GIFARC: Synthetic Dataset for Leveraging Human-Intuitive Analogies to Elevate AI Reasoning

• URL: http://arxiv.org/abs/2505.20672v1
• Date: Tue, 27 May 2025 03:42:51 GMT
• Title: GIFARC: Synthetic Dataset for Leveraging Human-Intuitive Analogies to Elevate AI Reasoning
• Authors: Woochang Sim, Hyunseok Ryu, Kyungmin Choi, Sungwon Han, Sundong Kim,
• Abstract summary: State-of-the-art models still achieve accuracy rates of merely 40-55% on 2024 ARC Competition.<n>We introduce an analogy-inspired ARC dataset, GIFARC.<n>GIFARC guides AI agents to evaluate the task analogically before engaging in brute-force pattern search.
• Score: 7.09254962218677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Abstraction and Reasoning Corpus (ARC) poses a stringent test of general AI capabilities, requiring solvers to infer abstract patterns from only a handful of examples. Despite substantial progress in deep learning, state-of-the-art models still achieve accuracy rates of merely 40-55% on 2024 ARC Competition, indicative of a significant gap between their performance and human-level reasoning. In this work, we seek to bridge that gap by introducing an analogy-inspired ARC dataset, GIFARC. Leveraging large language models (LLMs) and vision-language models (VLMs), we synthesize new ARC-style tasks from a variety of GIF images that include analogies. Each new task is paired with ground-truth analogy, providing an explicit mapping between visual transformations and everyday concepts. By embedding robust human-intuitive analogies into ARC-style tasks, GIFARC guides AI agents to evaluate the task analogically before engaging in brute-force pattern search, thus efficiently reducing problem complexity and build a more concise and human-understandable solution. We empirically validate that guiding LLM with analogic approach with GIFARC affects task-solving approaches of LLMs to align with analogic approach of human.

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