AGENTIC-SYSTEMSJuly 13, 20261 min read

ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning

Researchers have introduced ARCANA, a collaborative multi-agent framework designed to solve challenging ARC-AGI-2 reasoning tasks under strict constraints. By combining structured program search with adaptive multi-turn correction, ARCANA significantly improves reasoning efficiency and solution quality.

Computer Science > Artificial Intelligence

Title:ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning

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. 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. The design combines structured program search with adaptive multi turn correction, improving reasoning efficiency and solution quality on challenging abstract transformation tasks.

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Source: arXiv cs.AI Recent

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