To balance effectiveness and efficiency in recommender systems, multi-stage pipelines commonly use lightweight two-tower models for large-scale candidate retrieval. However, the isolated two-tower architecture restricts representation capacity, embedding-space alignment, and cross-feature interactions. Existing solutions such as late interaction and knowledge distillation can mitigate these issues, but often increase latency or are difficult to deploy in online learning settings. We propose Capability Synergy (CS3), an efficient online framework that strengthens two-tower retrievers while preserving real-time constraints. CS3 introduces three mechanisms: (1) Cycle-Adaptive Structure for self-revision via adaptive feature denoising within each tower; (2) Cross-Tower Synchronization to improve alignment through lightweight mutual awareness between towers; and (3) Cascade-Model Sharing to enhance cross-stage consistency by reusing knowledge from downstream models. CS3 is plug-and-play with diverse two-tower backbones and compatible with online learning. Experiments on three public datasets show consistent gains over strong baselines, and deployment in a large-scale advertising system yields up to 8.36% revenue improvement across three scenarios while maintaining ms-level latency.

In this paper, a nonsingular fast terminal sliding mode control law based on finite-time convergent extended state observer is designed for building large-scale swarm orbital configurations. First, a finite-time extended state observer is proposed to estimate the unknown external disturbance including J 2 perturbation, atmosphere drag, etc. Second, based on the disturbance estimation, an orbital coordinated control law is developed to maintain the given configuration to achieve space missions. Finally, a simulation example is presented to illustrate the effectiveness of the proposed orbital coordinated control approach.