With the widespread application of LLM-based agents across various domains, their complexity has introduced new security threats.Existing red-team methods mostly rely on modifying user prompts, which lack adaptability to new data and may impact the agent's performance.To address the challenge, this paper proposes the JailAgent framework, which completely avoids modifying the user prompt.Specifically, it implicitly manipulates the agent's reasoning trajectory and memory retrieval with three key stages: Trigger Extraction, Reasoning Hijacking, and Constraint Tightening.Through precise trigger identification, real-time adaptive mechanisms, and an optimized objective function, JailAgent demonstrates outstanding performance in cross-model and cross-scenario environments.

The Ethereum ecosystem, which secures over $381 billion in assets, fundamentally relies on client APIs as the sole interface between users and the blockchain. However, these critical APIs suffer from widespread implementation inconsistencies, which can lead to financial discrepancies, degraded user experiences, and threats to network reliability. Despite this criticality, existing testing approaches remain manual and incomplete: they require extensive domain expertise, struggle to keep pace with Ethereum’s rapid evolution, and fail to distinguish genuine bugs from acceptable implementation variations. We present APIDiffer , the first specification-guided differential testing framework designed to automatically detect API inconsistencies across Ethereum’s diverse client ecosystem. APIDiffer transforms API specifications into comprehensive test suites through two key innovations: (1) specification-guided test input generation that creates both syntactically valid and invalid requests enriched with real-time blockchain data, and (2) specification-aware false positive filtering that leverages large language models to distinguish genuine bugs from acceptable variations. Our evaluation across all 11 major Ethereum clients reveals the pervasiveness of API bugs in production systems. APIDiffer uncovered 72 bugs, with 90.28% already confirmed or fixed by developers, including one critical error in the official specifications themselves. Beyond these raw numbers, APIDiffer achieves up to 89.67% higher code coverage than existing tools and reduces false positive rates by 37.38%. The Ethereum community’s response validates our impact: developers have integrated our test cases, expressed interest in adopting our methodology, and escalated one bug to the official Ethereum Project Management meeting. By making APIDiffer open-source, we enable continuous validation of Ethereum client API implementations, thereby strengthening the foundational integrity of the entire Ethereum ecosystem.

Controllable 3D scene generation has extensive applications in virtual reality and interior design, where the generated scenes should exhibit high levels of realism and controllability in terms of geometry. Scene graphs provide a suitable data representation that facilitates these applications. However, current graph-based methods for scene generation are constrained to text-based inputs and exhibit insufficient adaptability to flexible user inputs, hindering the ability to precisely control object geometry. To address this issue, we propose MMGDreamer, a dual-branch diffusion model for scene generation that incorporates a novel Mixed-Modality Graph, visual enhancement module, and relation predictor. The mixed-modality graph allows object nodes to integrate textual and visual modalities, with optional relationships between nodes. It enhances adaptability to flexible user inputs and enables meticulous control over the geometry of objects in the generated scenes. The visual enhancement module enriches the visual fidelity of text-only nodes by constructing visual representations using text embeddings. Furthermore, our relation predictor leverages node representations to infer absent relationships between nodes, resulting in more coherent scene layouts. Extensive experimental results demonstrate that MMGDreamer exhibits superior control of object geometry, achieving state-of-the-art scene generation performance.

Predicting web service traffic is crucial for system operation tasks including dynamic resource scaling, anomaly detection, and fraud detection. Web service traffic is characterized by frequent and drastic fluctuations over time and are influenced by heterogeneous user behaviors, making accurate prediction a challenging task. Previous research has extensively explored statistical approaches, and neural networks to mine features from preceding service traffic time series for prediction. However, these methods have largely overlooked the latent causal relationships between services. Drawing inspiration from causality in ecological systems, we empirically recognize the causal relationships between web services. To leverage these relationships for improved traffic prediction, we propose an effective neural network module, CCMPlus, designed to extract causal relationship features across services. This module can be seamlessly integrated with existing time series models to consistently enhance the performance of traffic predictions. We theoretically justify that the causal correlation matrix generated by the CCMPlus module captures causal relationships among services. Empirical results on real-world datasets from Microsoft Azure, Alibaba Group, and Ant Group confirm that our method surpasses state-of-the-art approaches in Mean Squared Error and Mean Absolute Error for predicting service traffic time series. These findings highlight the efficacy of feature representations from the CCMPlus module.

Fine-grained category discovery using only coarse-grained supervision is a cost-effective yet challenging task.Previous training methods focus on aligning query samples with positive samples and distancing them from negatives.They often neglect intra-category and intercategory semantic similarities of fine-grained categories when navigating sample distributions in the embedding space.Furthermore, some evaluation techniques that rely on precollected test samples are inadequate for realtime applications.To address these shortcomings, we introduce a method that successfully detects fine-grained clusters of semantically similar texts guided by a novel objective function.The method uses semantic similarities in a logarithmic space to guide sample distributions in the Euclidean space and to form distinct clusters that represent fine-grained categories.We also propose a centroid inference mechanism to support real-time applications.The efficacy of the method is both theoretically justified and empirically confirmed on three benchmark tasks.The proposed objective function is integrated in multiple contrastive learning based neural models.Its results surpass existing state-of-the-art approaches in terms of Accuracy, Adjusted Rand Index and Normalized Mutual Information of the detected fine-grained categories.Code and data are publicly available at

Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operating systems, LLM agents face three primary challenges. Firstly, the action space is vast and dynamic, posing difficulties for LLM agents to maintain an up-to-date understanding and deliver accurate responses. Secondly, real-world tasks often require inter-application cooperation, demanding farsighted planning from LLM agents. Thirdly, agents need to identify optimal solutions aligning with user constraints, such as security concerns and preferences. These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system. To address high-cost of manpower, we design a scalable and semi-automated method to construct the benchmark. In the task evaluation, AndroidArena incorporates accurate and adaptive metrics to address the issue of non-unique solutions. Our findings reveal that even state-of-the-art LLM agents struggle in cross-APP scenarios and adhering to specific constraints. Additionally, we identify a lack of four key capabilities, i.e. understanding, reasoning, exploration, and reflection, as primary reasons for the failure of LLM agents. Furthermore, we provide empirical analysis on the failure of reflection, and improve the success rate by 27% with our proposed exploration strategy. This work is the first to present valuable insights in understanding fine-grained weakness of LLM agents, and offers a path forward for future research in this area. Environment, benchmark, prompt, and evaluation code for AndroidArena are released at https://github.com/AndroidArenaAgent/AndroidArena.

As data scales continue to expand, graph structures are widely applied across multiple domains due to their effective organization of complex data. Graph Pattern Matching (GPM) is a fundamental task in graph analysis to identify all user-interesting subgraphs in a graph. Current GPM systems achieve this goal by generating efficient traversal path strategies. However, when matching patterns that include a specific vertex (S-GPM), current GPM systems often traverse paths without the specific vertex or duplicate traverse some paths. These redundant traversals lead to decreased execution efficiency. In this paper, we introduce AnchorMine, a GPM system designed for S-GPM tasks, aiming to significantly reduce redundant path traversal by identifying and reusing paths that include specific vertex. Specifically, AnchorMine first analyzes the pattern to identify vertices in different positions within the pattern, named Anchors (ACs). Then AnchorMine extracts features of reusable paths based on each Anchor (AC). These features enable the system to identify paths that can be reused during matching. Using these features, it further generates the parameters required for matching based on path reuse, achieving efficient matching for S-GPM tasks. In experiments on 8 real-world graph datasets, AnchorMine significantly outperformed GraphPi, SandSlash, and Peregrine on 6 datasets used for performance testing, with matching performance improvements of 3249.22 ×, 2018.73 × and 7573.27 ×, respectively. On the remaining 2 datasets used for scalability testing, AnchorMine scales well.

Sharding provides an opportunity to overcome the inherent scalability challenges of the blockchain, which is the infrastructure for the next generation of the Web. In a sharding blockchain, the state is partitioned into smaller groups known as "shards." Since the states are placed on different shards, cross-shard transactions are inevitable, which is detrimental to the performance of the sharding blockchain. Existing solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less effective or costly. In this paper, we present SPRING, the first deep-reinforcement-learning(DRL)-based sharding framework for state placement. SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy. Experimental results based on real Ethereum transaction data demonstrate the superiority of SPRING compared to other state placement solutions. In particular, it decreases the cross-shard transaction ratio by up to 26.63% and boosts throughput by up to 36.03%, all without unduly sacrificing the workload balance among shards. Moreover, updating the training model and making decisions takes only 0.1s and 0.002s, respectively, which shows the overhead is acceptable.

Public cloud GPU clusters are becoming emerging platforms for training distributed deep learning jobs. Under this training paradigm, the job scheduler is a crucial component to improve user experiences, i.e., reducing training fees and job completion time, which can also save power costs for service providers. However, the scheduling problem is known to be NP-hard. Most existing work divides it into two easier sub-tasks, i.e., ordering task and placement task, which are responsible for deciding the scheduling orders of jobs and placement orders of GPU machines, respectively. Due to the superior adaptation ability, learning-based policies can generally perform better than traditional heuristic-based methods. Nevertheless, there are still two main challenges that have not been well-solved. First, most learning-based methods only focus on ordering or placement policy independently, while ignoring their cooperation. Second, the unbalanced machine performances and resource contention impose huge overhead and uncertainty on job duration, but rarely be considered in existing work. To tackle these issues, this paper presents a dual-agent scheduler framework abstracted from the two sub-tasks to jointly learn the ordering and placement policies and make better-informed scheduling decisions. Specifically, we design an ordering agent with a scalable squeeze-and-communicate strategy for better cooperation; for the placement agent, we propose a novel Random Walk Gaussian Process to learn the performance similarities of GPU machines while being aware of the uncertain performance fluctuation. Finally, the dual-agent is jointly optimized with multi-agent reinforcement learning. Extensive experiments conducted on the real-world production cluster trace demonstrate the superiority of our model.

On computing clusters, the autoscaler is responsible for allocating resources for jobs or fine-grained tasks to ensure their Quality of Service. Due to a more precise resource management, fine-grained autoscaling can generally achieve better performance. However, the fine-grained autoscaling for streaming jobs needs intensive computation to model the complicated running states of tasks, and has not been adequately studied previously. In this paper, we propose a novel fine-grained autoscaler for streaming jobs based on reinforcement learning. We first organize the running states of streaming jobs as spatio-temporal graphs. To efficiently make autoscaling decisions, we propose a Neural Variational Subgraph Sampler to sample spatio-temporal subgraphs. Furthermore, we propose a mutual-information-based objective function to explicitly guide the sampler to extract more representative subgraphs. After that, the autoscaler makes decisions based on the learned subgraph representations. Experiments conducted on real-world datasets demonstrate the superiority of our method over six competitive baselines.

In industry, customer lifetime value (LTV) prediction is a challenging task, since user consumption data is usually volatile, noisy, or sparse. To address these issues, this paper presents a novel Temporal-Structural User Representation (named TSUR) network to predict LTV. We utilize historical revenue time series and user attributes to learn both temporal and structural user representations, respectively. Specifically, the temporal representation is learned with a temporal trend encoder based on a novel multi-channel Discrete Wavelet Transform~(DWT) module, while the structural representation is derived with Graph Attention Network (GAT) on an attribute similarity graph. Furthermore, a novel cluster-alignment regularization method is employed to align and enhance these two kinds of representations. In essence, such a fusion way can be considered as the association of temporal and structural representations in the low-pass representation space, which is also useful to prevent the data noise from being transferred across different views. To our knowledge, it is the first time that temporal and structural user representations are jointly learned for LTV prediction. Extensive offline experiments on two large-scale real-world datasets and online A/B tests have shown the superiority of our approach over a number of competitive baselines.

Online chatting is gaining popularity and plays an increasingly significant role in software development. When discussing functionalities, developers might reveal their desired features to other developers. Automated mining techniques towards retrieving feature requests from massive chat messages can benefit the requirements gathering process. But it is quite challenging to perform such techniques because detecting feature requests from dialogues requires a thorough understanding of the contextual information, and it is also extremely expensive on annotating feature-request dialogues for learning. To bridge that gap, we recast the traditional text classification task of mapping single dialog to its class into the task of determining whether two dialogues are similar or not by incorporating few-shot learning. We propose a novel approach, named FRMiner, which can detect feature-request dialogues from chat messages via deep Siamese network. We design a BiLSTM-based dialog model that can learn the contextual information of a dialog in both forward and reverse directions. Evaluation on the real-world projects shows that our approach achieves average precision, recall and F1-score of 88.52%, 88.50% and 88.51%, which confirms that our approach could effectively detect hidden feature requests from chat messages, thus can facilitate gathering comprehensive requirements from the crowd in an automated way.

In practice, it is very important to determine the size of a proposed software system yet to be built based on its requirements, i.e., early in the development life cycle. The most widely used approach for size estimation is Function Point Analysis (FPA). However, since FPA involves human judgment, the estimation results are some degree of subjective, and the process is labor and cost intensive. In this paper, we propose a novel approach to identify transaction functions from textual requirements automatically by leveraging a set of natural language processing techniques and machine learning models. We evaluate our approach on 1,864 requirements and 104,691 transaction functions taken from 36 financial projects from one banking industry. The results show that the contents of the suggested transaction functions by our approach are high in quality, with low perplexity value of 8.5 and high BLEU score of 34 on average. The types of suggested transaction functions can also be accurately classified, with overall accuracy of 0.99 on average. Our approach can provide reasonable suggestions that assist industrial practitioners to identify transaction functions faster and easier.