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ARC-AGI-3

11 articles · Page 1 of 1
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Sep 7, 2026 · Artificial Intelligence

GPT-6 Astra's Symbolic World Model: Breakthrough or $360-per-Question Brute Force?

GPT-6 Astra achieves near-perfect scores on the ARC-AGI-3 benchmark using a symbolic world model that internalizes reasoning and tool creation, but the $360-per-task compute cost and reliance on external harnesses raise questions about whether this represents genuine AGI progress or expensive brute-force engineering.

AGI benchmarkAI reasoningARC-AGI-3
0 likes · 11 min read
GPT-6 Astra's Symbolic World Model: Breakthrough or $360-per-Question Brute Force?
Top Architecture Tech Stack
Top Architecture Tech Stack
Sep 7, 2026 · Artificial Intelligence

GPT-6 Astra Benchmarks: 99.9% ARC-AGI-3, 4x Human Excel Speed

OpenAI's GPT-6 Astra achieves 99.9% on ARC-AGI-3, solves financial modeling tasks four times faster than human champions, and scores 100% on ExploitBench, outperforming Claude Opus 5 and GPT-5.6 Sol across coding, reverse engineering, and scientific workflow benchmarks.

AI Coding AgentsAI benchmarksAPI pricing
0 likes · 6 min read
GPT-6 Astra Benchmarks: 99.9% ARC-AGI-3, 4x Human Excel Speed
Baobao Algorithm Notes
Baobao Algorithm Notes
Sep 5, 2026 · Artificial Intelligence

GPT-6 Astra Scores 99.9% on ARC-AGI-3: Model Leap or Harness Win?

OpenAI's GPT-6 Astra achieves 99.9% on the ARC-AGI-3 benchmark using its native Provider Adapter harness, demonstrating novel behaviors like inventing algebraic shorthand, surpassing human action efficiency, and writing its own tools, though the official standard harness yields 62.7%, raising questions about whether this constitutes AGI.

AGIAI SafetyAI benchmarks
0 likes · 17 min read
GPT-6 Astra Scores 99.9% on ARC-AGI-3: Model Leap or Harness Win?
AI Architecture Path
AI Architecture Path
Aug 8, 2026 · Artificial Intelligence

Prime Agent Scores 95.5% on ARC‑AGI‑3: A Self‑Evolving AI Agent Framework

Prime Agent, an open‑source AI agent framework, achieves a 95.5% score on the ARC‑AGI‑3 benchmark—surpassing the human baseline—by introducing Recursive Language Model (RLM) and a Continual Harness that enable persistent sessions, self‑improvement, and long‑task execution, while the article also examines controversies, risks, and practical deployment guidance.

AI agentARC-AGI-3Prime Agent
0 likes · 15 min read
Prime Agent Scores 95.5% on ARC‑AGI‑3: A Self‑Evolving AI Agent Framework
Machine Heart
Machine Heart
Aug 7, 2026 · Artificial Intelligence

Prime Agent’s RLM Harness Beats ARC‑AGI‑3 but Sparks Controversy

Prime Agent, an open‑source agent framework, claims a 95.5% score on ARC‑AGI‑3 by engineering the RLM harness and a Continual Harness for self‑improvement and long‑running tasks, yet critics question the depth of its recursion, potential reward‑hacking, and whether the benchmark results reflect genuine general intelligence.

ARC-AGI-3Agent FrameworkPrime Agent
0 likes · 14 min read
Prime Agent’s RLM Harness Beats ARC‑AGI‑3 but Sparks Controversy
Machine Heart
Machine Heart
Jul 30, 2026 · Artificial Intelligence

How Two Settings Tripled GPT‑5.6 Sol’s ARC‑AGI‑3 Score

OpenAI found that enabling retained reasoning and context compression in the GPT‑5.6 Sol API raised its ARC‑AGI‑3 benchmark score from 13.3% to 38.3%—a three‑fold increase—while also cutting token usage by about six times, highlighting how evaluation frameworks and settings can mask a model’s true capabilities.

AI benchmarkingARC-AGI-3GPT-5.6
0 likes · 8 min read
How Two Settings Tripled GPT‑5.6 Sol’s ARC‑AGI‑3 Score
Machine Heart
Machine Heart
Jul 18, 2026 · Artificial Intelligence

How the [schema] Harness Achieved 99% RHAE on ARC‑AGI‑3 by Making AI Think Like a Physicist

The article explains how the [schema] harness, a lightweight framework that wraps large language models, transformed ARC‑AGI‑3 scores from sub‑10% to 98.98% by grounding observations into state representations, discovering mechanisms, and iteratively testing hypotheses, while also discussing the benchmark’s scoring rules, potential “cheating” concerns, and the broader implications for AI research.

AI benchmarkingARC-AGI-3LLM
0 likes · 14 min read
How the [schema] Harness Achieved 99% RHAE on ARC‑AGI‑3 by Making AI Think Like a Physicist
Machine Heart
Machine Heart
May 2, 2026 · Artificial Intelligence

Why GPT‑5.5 and Claude Opus 4.7 Score Below 1% on ARC‑AGI‑3 While Humans Achieve 100%

The ARC‑AGI‑3 benchmark shows that GPT‑5.5 (0.43%) and Claude Opus 4.7 (0.18%) fail to solve any of the 135 novel environments, whereas a six‑year‑old human solves them all, and the analysis attributes the gap to three concrete failure modes and differing compression abilities of the two models.

AI BenchmarkARC-AGI-3Claude Opus 4.7
0 likes · 10 min read
Why GPT‑5.5 and Claude Opus 4.7 Score Below 1% on ARC‑AGI‑3 While Humans Achieve 100%
Lao Guo's Learning Space
Lao Guo's Learning Space
Apr 1, 2026 · Artificial Intelligence

Humans Achieve 100% While Top AI Models Score Below 0.4% on ARC‑AGI‑3 Benchmark

In the ARC‑AGI‑3 test, 486 random humans solved all 150+ game‑based puzzles with a perfect 100% success rate in a median of 7.4 minutes, whereas leading models such as GPT‑5, Claude Opus 4.6, Gemini 3.1 Pro and Grok 4.20 managed at most 0.37%, exposing a stark gap in meta‑cognitive reasoning.

AGIARC-AGI-3Large Language Models
0 likes · 9 min read
Humans Achieve 100% While Top AI Models Score Below 0.4% on ARC‑AGI‑3 Benchmark