Di Bai

Xidong Wu, Yue Zhuan, Ruoqiao Wei et al.

Modern large-scale recommendation systems are typically constructed as multi-stage pipelines, encompassing pre-ranking, ranking, and re-ranking phases. While traditional recommendation research typically focuses on optimizing a specific model, such as improving the pre-ranking model structure or ranking models training algorithm, system-level configurations optimization play a crucial role, which integrates the output from each model head to get the final score in each stage. Due to the complexity of the system, the configuration optimization is highly important and challenging. Any model modification requires new optimal system-level configurations. But each experimental iteration requires significant tuning effort. Furthermore, models in different stage operates within a distinct context and optimizes for different targets, requiring specialized domain expertise. In addition, optimization success depends on balancing competing multiple online metrics and alignment with shifting production development objectives. To address these challenges, we propose AgenticRecTune, an agentic framework comprising five specialized agents, Actor, Critic, Insight, Skill, and Online, designed to manage the end-to-end configuration optimization workflow. By leveraging the advanced reasoning of Large Language Models (LLMs), specifically Gemini, AgenticRecTune explore the optimal configuration spaces. The Actor Agent proposes multiple candidates and Critic Agent filters out suboptimal proposals.Then Online Agent autonomously prepares A/B tests based on the proposed configurations set from the Critic Agent and captures the subsequencet experimental results. We also introduce a self-evolving Skillhub, which utilizes a collaboration between the Insight Agent and Skill Agent to summarize the history results, extract underlying mechanics of each task in recommendation system and update skills.

Nianyu Li, Di Bai, Zhuoqun Yang et al.

Self-adaptive software is considered as the most advanced approach and its development attracts a lot of attention. Decentralization is an effective way to design and manage the complexity of modern self-adaptive software systems. However, there are still tremendous challenges. One major challenge is to unify decentrality with traditional self-adaptive implementation framework during design and implementation activity. One is to guarantee the required global goals and performance of decentralized self-adaptive systems operating in highly dynamic and uncertain environments. Another challenge is to predict the influence of system's internal change on its self-adaptability to the environment. To solve these problems, we combine the mechanisms of separation of concerns with modeling method using timed automata to allow the system to be analyzed and verified. Timed computation tree logic is used to specify system goals and stochastic simulations in dynamic environment are experimented to verify decentralized self-adaptive system's adaptation properties. In this paper, we extracted a motivation example from practical applications in UAV emergency mission scenarios. The whole approach is evaluated and illustrated with this motivation example and the statistical results can be used as reference for arrangement planning of UAVs in cyber physical spaces.