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ASH is introduced, an agentic system that learns a long-horizon policy from unlabeled, noisy internet video, without reward shaping or expert annotation, and it is demonstrated that self-improving agents are a scalable recipe for learning in long-horizon worlds.
X-Tree improves over standard recipes at matched data and budget by up to 4.5% SR on WebArena, 5.8% SR on ScienceWorld and 4.1% success on WebShop, and matched analyses attribute the gains to the X-Tree structure and the three integrations.
LakeQuest is introduced, a human-validated benchmark of 9,846 QA pairs designed to evaluate the end-to-end retrieve-and-synthesize pipeline over realistic data lakes and exposes critical failure modes in modern QA systems.
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