A North Star for open AGI. Co-founders: @fchollet @mikeknoop. President: @gregkamradt. We're hiring mission-driven builders: arcprize.org/jobs

Joined March 2024
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Announcing ARC-AGI-3 The only unsaturated agentic intelligence benchmark in the world Humans score 100%, AI <1% This human-AI gap demonstrates we do not yet have AGI Most benchmarks test what models already know, ARC-AGI-3 tests how they learn
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ARC Prize 2026: ARC-AGI-3 Milestone Prize #1 Congratulations to the teams who open-sourced the top notebooks on the ARC-AGI-3 public leaderboard: 1. @tufalabs 2. Reki 3. Md Boktiar Mahbub Murad A prize write-up and analysis of the winning solutions will follow.
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Video interview of @tufalabs and their submission by @MLStreetTalk x.com/MLStreetTalk/status/20…
ARC-AGI-3 is built different, it has dumbfounded almost all regular attempts so far because it's so much harder than anything that came before. It has no rules, it's agentic and has no explicit goals, they need to be discovered. @tufalabs won the first milestone of @arcprize > There is no language built into the benchmark, but these guys "put the language back in", because in their view - it's the best way to climb up the notional "abstraction mountain" and effectively use many of the abstractions which have evolved over millions of years of language evolution. > They built a novel harness "The Duck" around a 27B open weights model (Qwen 3.6) to solve extremely challenging and novel reasoning problems that require abstraction. > This is the launch video of their winning agentic harness, "The Duck". We have also released an exclusive interview with them on MLST, just dropped. > The million dollar question is: what will @fchollet think about how they've done it, and is this a step towards AGI?
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ARC Prize reposted
Watch @fchollet live at @LaudeInstitute Open Frontier. Panel: "Building Things That Last: Lessons from Computing's Long Arc". youtube.com/live/-41kYH6JgvU
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Continual Harness: An Efficient Self-Improving Agent on ARC-AGI-3 by @sethkarten from @PrimeIntellect > The heavy test-time learning required by the benchmark (ARC-AGI-3) pushes agents to form an internal world model of the rules and mechanics that updates with new evidence.
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Submissions for the first ARC-AGI-3 milestone prize are due in 6 days Notebooks must be open sourced by 11:59pm UTC on June 30th $37.5K in prizes await the winners
ARC Prize 2026 - ARC-AGI-3 Progress Prize $37,500 in prizes are being awarded on June 30th to the top open source solutions Only one team is out performing the templates Can you be the next?
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GLM-5.2 from @Zai_org on ARC-AGI (Verified) - ARC-AGI-2: 22.8%, $0.25 - ARC-AGI-1: 77.0%, $0.19 Performance is comparable with GPT-5.4 & 5.5 (Low Reasoning Effort)
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ARC-AGI-3 testing pending due to inference throughput constraints. Full ARC-AGI-3 verification is significantly more operationally intensive than ARC-AGI-1/2, and we will report results once we can run a reliable evaluation.
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ARC Prize Foundation General Intuition We’re grateful to announce that @gen_intuition has donated $1,001,337 to support the development of ARC-AGI-4 Thank you @PimDeWitte, @EloiAlonso1, @AdamJelley2, and @micheli_vincent for helping push open AGI benchmarking forward
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As always, donations to ARC Prize Foundation do not come with preferred treatment. We have not tested General Intuition’s models, but welcome the opportunity to do so in the future. Any testing partnership will be disclosed at the time of evaluation. Policy: arcprize.org/policy
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gpt-oss-120b template now available for ARC Prize 2026: ARC-AGI-3 Use it to submit directly or plug into your harness $35.7K in prizes will be awarded on June 30th for top open source solutions Will yours be one of them?
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ARC Prize 2026 - ARC-AGI-3 Progress Prize $37,500 in prizes are being awarded on June 30th to the top open source solutions Only one team is out performing the templates Can you be the next?
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The Prize Pool: * 1st - $25,000 * 2nd - $7,500 * 3rd - $5,000 kaggle.com/competitions/arc-…
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Enter ARC Prize 2026 on Kaggle kaggle.com/competitions/arc-… New to ARC-AGI-3? Build your first agent in a few lines of code arcprize.org/arc-agi/3 docs.arcprize.org/
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We had early access to Anthropic’s Fable 5, but did not run verified Semi-Private ARC-AGI-1/2/3 evals due to their new data-retention terms for Mythos-class models. We’re working with Anthropic to keep ARC verification data private. Scores will come once we can run them safely.
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Anthropic Opus 4.8 is new SOTA on ARC-AGI-3 Score: 1.5%, ~$10K ARC-AGI-3 analysis notes: * Opus 4.8 read the environment an abstraction *above* Opus 4.7, as objects & systems, not pictures * Opus 4.8 succeeded on early levels, but still committed to a wrong sub-goal
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Testing notes: * ARC-AGI-3 Prompt Update - We observed that in our original system prompt, GPT and Gemini, unlike other models, would not "think out loud" in their reply. This caused them to *only* return an action in their response (ex: "ACTION1"). This capped the signal we were able to extract from the model. We updated the system prompt used for ARC-AGI-3 to *explicitly* say context will be carried forward instead of the original *implicit* nudge See the exact change on the commit below This will be the system prompt going forward. The previous 6 models already tested will not be re-tested due to costs (estimated at $40K in api costs) github.com/arcprize/arc-agi-… * We also noticed that Opus 4.8 used over $10K (our testing limit) in api costs for Semi Private testing (55 games). Going forward we will be implementing a hard max per game for costs
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