OpenRaiser/Intern-Atlas huggingface dataset:
A Fast Solver-Free Algorithm for Traffic Engineering in Large-Scale Data Center Network
Venue: arXiv cs.NI 2025
Year: 2025

Rapid growth of data center networks (DCNs) poses significant challenges for large-scale traffic engineering (TE). Existing acceleration strategies, which rely on commercial solvers or deep learning, face scalability issues and struggle with degrading performance or long computational time. Unlike existing algorithms adopting parallel strategies, we propose Sequential Source-Destination Optimization (SSDO), a sequential solver-free algorithm for TE. SSDO decomposes the problem into subproblems, each focused on adjusting the split ratios for a specific source-destination (SD) demand while keeping others fixed. To enhance the efficiency of subproblem optimization, we design a Balanced Binary Search Method (BBSM), which identifies the most balanced split ratios among multiple solutions that minimize Maximum Link Utilization (MLU). SSDO dynamically updates the sequence of SDs based on real-time utilization, which accelerates convergence and enhances solution quality. We evaluate SSDO on Meta DCNs and two wide-area networks. In a Meta topology, SSDO achieves a 65\% and 60\% reduction in normalized MLU compared to TEAL and POP, two state-of-the-art TE acceleration methods, while delivering a $12\times$ speedup over POP. These results demonstrate the superior performance of SSDO in large-scale TE.
Memory-Statistics Tradeoff in Continual Learning with Structural Regularization
Venue: arXiv cs.LG 2025
Year: 2025

We study the statistical performance of a continual learning problem with two linear regression tasks in a well-specified random design setting. We consider a structural regularization algorithm that incorporates a generalized $\ell_2$-regularization tailored to the Hessian of the previous task for mitigating catastrophic forgetting. We establish upper and lower bounds on the joint excess risk for this algorithm. Our analysis reveals a fundamental trade-off between memory complexity and statistical efficiency, where memory complexity is measured by the number of vectors needed to define the structural regularization. Specifically, increasing the number of vectors in structural regularization leads to a worse memory complexity but an improved excess risk, and vice versa. Furthermore, our theory suggests that naive continual learning without regularization suffers from catastrophic forgetting, while structural regularization mitigates this issue. Notably, structural regularization achieves comparable performance to joint training with access to both tasks simultaneously. These results highlight the critical role of curvature-aware regularization for continual learning.
FISH-Tuning: Enhancing PEFT Methods with Fisher Information
Venue: arXiv cs.CL 2025
Year: 2025

The rapid growth in the parameter size of Large Language Models (LLMs) has spurred the development of Parameter-Efficient Fine-Tuning (PEFT) methods to mitigate the substantial computational costs of fine-tuning. Among these, Fisher Induced Sparse uncHanging (FISH) Mask is a selection-based PEFT technique that identifies a critical subset of pre-trained parameters using approximate Fisher information. While addition-based and reparameterization-based PEFT methods like LoRA and Adapter already fine-tune only a small number of parameters, the newly introduced parameters within these methods themselves present an opportunity for further optimization. Selectively fine-tuning only the most impactful among these new parameters could further reduce resource consumption while maintaining, or even improving, fine-tuning effectiveness. In this paper, we propose \textbf{FISH-Tuning}, a novel approach that incorporates FISH Mask into such PEFT methods, including LoRA, Adapter, and their variants. By leveraging Fisher information to identify and update only the most significant parameters within these added or reparameterized components, FISH-Tuning aims to achieve superior performance without increasing training time or inference latency compared to the vanilla PEFT methods. Experimental results across various datasets and pre-trained models demonstrate that FISH-Tuning consistently outperforms the vanilla PEFT methods when using the same proportion of trainable parameters. Code is available at https://anonymous.4open.science/r/FISH-Tuning-6F7C.
Learning-Based Multi-Criteria Decision Model for Site Selection Problems
Venue: arXiv cs.LG 2025
Year: 2025

Strategically locating sawmills is critical for the efficiency, profitability, and sustainability of timber supply chains, yet it involves a series of complex decision-making affected by various factors, such as proximity to resources and markets, proximity to roads and rail lines, distance from the urban area, slope, labor market, and existing sawmill data. Although conventional Multi-Criteria Decision-Making (MCDM) approaches utilize these factors while locating facilities, they are susceptible to bias since they rely heavily on expert opinions to determine the relative factor weights. Machine learning (ML) models provide an objective, data-driven alternative for site selection that derives these weights directly from the patterns in large datasets without requiring subjective weighting. Additionally, ML models autonomously identify critical features, eliminating the need for subjective feature selection. In this study, we propose integrated ML and MCDM methods and showcase the utility of this integrated model to improve sawmill location decisions via a case study in Mississippi. This integrated model is flexible and applicable to site selection problems across various industries.
Enforcement Agents: Enhancing Accountability and Resilience in Multi-Agent AI Frameworks
Venue: arXiv cs.MA 2025
Year: 2025

As autonomous agents become more powerful and widely used, it is becoming increasingly important to ensure they behave safely and stay aligned with system goals, especially in multi-agent settings. Current systems often rely on agents self-monitoring or correcting issues after the fact, but they lack mechanisms for real-time oversight. This paper introduces the Enforcement Agent (EA) Framework, which embeds dedicated supervisory agents into the environment to monitor others, detect misbehavior, and intervene through real-time correction. We implement this framework in a custom drone simulation and evaluate it across 90 episodes using 0, 1, and 2 EA configurations. Results show that adding EAs significantly improves system safety: success rates rise from 0.0% with no EA to 7.4% with one EA and 26.7% with two EAs. The system also demonstrates increased operational longevity and higher rates of malicious drone reformation. These findings highlight the potential of lightweight, real-time supervision for enhancing alignment and resilience in multi-agent systems.
Collaboration and Controversy Among Experts: Rumor Early Detection by Tuning a Comment Generator
Venue: arXiv cs.CL 2025
Year: 2025

Over the past decade, social media platforms have been key in spreading rumors, leading to significant negative impacts. To counter this, the community has developed various Rumor Detection (RD) algorithms to automatically identify them using user comments as evidence. However, these RD methods often fail in the early stages of rumor propagation when only limited user comments are available, leading the community to focus on a more challenging topic named Rumor Early Detection (RED). Typically, existing RED methods learn from limited semantics in early comments. However, our preliminary experiment reveals that the RED models always perform best when the number of training and test comments is consistent and extensive. This inspires us to address the RED issue by generating more human-like comments to support this hypothesis. To implement this idea, we tune a comment generator by simulating expert collaboration and controversy and propose a new RED framework named CAMERED. Specifically, we integrate a mixture-of-expert structure into a generative language model and present a novel routing network for expert collaboration. Additionally, we synthesize a knowledgeable dataset and design an adversarial learning strategy to align the style of generated comments with real-world comments. We further integrate generated and original comments with a mutual controversy fusion module. Experimental results show that CAMERED outperforms state-of-the-art RED baseline models and generation methods, demonstrating its effectiveness.
TARAC: Mitigating Hallucination in LVLMs via Temporal Attention Real-time Accumulative Connection
Venue: arXiv cs.CV 2025
Year: 2025

Large Vision-Language Models have demonstrated remarkable performance across various tasks; however, the challenge of hallucinations constrains their practical applications. The hallucination problem arises from multiple factors, including the inherent hallucinations in language models, the limitations of visual encoders in perception, and biases introduced by multimodal data. Extensive research has explored ways to mitigate hallucinations. For instance, OPERA prevents the model from overly focusing on "anchor tokens", thereby reducing hallucinations, whereas VCD mitigates hallucinations by employing a contrastive decoding approach. In this paper, we investigate the correlation between the decay of attention to image tokens and the occurrence of hallucinations. Based on this finding, we propose Temporal Attention Real-time Accumulative Connection (TARAC), a novel training-free method that dynamically accumulates and updates LVLMs' attention on image tokens during generation. By enhancing the model's attention to image tokens, TARAC mitigates hallucinations caused by the decay of attention on image tokens. We validate the effectiveness of TARAC across multiple models and datasets, demonstrating that our approach substantially mitigates hallucinations. In particular, TARAC reduces $C_S$ by 25.2 and $C_I$ by 8.7 compared to VCD on the CHAIR benchmark.
LATTE: Lightweight Attention-based Traffic Accident Anticipation Engine
Venue: arXiv cs.CE 2025
Year: 2025

Accurately predicting traffic accidents in real-time is a critical challenge in autonomous driving, particularly in resource-constrained environments. Existing solutions often suffer from high computational overhead or fail to adequately address the uncertainty of evolving traffic scenarios. This paper introduces LATTE, a Lightweight Attention-based Traffic Accident Anticipation Engine, which integrates computational efficiency with state-of-the-art performance. LATTE employs Efficient Multiscale Spatial Aggregation (EMSA) to capture spatial features across scales, Memory Attention Aggregation (MAA) to enhance temporal modeling, and Auxiliary Self-Attention Aggregation (AAA) to extract latent dependencies over extended sequences. Additionally, LATTE incorporates the Flamingo Alert-Assisted System (FAA), leveraging a vision-language model to provide real-time, cognitively accessible verbal hazard alerts, improving passenger situational awareness. Evaluations on benchmark datasets (DAD, CCD, A3D) demonstrate LATTE's superior predictive capabilities and computational efficiency. LATTE achieves state-of-the-art 89.74% Average Precision (AP) on DAD benchmark, with 5.4% higher mean Time-To-Accident (mTTA) than the second-best model, and maintains competitive mTTA at a Recall of 80% (TTA@R80) (4.04s) while demonstrating robust accident anticipation across diverse driving conditions. Its lightweight design delivers a 93.14% reduction in floating-point operations (FLOPs) and a 31.58% decrease in parameter count (Params), enabling real-time operation on resource-limited hardware without compromising performance. Ablation studies confirm the effectiveness of LATTE's architectural components, while visualizations and failure case analyses highlight its practical applicability and areas for enhancement.
The Probability Spaces of QuickSort
Venue: arXiv cs.CC 2025
Year: 2025

QuickSort and the analysis of its expected run time was presented 1962 in a classical paper by C.A.R Hoare. There the run time analysis hinges on a by now well known recurrence equation for the expected run time, which in turn was justified by referring to ``the law of conditional expectations''. A probability space for the runs of the algorithms was not constructed. Subsequent textbooks treated the recurrence relation as self evident and present it until this day without proof. Here we give an inductive definition of the probability space for the runs of randomized QuickSort and subsequently derive the recurrence equation with a not completely trivial proof.
Iterative Random Weight EC-TOPSIS Method for Ranking Companies on Social Media
Venue: arXiv cs.CE 2025
Year: 2025

This study aims to present a new hybrid method for weighting criteria. The methodological project combines the ENTROPY and CRITIC methods with the TOPSIS method to create EC-TOPSIS. The difference lies in the use of a weight range per criterion. Each weight range has a lower limit and an upper limit, which are combined to generate random numbers, producing t sets of weights per criterion, allowing t final rankings to be obtained. The final ranking is obtained by applying the MODE statistical measure to the set of t positions of each alternative. The method was validated by ranking the companies based on social media metrics consisting of user-generated content (UGC). The result was compared with the original modeling using the CRITIC-ARAS and CRITIC-COPRAS methods, and the results were consistent and balanced, with few changes. The practical implication of the method is in reducing the uncertainties surrounding the final classification due to the random weighting process and the number of interactions sent.
The Ripple Effect of Vulnerabilities in Maven Central: Prevalence, Propagation, and Mitigation Challenges
Venue: arXiv cs.SE 2025
Year: 2025

The widespread use of package managers like Maven has accelerated software development but has also introduced significant security risks due to vulnerabilities in dependencies. In this study, we analyze the prevalence and impact of vulnerabilities within the Maven Central ecosystem, using Common Vulnerabilities and Exposures (CVE) data from OSV.dev and a subsample enriched with aggregated CVE data (CVE_AGGREGATED), which captures both direct and transitive vulnerabilities. In our subsample of around 4 million releases, we found that while only about 1% of releases have direct vulnerabilities, approximately 46.8% are affected by transitive vulnerabilities. This highlights how a small number of vulnerable yet influential artifacts can impact a vast portion of the ecosystem. Moreover, our analysis shows that vulnerabilities propagate rapidly through dependency networks and that more central artifacts (those with a high number of dependents) are not necessarily less vulnerable. We also observed that the time taken to patch vulnerabilities, including those of high or critical severity, often spans several years. Additionally, we found that dependents of artifacts tend to prefer presumably non-vulnerable versions; however, some continue to use vulnerable versions, indicating challenges in adopting patched releases. These findings highlight the critical need for improved dependency management practices and timely vulnerability remediation to enhance the security of software ecosystems.
AutoComp: Automated Data Compaction for Log-Structured Tables in Data Lakes
Venue: arXiv cs.DB 2025
Year: 2025

The proliferation of small files in data lakes poses significant challenges, including degraded query performance, increased storage costs, and scalability bottlenecks in distributed storage systems. Log-structured table formats (LSTs) such as Delta Lake, Apache Iceberg, and Apache Hudi exacerbate this issue due to their append-only write patterns and metadata-intensive operations. While compaction--the process of consolidating small files into fewer, larger files--is a common solution, existing automation mechanisms often lack the flexibility and scalability to adapt to diverse workloads and system requirements while balancing the trade-offs between compaction benefits and costs. In this paper, we present AutoComp, a scalable framework for automatic data compaction tailored to the needs of modern data lakes. Drawing on deployment experience at LinkedIn, we analyze the operational impact of small file proliferation, establish key requirements for effective automatic compaction, and demonstrate how AutoComp addresses these challenges. Our evaluation, conducted using synthetic benchmarks and production environments via integration with OpenHouse--a control plane for catalog management, schema governance, and data services--shows significant improvements in file count reduction and query performance. We believe AutoComp's built-in extensibility provides a robust foundation for evolving compaction systems, facilitating future integration of refined multi-objective optimization approaches, workload-aware compaction strategies, and expanded support for broader data layout optimizations.
Exploration of Approaches for Robustness and Safety in a Low Code Open Environment for Factory Automation
Venue: arXiv cs.SE 2025
Year: 2025

This report is a compilation of technical knowledge and concepts that were produced by the authors and additional contributors in the context of the collaboration projects "Abstraction Requirements for Language of Choice in Industrial Automation" (FY21-22) and "Approaches for Robust and Safe Low-Code" (FY23-24) from Siemens Technology and the University of California, Berkeley. The primary objective of these projects was to assess Siemens Open Industrial Edge (OIE) engineering capabilities by defining a concept that ensures the satisfaction of coordination and safety requirements when using disparate OIE modules. The objective was to use the Lingua Franca (LF) coordination language to demonstrate how to address challenges in: 1. engineering modular, distributed, and flexible automation solutions that ensure, by design, robust and safe operation1; 2. the use of IEC 61499, the event driven execution model for specifying the execution order of OIE modules (defined as function blocks); 3. support large-scale distributed OIE automation solutions, and eventually 4. define optimal solutions with synchronization and time-optimal mechanisms.
Improving Chronic Kidney Disease Detection Efficiency: Fine Tuned CatBoost and Nature-Inspired Algorithms with Explainable AI
Venue: arXiv cs.AI 2025
Year: 2025

Chronic Kidney Disease (CKD) is a major global health issue which is affecting million people around the world and with increasing rate of mortality. Mitigation of progression of CKD and better patient outcomes requires early detection. Nevertheless, limitations lie in traditional diagnostic methods, especially in resource constrained settings. This study proposes an advanced machine learning approach to enhance CKD detection by evaluating four models: Random Forest (RF), Multi-Layer Perceptron (MLP), Logistic Regression (LR), and a fine-tuned CatBoost algorithm. Specifically, among these, the fine-tuned CatBoost model demonstrated the best overall performance having an accuracy of 98.75%, an AUC of 0.9993 and a Kappa score of 97.35% of the studies. The proposed CatBoost model has used a nature inspired algorithm such as Simulated Annealing to select the most important features, Cuckoo Search to adjust outliers and grid search to fine tune its settings in such a way to achieve improved prediction accuracy. Features significance is explained by SHAP-a well-known XAI technique-for gaining transparency in the decision-making process of proposed model and bring up trust in diagnostic systems. Using SHAP, the significant clinical features were identified as specific gravity, serum creatinine, albumin, hemoglobin, and diabetes mellitus. The potential of advanced machine learning techniques in CKD detection is shown in this research, particularly for low income and middle-income healthcare settings where prompt and correct diagnoses are vital. This study seeks to provide a highly accurate, interpretable, and efficient diagnostic tool to add to efforts for early intervention and improved healthcare outcomes for all CKD patients.
Beyond the Hype: Embeddings vs. Prompting for Multiclass Classification Tasks
Venue: arXiv cs.LG 2025
Year: 2025

Are traditional classification approaches irrelevant in this era of AI hype? We show that there are multiclass classification problems where predictive models holistically outperform LLM prompt-based frameworks. Given text and images from home-service project descriptions provided by Thumbtack customers, we build embeddings-based softmax models that predict the professional category (e.g., handyman, bathroom remodeling) associated with each problem description. We then compare against prompts that ask state-of-the-art LLM models to solve the same problem. We find that the embeddings approach outperforms the best LLM prompts in terms of accuracy, calibration, latency, and financial cost. In particular, the embeddings approach has 49.5\% higher accuracy than the prompting approach, and its superiority is consistent across text-only, image-only, and text-image problem descriptions. Furthermore, it yields well-calibrated probabilities, which we later use as confidence signals to provide contextualized user experience during deployment. On the contrary, prompting scores are overly uninformative. Finally, the embeddings approach is 14 and 81 times faster than prompting in processing images and text respectively, while under realistic deployment assumptions, it can be up to 10 times cheaper. Based on these results, we deployed a variation of the embeddings approach, and through A/B testing we observed performance consistent with our offline analysis. Our study shows that for multiclass classification problems that can leverage proprietary datasets, an embeddings-based approach may yield unequivocally better results. Hence, scientists, practitioners, engineers, and business leaders can use our study to go beyond the hype and consider appropriate predictive models for their classification use cases.
Balancing Complexity and Informativeness in LLM-Based Clustering: Finding the Goldilocks Zone
Venue: arXiv cs.CL 2025
Year: 2025

The challenge of clustering short text data lies in balancing informativeness with interpretability. Traditional evaluation metrics often overlook this trade-off. Inspired by linguistic principles of communicative efficiency, this paper investigates the optimal number of clusters by quantifying the trade-off between informativeness and cognitive simplicity. We use large language models (LLMs) to generate cluster names and evaluate their effectiveness through semantic density, information theory, and clustering accuracy. Our results show that Gaussian Mixture Model (GMM) clustering on embeddings generated by a LLM, increases semantic density compared to random assignment, effectively grouping similar bios. However, as clusters increase, interpretability declines, as measured by a generative LLM's ability to correctly assign bios based on cluster names. A logistic regression analysis confirms that classification accuracy depends on the semantic similarity between bios and their assigned cluster names, as well as their distinction from alternatives. These findings reveal a "Goldilocks zone" where clusters remain distinct yet interpretable. We identify an optimal range of 16-22 clusters, paralleling linguistic efficiency in lexical categorization. These insights inform both theoretical models and practical applications, guiding future research toward optimising cluster interpretability and usefulness.