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This work introduces a bounded, online step-level repair module that feeds the compiler's error message to a pretrained repair model to fix individual rejected steps in place and improves the solve rate over a sampling-only baseline across all tested candidate budgets.
Results show ARC can create and support rural robotics programs, train undergraduate AI and robotics mentors, and potentially scale mentorship across a region, as well as create a spatial Markov model of ARC's growth and simulate it using the state of Indiana as a testbed.
This work introduces Adaptive Agents via Latent Topologies (AALT), which requests demonstrations that maximize expected gains in start-goal connectivity and shows that this objective is formally tied to information gain about task reachability.
Elhuyar is introduced, a multi-agent, human-in-the-loop system that integrates LLMs, structured AI, and human scientists to extract, analyze, and iteratively refine insights from scientific literature.
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