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foundation models

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  1. 1

    LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence

    LimiX-2 is a new tabular foundation model that replaces the usual target‑centric in‑context learning objective with a joint‑distribution objective via Contextual Mechanism Networks (CMNs). Trained on synthetic causal graphs using Context‑Conditional Masked Modeling, it outperforms prior tabular PFNs on TabArena, TALENT, and BCCO and can recover causal skeletons from attention patterns.

    Hugging Face Daily Papersarxiv.org2 minpaper
  2. 2

    AI for Games in the Foundation Model Era

    The paper surveys how foundation models are used across six roles in the game development lifecycle—from playing agents to design assistance and runtime adaptation. It highlights limited transferability due to game-specific interfaces and notes that evaluation is mature for bounded play but weak for adaptive and testing scenarios.

    Hugging Face Daily Papersarxiv.org1 minpaper
  3. 3

    Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models

    Latent Interface Training (LIT) first learns a goal‑conditioned action prior without visual input, then adds a pose‑supervised latent interface as the only visual conditioning path. Applied to several vision‑language‑action models, LIT cuts vision‑action shortcuts and lifts LIBERO‑Plus success by 3.9–10.7 points and real‑world task success by 13.3–16.7 points under distribution shifts.

    Hugging Face Daily Papersarxiv.org1 minpaper