ARCANA: A Reflective Multi-Agent Program Synthesis Framework for ARC-AGI-2 Reasoning
Quick Answer
ARCANA is a multi-agent framework designed for ARC AGI 2 tasks, optimizing reasoning efficiency through a structured approach involving perception, hypothesis generation, and reflective refinement.
Quick Take
It employs a shared differentiable blackboard and a learned meta controller to enhance solution quality under strict constraints.
Key Points
- ARCANA decomposes tasks into perception, hypothesis generation, symbolic execution, and reflective refinement.
- A perceptual grounding agent creates object-centric scene graphs from raw data.
- A latent program policy proposes diverse domain-specific language (DSL) programs.
- The framework improves reasoning efficiency and solution quality on complex tasks.
- Agents communicate via a shared differentiable blackboard managed by a learned meta controller.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.09059 [cs.AI] |
| (or arXiv:2607.09059v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09059 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kejian Tong [view email]
[v1]
Fri, 10 Jul 2026 03:03:42 UTC (6,052 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.