ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning
ARCANA decomposes each task into iterative perception, hypothesis generation, symbolic execution, and reflective refinement. A perceptual grounding agent builds object centric scene graphs from raw grids, a latent program policy proposes diverse DSL programs, a symbolic executor verifies candidates on demonstrations, and a reflective agent synthesizes failure driven feedback for the next turn. These agents communicate through a shared differentiable blackboard and are scheduled by a learned meta controller.
- ▪ARCANA decomposes each task into iterative perception, hypothesis generation, symbolic execution, and reflective refinement.
- ▪A perceptual grounding agent builds object centric scene graphs from raw grids, a latent program policy proposes diverse DSL programs, a symbolic executor verifies candidates on demonstrations, and a reflective agent synthesizes failure dri
- ▪These agents communicate through a shared differentiable blackboard and are scheduled by a learned meta controller.
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Computer Science > Artificial Intelligence arXiv:2607.09059 (cs) [Submitted on 10 Jul 2026] Title:ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning Authors:Kunbo Zhang, Lei Fu, Zeyu Wang, Zijing Liu, Kejian Tong View a PDF of the paper titled ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning, by Kunbo Zhang and 4 other authors View PDF HTML (experimental) Abstract:We present ARCANA, a collaborative multi agent framework for solving ARC AGI 2 tasks under strict test time and hardware constraints. ARCANA decomposes each task into iterative perception, hypothesis generation, symbolic execution, and reflective refinement.
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