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The ARC Prize is an ongoing international competition designed to incentivize and accelerate progress towards Artificial general intelligence (AGI). Presented by the non-profit ARC Prize Foundation, the competition challenges participants to develop AI systems capable of achieving human-level performance on the Abstraction and Reasoning Corpus (ARC-AGI) benchmark, created by François Chollet. The prize emphasizes the development of AI that demonstrates efficient skill acquisition and robust problem-solving on novel tasks, rather than relying primarily on memorization or performance on tasks seen during training.^[1][2]

The ARC Prize was established to address a perceived gap in AI evaluation, focusing on a definition of intelligence centered on the ability to adapt and solve new problems efficiently, distinct from task-specific skills acquired through large-scale data memorization.^[1] It utilizes the ARC-AGI benchmark, a set of visual reasoning puzzles designed to be easily solvable by humans using "core knowledge" (basic concepts of objectness, physics, geometry, etc.) but challenging for current AI systems due to the novelty of each task.^[2][3] The organizers argue that progress on ARC-AGI serves as a better indicator of true generalization ability and a necessary step towards AGI than performance on benchmarks that risk saturation through memorization.^[1] The competition aims to foster innovation, particularly novel approaches beyond simply scaling existing models like Large language models (LLMs), and promotes open-source sharing of solutions.^[1][2]

The ARC-AGI benchmark was created by François Chollet and released in 2019 alongside his paper "On the Measure of Intelligence", which proposed efficient skill acquisition as a key characteristic of general intelligence.^[2] The benchmark was designed to resist memorization and test abstract reasoning capabilities.

Before the formal ARC Prize, the benchmark was the subject of several competitions:

• 2020: First ARC-AGI Kaggle competition with a $20,000 prize pool. The top score achieved was 21%.^[2] Solutions primarily relied on brute-force program search techniques.
• 2022 & 2023: "ARCathons" were organized in collaboration with the non-profit AI lab Lab42, offering $100,000 in prizes each year.^[2] The top score increased modestly, reaching around 30-33% by 2023, indicating the benchmark's resistance to the concurrent rapid advancements in LLMs.^[2]

Observing the benchmark's resilience and believing that progress towards AGI required new ideas beyond LLM scaling, Mike Knoop (co-founder of Zapier) collaborated with François Chollet and others to launch the ARC Prize with a significantly larger prize pool.^[1] The ARC Prize Foundation was established to manage the competition.

The 2024 competition ran from June 11 to November 10, 2024, offering over $1 million in potential prizes, including a $600,000 Grand Prize for the first team to achieve an 85% score on the private evaluation set using an open-source solution.^[2][1] While the Grand Prize went unclaimed, the competition saw significant progress, with the top open-source score reaching 53.5% and a closed-source entry reaching 55.5%.^[2] The competition catalyzed renewed interest and the development of new techniques, notably Test-Time Training (TTT) and advanced program synthesis methods.^[2]

The ARC Prize 2025 was announced to continue the challenge, utilizing a new version of the benchmark, ARC-AGI-2.^[2] ARC-AGI-2 was developed to address limitations identified in ARC-AGI-1. It features tasks extensively calibrated against human performance (tested on ~400 individuals) to ensure feasibility while maintaining difficulty for AI. It removes tasks deemed "low-signal" or overly susceptible to brute-force search and emphasizes problems requiring higher levels of abstraction and recombination.^[2] The organizers also plan to introduce separate datasets for intermediate leaderboard scoring and final evaluation to mitigate overfitting risks from the previous private set's repeated use.^[2]

The ARC Prize generally features multiple tracks:

• Main Competition Track (Kaggle): This is the primary track for prize eligibility.
  • Environment: Runs within a constrained environment on Kaggle, with limitations on compute, runtime (e.g., 12 hours for 100 tasks), and no internet access.^[2][1]
  • Goal: To develop efficient, self-contained AI systems capable of solving ARC-AGI tasks.
  • Evaluation: Submissions are evaluated against private ARC-AGI dataset.
  • Requirement: Winning solutions (claiming prize money) must be open-sourced.^[2][1]

• Public Leaderboard (ARC-AGI-Pub): A secondary track for benchmarking, particularly for large-scale or commercial models.
  • Environment: Allows internet access and significantly higher compute budgets (up to $10,000 in API credits).^[2]
  • Goal: To assess the capabilities of frontier models and approaches that may not fit the constraints of the main track.
  • Evaluation: Uses a semi-private evaluation sets. Scores are monitored for potential overfitting or data contamination.^[2]

• Paper Awards: Prizes awarded for research papers detailing novel concepts or approaches relevant to solving ARC-AGI, regardless of their solution's leaderboard score.^[2]

The core challenge involves inferring the underlying task rule from a few input-output grid examples ("demonstration pairs") and applying that rule to new "test inputs" to generate the correct output grid.^[2]

The ARC Prize features a substantial prize pool, intended to be awarded annually until the main goal is achieved.^[1]

• Grand Prize: $700,000 awarded to the first team that achieves an 85% score (considered a baseline for average human performance) on the private evaluation set, provided their solution is efficient and open-sourced.^[2][1] As of the start of the 2025 competition, this prize remains unclaimed.
• Progress Prizes: If the Grand Prize is not awarded, smaller prizes are distributed annually. In 2024, $100,000 was awarded, split between top leaderboard performers ($50,000) and best paper submissions ($50,000).^[2][1]
• Open Source Requirement: A key condition for claiming monetary prizes in the main competition track is the public release of the winning code under an open-source license.^[2][1]

Despite its relatively short history as a large-scale prize, the ARC Prize and the underlying ARC-AGI benchmark have had a noticeable impact on the AI research landscape:

• Benchmark Adoption: ARC-AGI has been adopted as a key benchmark by several AI startups focused on AGI (including Basis AI, Agemo, Symbolica, Tufa Labs) and is used internally by large AI labs like OpenAI and Google.^[2] OpenAI notably used ARC-AGI performance to demonstrate capabilities of a frontier system in late 2024.^[4]
• Shifted Research Focus: The prize successfully drew attention to the limitations of pure scaling approaches and encouraged research into alternative paradigms like program synthesis and methods for test-time adaptation.^[2][1]
• Technique Development: The 2024 prize saw the popularization and refinement of techniques like Test-Time Training (TTT) and various forms of deep learning-guided program synthesis specifically for ARC-AGI.^[2]
• State-of-the-Art Advancement: The 2024 competition significantly pushed the state-of-the-art score on the ARC-AGI-1 private evaluation set from approximately 33% to over 55%.^[2]
• Community Growth: It fostered an open-source community contributing tools, datasets (e.g., ConceptARC, RE-ARC), and shared approaches related to the benchmark.^[2]

While generally seen as a valuable contribution, the ARC Prize and benchmark are subject to discussion regarding their limitations and interpretation:

• Definition of Intelligence: The prize is predicated on Chollet's definition of intelligence (efficient skill acquisition on novel tasks), which implicitly critiques the dominant scaling paradigm for LLMs. This places the prize within the broader debate about the nature of intelligence and the most promising paths toward AGI, a debate where there is no universal consensus.^[1] Some proponents of LLM scaling argue that sufficient scale, potentially combined with architectural improvements or techniques like reinforcement learning, will lead to the type of generalization ARC measures, viewing ARC's current difficulty as a temporary limitation rather than a fundamental one.^[1]