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AI CLUSTERED EVENT · 10/14/2026

SanSi Looping Architecture: 1.4B Model Beats 4.2B Baseline on Decision Tasks via Layer Reuse

1 reports archived1 independent sourcesupdated 10/14/2026, 04:02:34
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The SanSi framework enables small pre-trained models like Ouro-1.4B to loop through the same network layers multiple times during inference for complex decision-making. It achieved 72.0% accuracy on 10,027 test decisions, outperforming a non-looped baseline by 13.6 points and surpassing a 4.2B single-pass model on long-chain logic tasks, offering a high-accuracy solution for memory-constrained environments.

LATEST/The SanSi framework enables small pre-trained models like Ouro-1.4B to loop through the same network layers multiple times during inference for complex decision-making. It achieved 72.0% accuracy on 10,027 test decisions, outperforming a non-looped baseline by 13.6 points and surpassing a 4.2B single-pass model on long-chain logic tasks, offering a high-accuracy solution for memory-constrained environments.

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Total 1 reports · Latest first
  1. Rohan Paul (@rohanpaul_ai)T2·68 pts
    • SanSi supports dynamic early stopping between 1 to 8 loops, outputting option probabilities at each step for adaptive computation.
    • Looping yields a 13.6% absolute accuracy gain over same-shape baselines, albeit with 7.7x compute cost.
    • For multi-step dependent decisions, looping small models offer better cost-efficiency than simply scaling up parameters.
SanSi Looping Architecture: 1.4B Model Beats 4.2B Baseline on Decision Tasks via Layer Reuse | Today for AI