World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic framework based on ortho...
Tao-Yong Cui, Zhong-Yao Wang, Xin-Yue Xu et al.· 0 citations
The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities.
He-Jia Geng, Ze-Sen Huang, Hao-Yang Li et al.· 1 citation
This work introduces and releases ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researchers'everyday workflows and presents case studies of researcher interaction, harness refinement, and model learning, with the benchmark cases spanning four scienti...
Shu-Han Xue, Jian-Yuan Zhong, Ziyuan Nan et al.· 2 citations
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