100 个认知任务联合训练：研究 RNN 中的多任务相互作用
Multitask Interactions in Recurrent Dynamics across 100 Cognitive Tasks
用 AL-RNN 在 100 个认知任务上同时训练，解释了任务间什么时候互相帮助、什么时候互相打架，做神经计算建模的可以看看。
研究在 100 个覆盖不同认知需求的任务上同时训练层级化 Almost-Linear RNN（AL-RNN），考察任务间何时迁移、何时干扰。结果显示，联合训练的层级化模型优于单独训练的模型和单个非结构化联合模型，性能随非线性容量和任务特征维度提升。许多任务单独可用线性动力学求解，但共享线性模型无法同时求解。模型通过层级任务特征、线性区域使用和任务几何提供多种任务相似性度量，并与任务复杂度、成对迁移学习和留一法适应相联系。
Multitask Interactions in Recurrent Dynamics across 100 Cognitive Tasks
The brain continually learns tasks with very different computational demands. Recurrent neural networks (RNNs) have been used as dynamical surrogates for studying task solutions, with recent work extending this approach to joint training on multitask batteries. However, it remains unclear how tasks interact as these batteries grow in scale and heterogeneity: under what conditions do they transfer or interfere with one another, and how do these effects relate to task complexity and model structure? We study this question by simultaneously training hierarchical Almost-Linear RNNs (AL-RNNs) on 100 tasks spanning a wide range of cognitive demands. AL-RNNs allow us to vary the amount of nonlinearity in the recurrence, while hierarchical task features provide a low-dimensional interface for task-specific adaptation within shared dynamics. On this task battery, performance improves with both nonlinear capacity and task-feature dimensionality, with the joint hierarchical model outperforming both individually trained models and a single unstructured joint model. Many tasks are individually solvable with linear dynamics, yet cannot be solved together by a shared linear model. Through hierarchical task features, task-specific linear region usage, and task geometry, the trained model provides several related but distinct measures of task similarity that capture how computations are separated and combined within the shared backbone. Across a series of experiments, we relate these representations to individual task complexity, joint solvability, pairwise transfer learning, and leave-one-out adaptation. Together, our experiments characterize task interactions in multitask recurrent learning while demonstrating both the potential and the challenges of reusable dynamical backbones for cognitive computation.
