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arXiv · 2605.01154

Multi-Perspective Transformers in ARC-AGI-2 Challenge

Caleb Talley·Vedant Tibrewal·Seun Adekunle·Weiwen Dong·Xinyu Wu·Fariha Sheikh
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Abstract

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.

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BibTeXRIS

Caleb Talley, Vedant Tibrewal, Seun Adekunle, Weiwen Dong, Xinyu Wu, Fariha Sheikh. 2026-05-01. Multi-Perspective Transformers in ARC-AGI-2 Challenge. https://arxiv.org/abs/2605.01154

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