Model architectures

Sebastian Raschka reviews changes in language-model research, including reasoning-focused post-training, inference-time computation and reinforcement learning with verifiable rewards. The discussion connects these methods with self-consistency, model architectures, efficient attention and long contexts, while examining limits in continual learning and the reliability of agent workflows.

Neural networks are now widely used for modeling neural activity in the brain. They have been particularly successful in modeling the visual system, using mostly feedforward networks and leveraging community-wide efforts centered around benchmarks to both improve model architectures and evaluate model fits to data. Now that recurrent neural networks (RNNs) are also used to model a larger variety of brain functions, there is a similar need for developing appropriate metrics to compare these models. Towards this goal, we have built a high-throughput pipeline for training different RNN models on