NeuroMAS reframes multi-agent language systems as neural architectures where LLM agents learn coordination via reinforcement learning rather than predefined roles.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages =
4 Pith papers cite this work. Polarity classification is still indexing.
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UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.
Machine learning research should prioritize ideas by testing their predicted behavioral signatures in modern models through custom experiments instead of leaderboard chasing or abstract theorems.
citing papers explorer
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NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
NeuroMAS reframes multi-agent language systems as neural architectures where LLM agents learn coordination via reinforcement learning rather than predefined roles.
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UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
UTOPYA fuses eight modalities via FiLM-conditioned attention and physics-informed regularization to reach AUROC 0.874 for anomaly detection in batch distillation, outperforming baselines by 0.147.
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Position: Ideas Should be the Center of Machine Learning Research
Machine learning research should prioritize ideas by testing their predicted behavioral signatures in modern models through custom experiments instead of leaderboard chasing or abstract theorems.
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