MECO is a multimodal dataset of 38 hours of video, audio, EEG, and ECG data from 42 older adults annotated for emotional states and cognitive scores.
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3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Co-evolving coder and tester models via consensus over a self-generated pass matrix improves LLM code generation up to 14.5% label-free and 21.6% with a lightly calibrated Bayesian selector.
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
citing papers explorer
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MECO: A Multimodal Dataset for Emotion and Cognitive Understanding in Older Adults
MECO is a multimodal dataset of 38 hours of video, audio, EEG, and ECG data from 42 older adults annotated for emotional states and cognitive scores.
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ZeroCoder: Can LLMs Improve Code Generation Without Ground-Truth Supervision?
Co-evolving coder and tester models via consensus over a self-generated pass matrix improves LLM code generation up to 14.5% label-free and 21.6% with a lightly calibrated Bayesian selector.
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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.