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Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformer
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In the pursuit of artificial general intelligence (AGI), we tackle Abstraction and Reasoning Corpus (ARC) tasks using a novel two-pronged approach. We employ the Decision Transformer in an imitation learning paradigm to model human problem-solving, and introduce an object detection algorithm, the Push and Pull clustering method. This dual strategy enhances AI's ARC problem-solving skills and provides insights for AGI progression. Yet, our work reveals the need for advanced data collection tools, robust training datasets, and refined model structures. This study highlights potential improvements for Decision Transformers and propels future AGI research.
Forward citations
Cited by 2 Pith papers
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The Jumping Reasoning Curve? Tracking the Evolution of Reasoning Performance in GPT-[n] and o-[n] Models on Multimodal Puzzles
Later OpenAI o-series models substantially outperform GPT-series models on multimodal puzzles, but fine-grained visual perception and algorithmic puzzles remain hard.
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Abductive Symbolic Solver on Abstraction and Reasoning Corpus
A knowledge-graph-based abductive symbolic solver predicts ARC output grid size and color set with reported accuracies of 90.5% and 74.75%, but without trivial baselines or a disclosed evaluation split.
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