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Paper detail

Multi-Perspective Transformers in ARC-AGI-2 Challenge

ARC-AGI-2 is a benchmark of human-intuitive visual puzzles that measures a machine's ability to generalize from limited examples, interpret symbolic meaning, and flexibly apply rules in varying contexts. In this paper, we discuss our approach to solving the ARC-AGI-2 puzzles with TinyLM, with additional fine-tuning at test time, including Test-Time-Training (TTT) and Products of Experts (POE). Our model achieves 96.1% accuracy on the training set and 21.7% accuracy on the evaluation set.

preprint2026arXivOpen access
Caleb TalleyVedant TibrewalSeun AdekunleWeiwen DongXinyu WuFariha Sheikh
Machine LearningArtificial Intelligence
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Caleb TalleyVedant TibrewalSeun AdekunleWeiwen DongXinyu WuFariha Sheikh

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