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Multi-agent debate (MAD) has emerged as an effective paradigm to improve the reasoning capabilities of large language models (LLMs) and is increasingly being extended to multimodal settings. However, existing multimodal MAD frameworks typically expose agents to the same fixed visual input, ignoring substantial variatio...
A novel and effective oversampling method that leverages a predictive model to generate nominal variables, rather than interpolating them and is significantly better than existing oversampling approaches in predicting elevated symptoms of amotivation and anhedonia.
Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predictor-outcome relationship and effect structure to be specified a priori. Nonlinear ML models learn complex prediction surfaces that do not admit straightforward analytical...
This work proposes Creative-MAD, which introduces two synergistic interventions to sustain agent divergence, and significantly enhances both lexical and semantic diversity while maintaining MAD's output quality.
By improving the reliable identification of non-responders, the method provides a robust computational tool to help clinicians rapidly pivot to adjunctive therapies, thereby personalizing and optimizing mental health care pathways.
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