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
The paper introduces a neurosymbolic world model that separates observation reconstruction from reward prediction, enabling the model to adapt zero‑shot to new reward functions defined over a shared symbolic state space. This approach addresses the task‑dependency of traditional neural world models, which learn latent representations tied to specific training tasks. Experiments show that the neurosymbolic formulation generalises more strongly than purely neural methods.
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