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Towards Efficient Neurally-Guided Program Induction for ARC-AGI
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ARC-AGI is an open-world problem domain in which the ability to generalize out-of-distribution is a crucial quality. Under the program induction paradigm, we present a series of experiments that reveal the efficiency and generalization characteristics of various neurally-guided program induction approaches. The three paradigms we consider are Learning the grid space, Learning the program space, and Learning the transform space. We implement and experiment thoroughly on the first two, and retain the second one for ARC-AGI submission. After identifying the strengths and weaknesses of both of these approaches, we suggest the third as a potential solution, and run preliminary experiments.
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TraceViT: Grounded Trace Supervision for Visual Abstract Reasoning
A looped visual transformer trained on program-derived intermediate grid milestones plus task-reference/object-workspace grounding reaches 67.8% pass@2 on ARC-AGI-1 and 24.3% on ARC-AGI-2.
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