OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating the Impact of Lean and Green Practices on Operational Performance: A Real Data-Driven Simulation Case Study
Venue: arXiv cs.CY 2025
Year: 2025

Global market-driven forces and customer needs are continuously changing. In the past, profitability and efficiency were the primary objectives of most companies. However, in recent decades, sustainable performance has emerged as a new competitive advantage. Companies have been compelled to adopt a concept that combines these evolving global interests with traditional goals resulting in the innovation of the lean and green approach. In this study, a research methodology that includes system analysis and modeling procedures to apply the lean and green concept, combined with a new evaluation metric, the Overall Environmental Equipment Effectiveness (OEEE) was used to investigate the effects of adopting lean and green practices on overall performance. A simulation model and energy value stream mapping were implemented, and the OEEE value was calculated to assess the current performance in terms of quality, availability, productivity, and sustainability. The current state production lead time was 329.1 minutes per batch, and the OEEE value was 13.1%. This result indicates existing issues in performance and sustainability, suggesting that improvement efforts should focus on enhancing these two aspects to increase the overall OEEE value. Several improvement scenarios were proposed, including combining and rearranging the inspection workstations as the first scenario, and using UV lighting for drying purposes at the framing workstation as the second. After applying these improvements, both scenarios showed increased OEEE values and reduced lead times compared to the current state. In the first scenario, the lead time decreased to 158.23 minutes, and the OEEE increased to 35%. In the second scenario, the lead time was reduced to 292 minutes, with the OEEE increasing to 24%.
A Large-Scale Real-World Evaluation of LLM-Based Virtual Teaching Assistant
Venue: arXiv cs.CY 2025
Year: 2025

Virtual Teaching Assistants (VTAs) powered by Large Language Models (LLMs) have the potential to enhance student learning by providing instant feedback and facilitating multi-turn interactions. However, empirical studies on their effectiveness and acceptance in real-world classrooms are limited, leaving their practical impact uncertain. In this study, we develop an LLM-based VTA and deploy it in an introductory AI programming course with 477 graduate students. To assess how student perceptions of the VTA's performance evolve over time, we conduct three rounds of comprehensive surveys at different stages of the course. Additionally, we analyze 3,869 student--VTA interaction pairs to identify common question types and engagement patterns. We then compare these interactions with traditional student--human instructor interactions to evaluate the VTA's role in the learning process. Through a large-scale empirical study and interaction analysis, we assess the feasibility of deploying VTAs in real-world classrooms and identify key challenges for broader adoption. Finally, we release the source code of our VTA system, fostering future advancements in AI-driven education: \texttt{https://github.com/sean0042/VTA}.
Re-Evaluating Code LLM Benchmarks Under Semantic Mutation
Venue: arXiv cs.SE 2025
Year: 2025

In the era of large language models (LLMs), code benchmarks have become an important research area in software engineering and are widely used by practitioners. These benchmarks evaluate the performance of LLMs on specific code-related tasks, such as code understanding and generation. A critical step in constructing code benchmarks is the design of prompts. However, as existing code benchmarks typically rely on a single prompt template per task, they are prone to the issue of prompt sensitivity, where minor prompt variations could result in substantial performance variations, leading to unreliable evaluations of model capabilities. While previous studies have explored prompt sensitivity, their experimental designs and findings are limited to traditional natural language processing (NLP) tasks. In this paper, we present an empirical study to investigate prompt sensitivity in code benchmarks. We first propose a general framework that modifies prompt templates in a manner that preserves both their semantics and their structure as much as possible. Based on the framework, we conduct extensive experiments across eight code benchmark tasks on 10 representative open-source LLMs, with each task featuring 100 semantically similar prompt templates. We then analyze the evaluation results using various statistical metrics, focusing on both absolute and relative model performance. Our findings suggest that even slight prompt variations can lead to significant shifts in performance. Additionally, we observe that such variations can introduce inconsistencies in the performance rankings across different models. These insights highlight the need for considering prompt sensitivity when designing future code benchmarks, to ensure more reliable and accurate evaluation of LLM capabilities.
Adaptive Control Attention Network for Underwater Acoustic Localization and Domain Adaptation
Venue: arXiv cs.SD 2025
Year: 2025

Localizing acoustic sound sources in the ocean is a challenging task due to the complex and dynamic nature of the environment. Factors such as high background noise, irregular underwater geometries, and varying acoustic properties make accurate localization difficult. To address these obstacles, we propose a multi-branch network architecture designed to accurately predict the distance between a moving acoustic source and a receiver, tested on real-world underwater signal arrays. The network leverages Convolutional Neural Networks (CNNs) for robust spatial feature extraction and integrates Conformers with self-attention mechanism to effectively capture temporal dependencies. Log-mel spectrogram and generalized cross-correlation with phase transform (GCC-PHAT) features are employed as input representations. To further enhance the model performance, we introduce an Adaptive Gain Control (AGC) layer, that adaptively adjusts the amplitude of input features, ensuring consistent energy levels across varying ranges, signal strengths, and noise conditions. We assess the model's generalization capability by training it in one domain and testing it in a different domain, using only a limited amount of data from the test domain for fine-tuning. Our proposed method outperforms state-of-the-art (SOTA) approaches in similar settings, establishing new benchmarks for underwater sound localization.
AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions
Venue: arXiv cs.CV 2025
Year: 2025

Robust visual recognition in underwater environments remains a significant challenge due to complex distortions such as turbidity, low illumination, and occlusion, which severely degrade the performance of standard vision systems. This paper introduces AQUA20, a comprehensive benchmark dataset comprising 8,171 underwater images across 20 marine species reflecting real-world environmental challenges such as illumination, turbidity, occlusions, etc., providing a valuable resource for underwater visual understanding. Thirteen state-of-the-art deep learning models, including lightweight CNNs (SqueezeNet, MobileNetV2) and transformer-based architectures (ViT, ConvNeXt), were evaluated to benchmark their performance in classifying marine species under challenging conditions. Our experimental results show ConvNeXt achieving the best performance, with a Top-3 accuracy of 98.82% and a Top-1 accuracy of 90.69%, as well as the highest overall F1-score of 88.92% with moderately large parameter size. The results obtained from our other benchmark models also demonstrate trade-offs between complexity and performance. We also provide an extensive explainability analysis using GRAD-CAM and LIME for interpreting the strengths and pitfalls of the models. Our results reveal substantial room for improvement in underwater species recognition and demonstrate the value of AQUA20 as a foundation for future research in this domain. The dataset is publicly available at: https://huggingface.co/datasets/taufiktrf/AQUA20.
General-Purpose Robotic Navigation via LVLM-Orchestrated Perception, Reasoning, and Acting
Venue: arXiv cs.RO 2025
Year: 2025

Developing general-purpose navigation policies for unknown environments remains a core challenge in robotics. Most existing systems rely on task-specific neural networks and fixed information flows, limiting their generalizability. Large Vision-Language Models (LVLMs) offer a promising alternative by embedding human-like knowledge for reasoning and planning, but prior LVLM-robot integrations have largely depended on pre-mapped spaces, hard-coded representations, and rigid control logic. We introduce the Agentic Robotic Navigation Architecture (ARNA), a general-purpose framework that equips an LVLM-based agent with a library of perception, reasoning, and navigation tools drawn from modern robotic stacks. At runtime, the agent autonomously defines and executes task-specific workflows that iteratively query modules, reason over multimodal inputs, and select navigation actions. This agentic formulation enables robust navigation and reasoning in previously unmapped environments, offering a new perspective on robotic stack design. Evaluated in Habitat Lab on the HM-EQA benchmark, ARNA outperforms state-of-the-art EQA-specific approaches. Qualitative results on RxR and custom tasks further demonstrate its ability to generalize across a broad range of navigation challenges.
FedNAMs: Performing Interpretability Analysis in Federated Learning Context
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning continues to evolve but faces challenges in interpretability and explainability. To address these challenges, we introduce a novel approach that employs Neural Additive Models (NAMs) within a federated learning framework. This new Federated Neural Additive Models (FedNAMs) approach merges the advantages of NAMs, where individual networks concentrate on specific input features, with the decentralized approach of federated learning, ultimately producing interpretable analysis results. This integration enhances privacy by training on local data across multiple devices, thereby minimizing the risks associated with data centralization and improving model robustness and generalizability. FedNAMs maintain detailed, feature-specific learning, making them especially valuable in sectors such as finance and healthcare. They facilitate the training of client-specific models to integrate local updates, preserve privacy, and mitigate concerns related to centralization. Our studies on various text and image classification tasks, using datasets such as OpenFetch ML Wine, UCI Heart Disease, and Iris, show that FedNAMs deliver strong interpretability with minimal accuracy loss compared to traditional Federated Deep Neural Networks (DNNs). The research involves notable findings, including the identification of critical predictive features at both client and global levels. Volatile acidity, sulfates, and chlorides for wine quality. Chest pain type, maximum heart rate, and number of vessels for heart disease. Petal length and width for iris classification. This approach strengthens privacy and model efficiency and improves interpretability and robustness across diverse datasets. Finally, FedNAMs generate insights on causes of highly and low interpretable features.
From Unstructured Communication to Intelligent RAG: Multi-Agent Automation for Supply Chain Knowledge Bases
Venue: arXiv cs.AI 2025
Year: 2025

Supply chain operations generate vast amounts of operational data; however, critical knowledge such as system usage practices, troubleshooting workflows, and resolution techniques often remains buried within unstructured communications like support tickets, emails, and chat logs. While RAG systems aim to leverage such communications as a knowledge base, their effectiveness is limited by raw data challenges: support tickets are typically noisy, inconsistent, and incomplete, making direct retrieval suboptimal. Unlike existing RAG approaches that focus on runtime optimization, we introduce a novel offline-first methodology that transforms these communications into a structured knowledge base. Our key innovation is a LLMs-based multi-agent system orchestrating three specialized agents: Category Discovery for taxonomy creation, Categorization for ticket grouping, and Knowledge Synthesis for article generation. Applying our methodology to real-world support tickets with resolution notes and comments, our system creates a compact knowledge base - reducing total volume to just 3.4% of original ticket data while improving quality. Experiments demonstrate that our prebuilt knowledge base in RAG systems significantly outperforms traditional RAG implementations (48.74% vs. 38.60% helpful answers) and achieves a 77.4% reduction in unhelpful responses. By automating institutional knowledge capture that typically remains siloed in experts' heads, our solution translates to substantial operational efficiency: reducing support workload, accelerating resolution times, and creating self-improving systems that automatically resolve approximately 50% of future supply chain tickets. Our approach addresses a key gap in knowledge management by transforming transient communications into structured, reusable knowledge through intelligent offline processing rather than latency-inducing runtime architectures.
PreQRAG -- Classify and Rewrite for Enhanced RAG
Venue: arXiv cs.IR 2025
Year: 2025

This paper presents the submission of the UDInfo team to the SIGIR 2025 LiveRAG Challenge. We introduce PreQRAG, a Retrieval Augmented Generation (RAG) architecture designed to improve retrieval and generation quality through targeted question preprocessing. PreQRAG incorporates a pipeline that first classifies each input question as either single-document or multi-document type. For single-document questions, we employ question rewriting techniques to improve retrieval precision and generation relevance. For multi-document questions, we decompose complex queries into focused sub-questions that can be processed more effectively by downstream components. This classification and rewriting strategy improves the RAG performance. Experimental evaluation of the LiveRAG Challenge dataset demonstrates the effectiveness of our question-type-aware architecture, with PreQRAG achieving the preliminary second place in Session 2 of the LiveRAG challenge.
Semantic-Aware Parsing for Security Logs
Venue: arXiv cs.CR 2025
Year: 2025

Security logs are foundational to threat detection and post-incident investigation, yet analysts often struggle to fully leverage them due to their heterogeneity and unstructured nature. The standard practice of manually writing parsers to normalize the data in security event management systems is time-consuming and costly due to the long tail of log formats. Meanwhile, querying raw logs without explicit parsing using large language models (LLMs) is impractical at scale. In this paper, we introduce Matryoshka, an end-to-end system leveraging LLMs to automatically generate semantically-aware structured log parsers without labeled examples or human intervention. Matryoshka achieves this by directly inferring log syntax, variable naming, and normalization to common security-specific schemas (e.g., OCSF [1]) from unlabeled log line samples, then generating deterministic parsers and mapping rules that can be efficiently applied during data ingest. This approach provides analysts with semantically-rich data representations at scale, facilitating rapid and precise log search without the traditional burden of manual parser construction. We evaluate Matryoshka's capabilities through both established template generation datasets and new datasets curated to establish end-to-end performance on a realistic distribution of log types. Our experiments show that Matryoshka outperforms prior work on syntax parsing while matching human-generated parsers in both side-by-side comparisons and retrieval for security-relevant queries. These results demonstrate that Matryoshka significantly reduces manual effort by automatically extracting and organizing valuable security data, moving us closer to fully automated, AI-driven analytics.
Public Perceptions of Autonomous Vehicles: A Survey of Pedestrians and Cyclists in Pittsburgh
Venue: arXiv cs.CY 2025
Year: 2025

This study investigates how autonomous vehicle(AV) technology is perceived by pedestrians and bicyclists in Pittsburgh. Using survey data from over 1200 respondents, the research explores the interplay between demographics, AV interactions, infrastructural readiness, safety perceptions, and trust. Findings highlight demographic divides, infrastructure gaps, and the crucial role of communication and education in AV adoption.
On the Power of Spatial Locality on Online Routing Problems
Venue: arXiv cs.DS 2025
Year: 2025

We consider the online versions of two fundamental routing problems, traveling salesman (TSP) and dial-a-ride (DARP), which have a variety of relevant applications in logistics and robotics. The online versions of these problems concern with efficiently serving a sequence of requests presented in a real-time on-line fashion located at points of a metric space by servers (salesmen/vehicles/robots). In this paper, motivated from real-world applications, such as Uber/Lyft rides, where some limited knowledge is available on the future requests, we propose the {\em spatial locality} model that provides in advance the distance within which new request(s) will be released from the current position of server(s). We study the usefulness of this advanced information on achieving the improved competitive ratios for both the problems with $k\geq 1$ servers, compared to the competitive results established in the literature without such spatial locality consideration. We show that small locality is indeed useful in obtaining improved competitive ratios irrespective of the metric space.
A Survey of State Representation Learning for Deep Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Representation learning methods are an important tool for addressing the challenges posed by complex observations spaces in sequential decision making problems. Recently, many methods have used a wide variety of types of approaches for learning meaningful state representations in reinforcement learning, allowing better sample efficiency, generalization, and performance. This survey aims to provide a broad categorization of these methods within a model-free online setting, exploring how they tackle the learning of state representations differently. We categorize the methods into six main classes, detailing their mechanisms, benefits, and limitations. Through this taxonomy, our aim is to enhance the understanding of this field and provide a guide for new researchers. We also discuss techniques for assessing the quality of representations, and detail relevant future directions.
ConsumerBench: Benchmarking Generative AI Applications on End-User Devices
Venue: arXiv cs.DC 2025
Year: 2025

The recent shift in Generative AI (GenAI) applications from cloud-only environments to end-user devices introduces new challenges in resource management, system efficiency, and user experience. This paper presents ConsumerBench, a comprehensive benchmarking framework designed to evaluate the system efficiency and response time of GenAI models running on end-user devices. Unlike existing benchmarks that assume exclusive model access on dedicated GPUs, ConsumerBench simulates realistic multi-application scenarios executing concurrently on constrained hardware. Furthermore, ConsumerBench supports customizable workflows that simulate complex tasks requiring coordination among multiple applications. ConsumerBench captures both application-level metrics, including latency and Service Level Objective (SLO) attainment, and system-level metrics like CPU/GPU utilization and memory bandwidth. Through extensive experiments, ConsumerBench reveals inefficiencies in resource sharing, unfair scheduling under greedy allocation, and performance pitfalls of static model server configurations. The paper also provides practical insights for model developers and system designers, highlighting the benefits of custom kernels tailored to consumer-grade GPU architectures and the value of implementing SLO-aware scheduling strategies.
Breaking Single-Tester Limits: Multi-Agent LLMs for Multi-User Feature Testing
Venue: arXiv cs.SE 2025
Year: 2025

The growing dependence on mobile phones and their apps has made multi-user interactive features, like chat calls, live streaming, and video conferencing, indispensable for bridging the gaps in social connectivity caused by physical and situational barriers. However, automating these interactive features for testing is fraught with challenges, owing to their inherent need for timely, dynamic, and collaborative user interactions, which current automated testing methods inadequately address. Inspired by the concept of agents designed to autonomously and collaboratively tackle problems, we propose MAdroid, a novel multi-agent approach powered by the Large Language Models (LLMs) to automate the multi-user interactive task for app feature testing. Specifically, MAdroid employs two functional types of multi-agents: user agents (Operator) and supervisor agents (Coordinator and Observer). Each agent takes a specific role: the Coordinator directs the interactive task; the Operator mimics user interactions on the device; and the Observer monitors and reviews the task automation process. Our evaluation, which included 41 multi-user interactive tasks, demonstrates the effectiveness of our approach, achieving 82.9% of the tasks with 96.8% action similarity, outperforming the ablation studies and state-of-the-art baselines. Additionally, a preliminary investigation underscores MAdroid's practicality by helping identify 11 multi-user interactive bugs during regression app testing, confirming its potential value in real-world software development contexts.
Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems
Venue: arXiv cs.DC 2025
Year: 2025

With the rapid adoption of large language models (LLMs) in recommendation systems, the computational and communication bottlenecks caused by their massive parameter sizes and large data volumes have become increasingly prominent. This paper systematically investigates two classes of optimization methods-model parallelism and data parallelism-for distributed training of LLMs in recommendation scenarios. For model parallelism, we implement both tensor parallelism and pipeline parallelism, and introduce an adaptive load-balancing mechanism to reduce cross-device communication overhead. For data parallelism, we compare synchronous and asynchronous modes, combining gradient compression and sparsification techniques with an efficient aggregation communication framework to significantly improve bandwidth utilization. Experiments conducted on a real-world recommendation dataset in a simulated service environment demonstrate that our proposed hybrid parallelism scheme increases training throughput by over 30% and improves resource utilization by approximately 20% compared to traditional single-mode parallelism, while maintaining strong scalability and robustness. Finally, we discuss trade-offs among different parallel strategies in online deployment and outline future directions involving heterogeneous hardware integration and automated scheduling technologies.