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# Out-of-Distribution Generalization

3 related articles

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  1. arXiv

    Chain-of-thought solves unseen multi-step tasks by reusing one step predictor, with error growing sublinearly in depth

    Chain-of-thought out-of-distribution generalization is framed as compositional generalization: training supplies only single-step atomic transitions, while the target task requires composing many steps. Theory bounds target risk by the sum of step risks times a depth-independent coefficient, and shows that sharing one predictor makes the estimation term shrink as the inverse square root of depth rather than grow linearly. Atom-Task and GSM8K experiments support short-to-long and unseen-combination transfer, and show explicit state tracking is essential.

  2. arXiv

    Cheap physics stops picking samples: PI-Init pretrains neural operators label-free, cutting Burgers error 53.3%

    Training neural operators needs expensive numerical solution labels, while PDE inputs and governing equations are cheap physical information. PI-Init spends that information on initialization: it pretrains on unlabeled inputs with physics losses, then keeps the parameters for supervised fine-tuning. At matched label budgets, FNO prediction error falls 53.3% on Burgers and 62.7% on 1D Advection, and on Burgers 200 labeled trajectories beat supervised-only training with 2,000.

  3. arXiv

    LLMs stop guessing bandgaps: Motif turns them into chemistry taxonomies, cutting 2D perovskite OOD error from 0.122 to 0.111 eV

    On 2D halide perovskite bandgap prediction, Motif moves the large language model from emitting numbers to generating chemical taxonomies offline, which then define environments for invariant feature selection. Across six leave-one-spacer-family-out folds, mean OOD MAE falls from the kNN+GBT baseline's 0.122 eV to 0.111 eV; under a frozen dev/test protocol the deployable configuration lowers electronic bandgap error from 0.123 to 0.117 eV and optical bandgap error from 0.181 to 0.173 eV.

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