Fine-tuning large language models (LLMs) is challenged by the presence of noisy data and the high computational cost when training on large-scale datasets. While data selection has emerged as a promising approach to reduce training cost and improve data quality, existing methods often rely on static heuristics or manual metrics. These approaches struggle to adapt to the model’s evolving capabilities during training, as its understanding of tasks improves. As the model becomes more powerful, its requirements for data that can enhance performance also change, making it crucial to incorporate this dynamic into the data selection process. Moreover, ensuring data diversity throughout different stages of training is essential for preventing redundancy, reducing overfitting. To address these issues, we propose DSP, a Diversity-Aware Self-Paced data selection framework that evolves with the model. DSP progressively selects training samples based on the model’s own outputs and incorporates a diversity-aware mechanism to enhance generalization and mitigate overfitting. Unlike prior static or rule-based strategies, DSP adaptively adjusts to the model’s internal feedback and training stage. Experiments on two public benchmarks demonstrate that DSP consistently outperforms static and heuristic-based baselines across multiple datasets and backbone models. Our findings highlight the critical role of dynamic, diversity-aware data selection in effective LLM fine-tuning.

The rapid advancement of Large Language Models (LLMs) has catalyzed the development of multi-agent systems, where multiple LLM-based agents collaborate to solve complex tasks. However, existing systems predominantly rely on centralized coordination, which introduces scalability bottlenecks, limits adaptability, and creates single points of failure. Additionally, concerns over privacy and proprietary knowledge sharing hinder cross-organizational collaboration, leading to siloed expertise. To address these challenges, we propose AgentNet, a decentralized, Retrieval-Augmented Generation (RAG)-based framework that enables LLM-based agents to autonomously evolve their capabilities and collaborate efficiently in a Directed Acyclic Graph (DAG)-structured network. Unlike traditional multi-agent systems that depend on static role assignments or centralized control, AgentNet allows agents to specialize dynamically, adjust their connectivity, and route tasks without relying on predefined workflows. AgentNet’s core design is built upon several key innovations: (1) Fully Decentralized Paradigm: Removing the central orchestrator, allowing agents to coordinate and specialize autonomously, fostering fault tolerance and emergent collective intelligence. (2) Dynamically Evolving Graph Topology: Real-time adaptation of agent connections based on task demands, ensuring scalability and resilience. (3) Adaptive Learning for Expertise Refinement: A retrieval-based memory system that enables agents to continuously update and refine their specialized skills. By eliminating centralized control, AgentNet enhances fault tolerance, promotes scalable specialization, and enables privacy-preserving collaboration across organizations. Through decentralized coordination and minimal data exchange, agents can leverage diverse knowledge sources while safeguarding sensitive information. Experimental results demonstrate that AgentNet outperforms traditional centralized multi-agent systems, significantly improving efficiency, adaptability, and scalability in dynamic environments, making it a promising foundation for next-generation autonomous, privacy-respecting multi-agent ecosystems.

Several large language model (LLM) agents have been constructed for diverse purposes such as web navigation and online shopping, leveraging the broad knowledge and text comprehension capabilities of LLMs. Many of these works rely on in-context examples to achieve generalization without requiring fine-tuning. However, few have addressed the challenge of selecting and effectively utilizing these examples. Recent approaches have introduced trajectory-level retrieval with task meta-data and the use of trajectories as in-context examples to enhance overall performance in some sequential decision making tasks like computer control. Nevertheless, these methods face issues like plausible examples retrieved without task-specific state transition dynamics and long input with plenty of irrelevant context due to using complete trajectories. In this paper, we propose a novel framework (TRAD) to tackle these problems. TRAD first employs Thought Retrieval for step-level demonstration selection through thought matching, enhancing the quality of demonstrations and reducing irrelevant input noise. Then, Aligned Decision is introduced to complement retrieved demonstration steps with their preceding or subsequent steps, providing tolerance for imperfect thought and offering a balance between more context and less noise. Extensive experiments on ALFWorld and Mind2Web benchmarks demonstrate that TRAD not only surpasses state-of-the-art models but also effectively reduces noise and promotes generalization. Furthermore, TRAD has been deployed in real-world scenarios of a global business insurance company and yields an improved success rate of robotic process automation. Our codes are available at: https://github.com/skyriver-2000/TRAD-Official.

Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware counters.We propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth’s interpretability, providing insights to benefit the decision-making of cloud operators. The dataset and code of Alioth have been released on GitHub.

Real-Time Bidding (RTB) has shown remarkable success in display advertising and has been employed in other advertising scenarios, e.g., sponsored search advertising with multiple ad slots. Many current RTB techniques built for single-slot display advertising are thus no longer applicable, especially in the bid landscape forecasting. Landscape forecasting predicts market competition, including the highest bid price and winning probability, which is preliminary and crucial for the subsequent bidding strategy design. In the multi-slot advertising, predicting the winning prices for each position requires a more precise differentiation of bids among top advertisers. Furthermore, defining the winning probability and addressing censorship issues are not as straightforward as in the case of a single slot. In view of these challenges, how to forecast the bidding landscape in the multi-slot environment remains open.