influence. AI

IMPACT These frameworks offer new tools for simulating complex multi-agent systems, potentially advancing research in areas from robotics to computational social science.

RANK_REASON Two distinct academic papers introducing new frameworks for agent-based world modeling.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New frameworks advance agent-based world modeling for scalability and social science

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Renjie Zhao, Yuxiang Wu, Mingyu Zhang, Jiaxin Li, Sisi Li, Yimin Sheng, Tianxi Tan, Zhenkai Zhang, Jianyi Zhu, Yong-Lu Li ·

    Population-Scalable Multi-Agent World Modeling

    arXiv:2608.08600v1 Announce Type: cross Abstract: World models have recently achieved impressive progress in visual prediction and interactive generation, but extending them to multi-agent environments introduces a fundamental scalability challenge. Existing methods generally ass…

  2. arXiv cs.AI TIER_1 English(EN) · Xuan Zhang, Wenxuan Zhang, Anxu Wang, See-Kiong Ng, Yang Deng ·

    Multilingual Agent-Based World Modeling for Social Science

    arXiv:2512.07195v2 Announce Type: replace-cross Abstract: Multi-agent role-playing has recently shown promise for studying social behavior with language agents, but existing simulations are mostly monolingual without cross-lingual interaction, an essential property of real societ…