A June 30, 2026 post says that learning during inference in the ARC-AGI-3 benchmark leads agents to build and update internal models of the environment’s rules and mechanics.
A post titled “Continual Harness: An Efficient Self-Improving Agent on ARC-AGI-3,” published on June 30, 2026, discusses agents in the ARC-AGI-3 benchmark. According to the post, the requirements for learning during inference lead agents to create an internal world model of rules and mechanics, updated as new evidence appears.
The post emphasizes the role of updating this internal model during learning at inference time. To check the context and verify the claim, consult the original post by title and compare its description with the ARC-AGI-3 benchmark material; the available summary provides no quantitative results or further methodological details.