arXiv AI July 22, 2026 By Satpreet H. Singh, Sonja Johnson-Yu, Zhouyang Lu, Aaron Walsman, Federico Pedraja, Denis Turcu, Pratyusha Sharma, Naomi Saphra, Nathaniel B. Sawtell, Kanaka Rajan

Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives

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arXiv:2511. 08436v2 Announce Type: replace-cross Abstract: How complex collective behavior emerges from individual interactions is a fundamental scientific question, but experimental cost and difficulty of simultaneous multi-brain recordings limit direct study in animals.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

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A Deep Reinforcement Learning Framework for Closed-loop Guidance of Fish Schools via Virtual Agents

arXiv:2603.28200v2 Announce Type: replace-cross Abstract: Guiding collective motion in biological groups is a fundamental challenge in understanding social interaction rules. In this study, we propos...

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agents reinforcement-learning

arXiv AI

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SeekBrain: An Autonomous Multi-Agent System for Accelerating Neuroscience Discovery

arXiv:2607. 29347v1 Announce Type: cross Abstract: Modern neuroscience relies on integrating multi-scale, multimodal datasets to uncover the neural principles underlying intelligence.

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agents multimodal benchmarks

arXiv Machine Learning

Jul 23

Agent-Centric Animal Pose Forecasting

arXiv:2607. 19548v1 Announce Type: new Abstract: Understanding animal behavior at an algorithmic level -- what animals attend to, how they form internal models and plans, and how this maps to action -- remains a central challenge in neuroscience and ethology.

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arXiv Machine Learning

Aug 31

Emergent aggregation from collective foraging

arXiv:2608.28046v1 Announce Type: cross Abstract: Collective behaviour in living systems is usually modelled as the outcome of a \emph{direct} social drive: agents are rewarded, or hard-wired, to ali...

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arXiv Machine Learning

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Leveraging unlabelled data for generalizable neural population decoding

arXiv:2607. 14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments.

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arXiv AI

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A latent control model for realistic rodent motion

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