arXiv:2606. 01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience.
By Manu Srinath Halvagal, Sebastian Lee, SueYeon Chung
arXiv:2606. 03017v1 Announce Type: cross Abstract: Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals.
By Yikang Gui, Bikramjit Banerjee, Prashant Doshi
arXiv:2608. 17959v1 Announce Type: new Abstract: State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on the structure of the underlying environment.
By Isidoro Tamassia, Lennert De Smet, Giuseppe Marra
arXiv:2606. 14975v1 Announce Type: cross Abstract: How the wiring and functional organization of cortex shape recurrent computation remains a central question in both neuroscience and machine learning.
By Mo Shakiba, Rana Rokni, Mohammad Mohammadi, Nima Dehghani
arXiv:2512. 12225v3 Announce Type: replace Abstract: Developing artificial agents that unify representation, memory, adaptation, and prediction remains a fundamental challenge in artificial intelligence.
By Laha Ale
arXiv:2512. 10282v4 Announce Type: replace Abstract: Attention improves representation learning over RNNs, but its discrete nature limits continuous-time (CT) modeling.
By Waleed Razzaq, Yun-Bo Zhao