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Ming-Hao Fu

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Preprint Oct 2026

Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos

Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{auto...

Jin-Zhou Tang, Z. Zhang, Jing Yang et al. · 1 citation
#artificial intelligence Preprint Oct 2026

LeWAM: End-to-End World and Action Modeling with JEPAs

Joint-embedding predictive architectures (JEPAs) learn compact latent world models without pixel reconstruction. Existing JEPA pipelines typically train visual representations and action-conditioned dynamics before adding a control policy or solver. We introduce LeWAM, an end-to-end JEPA for control with: (1) a joint t...

Minghao Fu, Tavis Siebert, Eryk Halicki et al. · 0 citations
Preprint Oct 2026

Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos

Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{auto...

Jin-Zhou Tang, Z. Zhang, Jing Yang et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos

Building interactive simulators from real-world observations is a promising way to scale embodied data, but current pipelines still rely heavily on manual environment construction and calibration. We study whether frontier foundation models and coding agents can automate this process end to end. We formulate \emph{auto...

Jin-Zhou Tang, Z. Zhang, Jing Yang et al. · 0 citations
#machine learning Preprint Sep 2026

RSIGame: Autonomous Agentic Game Development with Recursive Self-improvement

Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a playable version remains challenging. Naive iterative refinement can easily overfit a small set of test cases, producing fragile games with unresolved bugs, missing behavio...

Wen-Yi Wu, Ming-Hao Fu, Jie-Yu You et al. · 0 citations
Preprint Aug 2026

StreamScout: Learning When to Look Deeper for Streaming Video Understanding

Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily focus on what to retain in a bounded memory, yet access that memory using the same fixed-cost procedure for every query, despite substantial variation in the evidence re...

Ce Zhang, Jing Bi, Jinxi He et al. · 2 citations

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