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This paper proposes MOCO, a novel diffusion-based framework capable of processing multiple simultaneous inputs, including speech audio, text descriptions, and trajectory data, to generate coherent and lifelike motions without requiring aligned multimodal data.
SearchMaster is presented, a self-play framework that trains a single LLM from search tasks it generates, solves, and verifies in a local search environment, showing that grounded and regulated self-play can provide effective search-agent training data without human-labeled QA pairs or expert demonstrations.
A benchmark that evaluates whether LLM auditors can localize, attribute, and repair search-agent failures through evidence-grounded adjudication, and proposes SearchAuditor, a multi-perspective auditing framework that effectively localizes, attributes, and repairs search-agent failures through evidence-grounded adjudic...
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