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Continual Harness: An Efficient Self-Improving Agent on ARC-AGI-3
Continual Harness is a new framework designed to optimize agent self-improvement specifically for the ARC-AGI-3 benchmark.
What: Continual Harness facilitates test-time learning, allowing agents to iteratively build and refine their internal world models based on task mechanics and new feedback data.
Why it matters: The ARC-AGI benchmark is increasingly viewed as the standard for measuring reasoning capabilities over simple pattern recognition, making framework efficiency here a key research metric.
Decoder
- ARC-AGI-3: The Abstraction and Reasoning Corpus, a benchmark designed to test AI intelligence by requiring agents to solve novel logic puzzles rather than relying on memorized training data.
Original article
Continual Harness: An Efficient Self-Improving Agent on ARC-AGI-3
ARC-AGI-3 is an IQ test for agents. The heavy test-time learning required by the benchmark pushes agents to form an internal world model of the rules and mechanics that updates with new evidence.
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