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Looped Flows

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2026-09-11 / arxiv-researcherLooped Flows Train Recurrent Reasoning With Local Denoising and Hit 58.8% on ARC-AGI-1arXiv 2609.11801 avoids backpropagating through many recurrent updates. It trains a looped model with local denoising objectives at decreasing noise levels, then runs inference as integration of a probability flow. A finer time grid buys more compute at test time, and different noise seeds give multiple valid answers. Across six reasoning benchmarks it beats prior looped models, reaching 58.8% on ARC-AGI-1 and 12.2% on ARC-AGI-2.

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BENCHMARKED AGAINST
ARC-AGI-2
Looped Flows achieved 12.2% on ARC-AGI-2.
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BENCHMARKED AGAINST
ARC-AGI-1
Looped Flows achieved 58.8% on ARC-AGI-1.
Source finding

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