In-context learning (ICL) empowers generative models to address new tasks effectively and efficiently on the fly, without relying on any artificially crafted optimization techniques. In this paper, we study extending ICL to address a broader range of tasks with an extended learning horizon and higher improvement potential, namely General-Purpose In-Context Learning (GPICL). To this end, we introduce two lightweight benchmarks specifically crafted to train and evaluate GPICL functionalities. Each benchmark encompasses a vast number of tasks characterized by significant task variance, facilitating meta-training that minimizes inductive bias. These tasks are also crafted to promote long-horizon in-context learning through continuous generation and interaction. These characteristics necessitate the models to leverage contexts and history interactions to enhance their capabilities, across domains such as language modeling, decision-making, and world modeling. Our experiments on the baseline models demonstrate that meta-training with minimal inductive bias and ICL from the ground up is feasible across all the domains we've discussed. Additionally, our findings indicate that the scale of parameters alone may not be crucial for ICL or GPICL, suggesting alternative approaches such as increasing the scale of contexts and memory states.

With the rapid development of underwater communication, sensing, automation, robot technologies, autonomous underwater vehicle (AUV) swarms are gradually becoming popular and have been widely promoted in ocean exploration and underwater tracking or surveillance, etc. However, the complex underwater environment poses significant challenges for AUV swarm-based accurate tracking for the underwater moving targets. In this paper, we aim at proposing a multi-AUV cooperative underwater multi-target tracking algorithm especially when the real underwater factors are taken into account.We first give normally modelling approach for the underwater sonar-based detection and the ocean current interference on the target tracking process.Then, we regard the AUV swarm as a underwater ad-hoc network and propose a novel Multi-Agent Reinforcement Learning (MARL) architecture towards the AUV swarm based on Software-Defined Networking (SDN).It enhances the flexibility and scalability of the AUV swarm through centralized management and distributed operations.Based on the proposed MARL architecture, we propose the "dynamic-attention switching" and "dynamic-resampling switching" mechanisms, to enhance the efficiency and accuracy of AUV swarm cooperation during task execution.Finally, based on a proposed AUV classification method, we propose an efficient cooperative tracking algorithm called ASMA.Evaluation results demonstrate that our proposed tracking algorithm can perform precise underwater multi-target tracking, comparing with many of recent research products in terms of convergence speed and tracking accuracy.