Thinking with Looped Flows
- Published
- Source
- arXiv
- Paper number
- 1083
- Field
- Machine Learning
- arXiv ID
- 2609.11801
Key points
- Compared with the prior best model of the same architecture (TRM), it raises ARC-AGI-1 accuracy from 44.6% to 58.8% and ARC-AGI-2 from 7.8% to 12.2%.
- It solves 90.9% of the cases where earlier loop models failed, recovering 89.9% of non-convergence cases and 98.0% of cases that settled on wrong answers.
- On problems with many valid answers such as N-Queens and graph coloring, different samplings yield multiple distinct valid solutions, achieving the best scores on every metric.
- Performance keeps improving simply by refining the inference-time grid or using higher-order integrators, realizing the goal of solving better with more compute.
- Ablations show that each flow component—time conditioning, the interpolant, annealed noise, and noise sharing—contributes measurably to performance.
Paper links
External research summaries. These are not HDATF publications or measured product results.