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Multi-agent communication aims to help agents benefit from one another's information. Yet improvements in system performance leave a fundamental ambiguity: do they reflect effective communication, a favorable agent architecture, or simply additional reasoning? Because communication methods are commonly evaluated within...
This work proposes HyperLabel, an encoder-decoder framework that explicitly models label dependencies through hypergraph neural networks, and proposes a unified cross-modal learning approach where HGNN+ performs bidirectional message passing to integrate feature information with label structure, and a shared cross-atte...
Brain-age prediction from resting-state fMRI provides a quantitative framework for characterizing age-related changes in spontaneous brain dynamics and for identifying functional signatures. Existing studies have linked fractal and multifractal scaling to age and examined the reliability of individual features. However...
AgentAuditor is introduced, which moves beyond frequency-based aggregation by organizing agent traces into a Reasoning Tree that explicitly represents agreements and divergences in their reasoning, and Anti-Consensus Preference Optimization (ACPO), which trains the adjudicator with evidence-verified preference supervis...
CARE is introduced, a reference-conditioned controller that separates program synthesis from experiment selection and attains the lowest normalized regret, the highest normalized best-so-far AUC, and the highest Top-1% Success@15 among the evaluated methods.
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