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New research explores geometric properties of dynamic programming for neural network generalization

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Researchers have explored the geometric properties of dynamic programming (DP) to understand why standard neural networks struggle with generalizing to longer inputs in DP tasks. They established that finite min-plus DP problems are equivalent to shortest path problems on directed acyclic graphs, which can also be represented as tropical polynomials. The study introduces two structural negatives regarding the reduction of dimensionality and composition of these DP structures, indicating limitations in current approaches for length generalization. AI

RANK_REASON The item is a research paper submitted to arXiv cs.LG. [lever_c_demoted from research: ic=1 ai=1.0]

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New research explores geometric properties of dynamic programming for neural network generalization

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The item is a research paper submitted to arXiv cs.LG. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Richard F. M. Lim, Ruriko Yoshida ·

    On the Representational Geometry of Dynamic Programs

    arXiv:2608.25034v1 Announce Type: new Abstract: Standard neural architectures often fail to generalize to longer inputs for dynamic programming (DP) targets. We investigate what makes this hard geometrically. Every finite min-plus DP is a shortest path on a DAG, which is equivale…

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