In real-world industrial scenarios, large language models (LLMs) require Continuous Learning (CL) to adapt to diverse tasks as opera- tional requirements diversify, demanding self-evolution capabilities to autonomously refine their knowledge and adapt to dynamic envi- ronments. However, existing CL approaches, such as replay-based and parameter isolation techniques, struggle with the catastrophic forgetting problem: new task training degrades performance on prior tasks due to the model's adaptation to new data distributions, which weakens its generalization to old tasks. To address this issue, we propose a novel parameter-efficient adversarial MoE framework, MoE-CL, for industrial-scale self-evolving continual instruction tuning of LLMs. Specifically, MoE-CL employs a dual-expert archi- tecture to enable self-evolution: a dedicated LoRA expert for each task to preserve task-specific knowledge, ensuring parameter inde- pendence and mitigating forgetting, and a shared LoRA expert to facilitate cross-task knowledge transfer. Specifically, a task-aware discriminator within a Generative Adversarial Network (GAN) is integrated into the shared expert to suppress task-irrelevant noise, ensuring only task-aligned knowledge is transferred during se- quential task training. Through adversarial training, the shared ex- pert learns generalized representations that mimic the task-aware discriminator, while dedicated experts retain task-specific details, balancing knowledge retention and cross-task generalization—key to the model's self-evolution by autonomously optimizing knowl- edge integration across tasks. Extensive experiments on a public MTL5 benchmark and an industrial Tencent3 benchmark validate MoE-CL's effectiveness in self-evolving continual learning. In real- world A/B testing on content compliance review in the Tencent Video Platform, MoE-CL reduced manual review costs by 15.3%,demonstrating its applicability for large-scale industrial deploy- ment where self-evolution is critical for adapting to evolving op- erational demands. Implementation code is publicly available at https://github.com/BAI-LAB/MoE-CL.

Drug-target interaction prediction (DTI) is essential in various applications including drug discovery and clinical application. There are two perspectives of input data widely used in DTI prediction: Intrinsic data represents how drugs or targets are constructed, and extrinsic data represents how drugs or targets are related to other biological entities. However, any of the two perspectives of input data can be scarce for some drugs or targets, especially for those unpopular or newly discovered. Furthermore, ground-truth labels for specific interaction types can also be scarce. Therefore, we propose the first method to tackle DTI prediction under input data and/or label scarcity. To make our model functional when only one perspective of input data is available, we design two separate experts to process intrinsic and extrinsic data respectively and fuse them adaptively according to different samples. Furthermore, to make the two perspectives complement each other and remedy label scarcity, two experts synergize with each other in a mutually supervised way to exploit the enormous unlabeled data. Extensive experiments on 3 real-world datasets under different extents of input data scarcity and/or label scarcity demonstrate our model outperforms states of the art significantly and steadily, with a maximum improvement of 53.53%. We also test our model without any data scarcity and it still outperforms current methods.

Large Language Models (LLMs) face a crucial challenge from fixed context windows and inadequate memory management, leading to a severe shortage of long-term memory capabilities and limited personalization in the interactive experience with AI agents.To overcome this challenge, we innovatively propose a Memory Operating System, i.e., Memo-ryOS, to achieve comprehensive and efficient memory management for AI agents.Inspired by the memory management principles in operating systems, MemoryOS designs a hierarchical storage architecture and consists of four key modules: Memory Storage, Updating, Retrieval, and Generation.Specifically, the architecture comprises three levels of storage units: short-term memory, mid-term memory, and long-term personal memory.Key operations within MemoryOS include dynamic updates between storage units: short-term to mid-term updates follow a dialogue-chain-based FIFO principle, while mid-term to long-term updates use a segmented page organization strategy.Extensive experiments on the LoCoMo benchmark show an average improvement of 49.11% on F1 and 46.18% on BLEU-1 over the baselines on GPT-4o-mini, showing contextual coherence and personalized memory retention in long conversations.