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AI CURATED HOT STORY · 10/29/2026

Cartesia Proposes Hierarchical Networks for End-to-End Learning from Bytes

1 reports archived1 independent sourcesupdated 10/29/2026, 02:07:41
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Cartesia Research introduces 'Hierarchical Networks' to address the reliance on hand-crafted encoding in current LLMs. The study advocates returning to the 'bitter lesson' by enabling end-to-end learning directly from raw bytes, aiming to improve generalization in data-scarce domains like science and robotics.

LATEST/Cartesia Research introduces 'Hierarchical Networks' to address the reliance on hand-crafted encoding in current LLMs. The study advocates returning to the 'bitter lesson' by enabling end-to-end learning directly from raw bytes, aiming to improve generalization in data-scarce domains like science and robotics.

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  1. Hacker News AIT2·78 pts

    Cartesia Proposes Hierarchical Networks for End-to-End Learning from Bytes

    • Highlights that while LLMs follow scaling laws, they still heavily rely on hand-crafted encoding steps (e.g., tokenization).
    • Proposes a hierarchical network architecture to learn abstract patterns directly from raw bytes instead of tokens.
    • Emphasizes that in complex, data-scarce domains (like biology and robotics), general learning algorithms are more critical than human-engineered priors.
Cartesia Proposes Hierarchical Networks for End-to-End Learning from Bytes | Today for AI