Building an ARC-2 Solver — From Socratic Panels to a Single Oracle

2026-02-13 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The article details the development of an ARC-2 solver, transitioning from a multi-agent Socratic architecture to a single, tool-augmented "Oracle" model. Initially, a supervisor agent orchestrated specialist panels debating hypotheses, but this approach proved inefficient due to flawed visual grounding and high token usage. The new Oracle model, built on LangGraph, achieved 50-70%+ accuracy on the ARC-2 public evaluation dataset, comparable to top leaderboard entries. This architecture emphasizes structured reflection, experimentation, and verification using a constrained tool environment, including Python for analysis and a utility library for ARC archetypes. Key to its success was improved visual perception through various grid presentation modes and a focus on stateless execution and mandatory verification before submission.

Key takeaway

For AI Engineers developing reasoning systems, this work suggests that focusing on robust perception and tool-augmented, disciplined single-agent architectures can yield better results than complex multi-agent debates. Prioritize clear visual grounding, integrate verified utility functions, and enforce mandatory verification steps to improve accuracy and reduce token costs. Consider "thinking time" (MAX_ORACLE_TOOL_CALLS) as a critical, tunable parameter for performance.

Key insights

A single, tool-augmented Oracle architecture outperforms multi-agent debate for ARC-2 by prioritizing perception, structured reflection, and verification.

Principles

• Deep reflection beats debate.
• Perceptual scaffolding matters.
• Thinking time scales performance.

Method

The Oracle model uses a LangGraph-based single-agent loop for reflection, experimentation via Python tools, and verification, submitting only after confirming correctness across all training examples.

In practice

• Use Python as an analytical microscope, not a solution generator.
• Implement stateless execution for rigor.
• Encode ARC archetypes into utility libraries.

Topics

• ARC-2 Solver
• Tool-Augmented AI
• Visual Perception
• AI Benchmarks
• Single-Agent Architecture

Best for: AI Engineer, Machine Learning Engineer, AI Researcher

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.