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Sagan

Paper

Reasoning emerges from constrained inference manifolds in large language models

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AI summary

The authors examine what happens inside language models during reasoning, not just whether they get the right answer. They find that the internal representations during inference compress into low-dimensional manifolds (the model's internal state lives in a much smaller space than you'd expect). Good reasoning only happens when three conditions hold: the compressed space is expressive enough, compression happens spontaneously, and information doesn't collapse to zero in the compressed subspace. When these conditions fail, you get predictable failure modes. They propose a diagnostic that doesn't need any labels—just watch the internal dynamics.

Main takeaways:

  • Reasoning compresses model representations into low-dimensional manifolds, but compression alone doesn't guarantee good reasoning
  • Three necessary conditions: adequate expressivity, spontaneous compression, and preserved information volume in the compressed subspace
  • Models outside this "structural regime" show characteristic pathological inference patterns
  • The authors built a label-free diagnostic computed purely from internal dynamics, offering an alternative to benchmark evaluation
  • Suggests reasoning is fundamentally governed by geometric and informational constraints, not just learned patterns