ARC-AGI Task #39a8645d

ARC-AGI-3

ARC-AGI-3 is the first interactive reasoning benchmark for AI agents—play as humans and build agents that learn in novel environments.

Alex MacCaw on Twitter / X

I suspect generalized reasoning was solved just a few weeks ago and it flew completely under the radar.HRM, a new arch, reportedly has SOTA results on ARC-AGI 1 & 2 benchmarks with only 27 million parameters and ~1k training examples.— Alex MacCaw (@maccaw) July 25, 2025

ARC Prize on Twitter / X

OpenAI’s internal testing shows that provider-managed conversation state preserves greater continuity across turns and improves performance on long-horizon tasks like ARC-AGI-3. This is a real and useful result. We’re encouraged to see ARC used to identify useful harness design.… https://t.co/6Xk2Op0Sls— ARC Prize (@arcprize) July 30, 2026

Where’s my ten minute AGI?

Why don’t AIs automate more real-world tasks if they can handle 1-hour ones? Here are at least three fundamental reasons.

ARC-AGI-3: A New Challenge for Frontier Agentic Intelligence

We introduce ARC-AGI-3, an interactive benchmark for studying agentic intelligence through novel, abstract, turn-based environments in which agents must explore, infer goals, build internal models of environment dynamics, and plan effective action sequences without explicit instructions. Like its predecessors ARC-AGI-1 and 2, ARC-AGI-3 focuses entirely on evaluating fluid adaptive efficiency on novel tasks, while avoiding language and external knowledge. ARC-AGI-3 environments only leverage Core Knowledge priors and are difficulty-calibrated via extensive testing with human test-takers. Our testing shows humans can solve 100% of the environments, in contrast to frontier AI systems which, as of March 2026, score below 1%. In this paper, we present the benchmark design, its efficiency-based scoring framework grounded in human action baselines, and the methodology used to construct, validate, and calibrate the environments.

François Chollet on Twitter / X

Quick reminder of what's ok vs not ok with harnesses used for playing ARC-AGI-3:1. Not okay: harnesses that were custom-made to solve the benchmark or that contain knowledge about the benchmark format / contents.2. Fine: general-purpose API settings that were not developed…— François Chollet (@fchollet) July 30, 2026

Letter to Arc members 2025

On Arc, its future, and the arrival of AI browsers — a moment to answer the largest questions you've asked us this past year.

Letter to Arc members 2025

On Arc, its future, and the arrival of AI browsers — a moment to answer the largest questions you've asked us this past year.

Arc Institute

Arc Institute is a independent nonprofit research organization headquartered in Palo Alto, California.

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