poetiq-ai/poetiq-arc-agi-solver

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1282 stars · 214 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Python research codebase that reproduces Poetiq's record-breaking, leaderboard-topping submissions to the ARC-AGI-1 and ARC-AGI-2 abstract reasoning benchmarks. It runs configurable reasoning setups (e.g., the Poetiq 3 config) on top of frontier LLM APIs such as Gemini and OpenAI to solve ARC puzzle tasks and measure cost versus accuracy.

Use cases

• reproduce state-of-the-art ARC-AGI benchmark results
• solve ARC-AGI puzzle tasks using frontier LLMs
• evaluate Gemini and OpenAI models on abstract reasoning problems
• experiment with reasoning configs on the ARC-AGI public eval sets
• measure cost versus accuracy trade-offs on ARC-AGI-2
• research LLM abstraction and reasoning capabilities for ARC Prize

When to choose

• You want to replicate or verify Poetiq's published ARC-AGI-1/2 results for research or benchmarking
• You need a runnable baseline for LLM-based solvers on the ARC-AGI public eval sets
• You are studying prompting and configuration strategies that achieve high reasoning scores at low cost

When to avoid

• You need production-ready software - the code expects editing constants in main.py and uncommenting configs, and is research-grade
• You want an offline or self-hosted solver - it requires paid Gemini/OpenAI API keys
• You need a general-purpose reasoning engine beyond the ARC-AGI task format
• You want to train or fine-tune models - it only orchestrates inference over existing LLM APIs

Facets

application · maturity active

machine-learning llm-inference prompt-engineering benchmarking artificial-intelligence large-language-models machine-learning python cli cross-platform arc-agi abstract-reasoning benchmark-reproduction reasoning llm-orchestration gemini openai state-of-the-art research-code puzzle-solving

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