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arXiv:2603.15381 (cs)
[Submitted on 16 Mar 2026]

Title:Why AI systems don't learn and what to do about it: Lessons on
autonomous learning from cognitive science

Authors:Emmanuel Dupoux, Yann LeCun, Jitendra Malik
View a PDF of the paper titled Why AI systems don't learn and what to
do about it: Lessons on autonomous learning from cognitive science,
by Emmanuel Dupoux and 2 other authors
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    Abstract:We critically examine the limitations of current AI
    models in achieving autonomous learning and propose a learning
    architecture inspired by human and animal cognition. The proposed
    framework integrates learning from observation (System A) and
    learning from active behavior (System B) while flexibly switching
    between these learning modes as a function of internally
    generated meta-control signals (System M). We discuss how this
    could be built by taking inspiration on how organisms adapt to
    real-world, dynamic environments across evolutionary and
    developmental timescales.

Subjects: Artificial Intelligence (cs.AI)
Cite as:  arXiv:2603.15381 [cs.AI]
          (or arXiv:2603.15381v1 [cs.AI] for this version)
          https://doi.org/10.48550/arXiv.2603.15381
          Focus to learn more
          arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Emmanuel Dupoux [view email]
[v1] Mon, 16 Mar 2026 14:54:56 UTC (2,827 KB)
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