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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UNVERDICTED 3representative citing papers
ZeroCoder co-evolves coder and tester LLMs via self-generated code-test execution feedback to improve code generation up to 21.6% without ground-truth supervision.
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?
ZeroCoder co-evolves coder and tester LLMs via self-generated code-test execution feedback to improve code generation up to 21.6% without ground-truth supervision.
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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.