OpenRaiser/Intern-Atlas huggingface dataset:
REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks
Venue: arXiv cs.NI 2025
Year: 2025

With the rise of Software-Defined Networking (SDN) for managing traffic and ensuring seamless operations across interconnected devices, challenges arise when SDN controllers share infrastructure with deep learning (DL) workloads. Resource contention between DL training and SDN operations, especially in latency-sensitive IoT environments, can degrade SDN's responsiveness and compromise network performance. Federated Learning (FL) helps address some of these concerns by decentralizing DL training to edge devices, thus reducing data transmission costs and enhancing privacy. Yet, the computational demands of DL training can still interfere with SDN's performance, especially under the continuous data streams characteristic of IoT systems. To mitigate this issue, we propose REDUS (Resampling for Efficient Data Utilization in Smart-Networks), a resampling technique that optimizes DL training by prioritizing misclassified samples and excluding redundant data, inspired by AdaBoost. REDUS reduces the number of training samples per epoch, thereby conserving computational resources, reducing energy consumption, and accelerating convergence without significantly impacting accuracy. Applied within an FL setup, REDUS enhances the efficiency of model training on resource-limited edge devices while maintaining network performance. In this paper, REDUS is evaluated on the CICIoT2023 dataset for IoT attack detection, showing a training time reduction of up to 72.6% with a minimal accuracy loss of only 1.62%, offering a scalable and practical solution for intelligent networks.
RoboBrain 2.0 Technical Report
Venue: arXiv cs.RO 2025
Year: 2025

We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in physical environments. It comes in two variants: a lightweight 7B model and a full-scale 32B model, featuring a heterogeneous architecture with a vision encoder and a language model. Despite its compact size, RoboBrain 2.0 achieves strong performance across a wide spectrum of embodied reasoning tasks. On both spatial and temporal benchmarks, the 32B variant achieves leading results, surpassing prior open-source and proprietary models. In particular, it supports key real-world embodied AI capabilities, including spatial understanding (e.g., affordance prediction, spatial referring, trajectory forecasting) and temporal decision-making (e.g., closed-loop interaction, multi-agent long-horizon planning, and scene graph updating). This report details the model architecture, data construction, multi-stage training strategies, infrastructure and practical applications. We hope RoboBrain 2.0 advances embodied AI research and serves as a practical step toward building generalist embodied agents. The code, checkpoint and benchmark are available at https://superrobobrain.github.io.
Advanced Printed Sensors for Environmental Applications: A Path Towards Sustainable Monitoring Solutions
Venue: arXiv cs.AR 2025
Year: 2025

Printed sensors represent a transformative advancement in sensor technology, utilizing innovative printing techniques to create flexible, cost-effective, and highly customizable sensing devices. Their versatility allows integration into numerous applications across diverse fields such as monitoring a wide range of environmental factors e.g. air and water quality, soil conditions, and atmospheric changes among others. These sensors demonstrate high sensitivity and accuracy in detecting pollutants, temperature variations, humidity levels, and other critical parameters essential for environmental assessment and protection.
How do Software Engineering Candidates Prepare for Technical Interviews?
Venue: arXiv cs.SE 2025
Year: 2025

To obtain employment, aspiring software engineers must complete technical interviews -- a hiring process which involves candidates writing code while communicating to an audience. However, the complexities of tech interviews are difficult to prepare for and seldom faced in computing curricula. To this end, we seek to understand how candidates prepare for technical interviews, investigating the effects of preparation methods and the role of education. We distributed a survey to candidates (n = 131) actively preparing for technical interviews. Our results suggest candidates rarely train in authentic settings and courses fail to support preparation efforts -- leading to stress and unpreparedness. Based on our findings, we provide implications for stakeholders to enhance tech interview preparation for candidates pursuing software engineering roles.
TAGF: Time-aware Gated Fusion for Multimodal Valence-Arousal Estimation
Venue: arXiv cs.MM 2025
Year: 2025

Multimodal emotion recognition often suffers from performance degradation in valence-arousal estimation due to noise and misalignment between audio and visual modalities. To address this challenge, we introduce TAGF, a Time-aware Gated Fusion framework for multimodal emotion recognition. The TAGF adaptively modulates the contribution of recursive attention outputs based on temporal dynamics. Specifically, the TAGF incorporates a BiLSTM-based temporal gating mechanism to learn the relative importance of each recursive step and effectively integrates multistep cross-modal features. By embedding temporal awareness into the recursive fusion process, the TAGF effectively captures the sequential evolution of emotional expressions and the complex interplay between modalities. Experimental results on the Aff-Wild2 dataset demonstrate that TAGF achieves competitive performance compared with existing recursive attention-based models. Furthermore, TAGF exhibits strong robustness to cross-modal misalignment and reliably models dynamic emotional transitions in real-world conditions.
McBE: A Multi-task Chinese Bias Evaluation Benchmark for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

As large language models (LLMs) are increasingly applied to various NLP tasks, their inherent biases are gradually disclosed. Therefore, measuring biases in LLMs is crucial to mitigate its ethical risks. However, most existing bias evaluation datasets focus on English and North American culture, and their bias categories are not fully applicable to other cultures. The datasets grounded in the Chinese language and culture are scarce. More importantly, these datasets usually only support single evaluation tasks and cannot evaluate the bias from multiple aspects in LLMs. To address these issues, we present a Multi-task Chinese Bias Evaluation Benchmark (McBE) that includes 4,077 bias evaluation instances, covering 12 single bias categories, 82 subcategories and introducing 5 evaluation tasks, providing extensive category coverage, content diversity, and measuring comprehensiveness. Additionally, we evaluate several popular LLMs from different series and with parameter sizes. In general, all these LLMs demonstrated varying degrees of bias. We conduct an in-depth analysis of results, offering novel insights into bias in LLMs.
Moderately Mighty: To What Extent Can Internal Software Metrics Predict App Popularity at Launch?
Venue: arXiv cs.SE 2025
Year: 2025

Predicting a mobile app's popularity before its first release can provide developers with a strategic advantage in a competitive marketplace, yet it remains a challenging problem. This study explores the extent to which internal software metrics, measurable from source code before deployment, can predict an app's popularity (i.e., ratings and downloads per year) at inception. For our analysis, we constructed a rigorously filtered dataset of 446 open-source Java-based Android apps that are available on both F-Droid and Google Play Store. Using app source code from F-Droid, we extracted a wide array of internal metrics, including system-, class-, and method-level code metrics, code smells, and app metadata. Popularity-related information, including reviews and download counts, was collected from the Play Store. We evaluate regression and classification models across three feature sets: a minimal Size-only baseline, a domain-informed Handpicked set, and a Voting set derived via feature selection algorithms. Our results show that, for both app ratings and number of downloads, regression models perform poorly due to skewed rating distributions and a highly scattered range of download counts in our dataset. However, when reframed as a binary classification (Popular vs. Unpopular), performance improves significantly-the best model, a Multilayer Perceptron, achieves an F1-score of 0.72. We conclude that, although internal code metrics alone are insufficient for accurately predicting an app's future popularity, they do exhibit meaningful correlations with it. Thus, our findings challenge prior studies that have entirely dismissed internal metrics as valid indicators of software quality. Instead, our results align with research suggesting that internal code metrics can be valuable when evaluated within the appropriate context-specifically, we found them useful for classification tasks.
CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Modern very large-scale integration (VLSI) design requires the implementation of integrated circuits using electronic design automation (EDA) tools. Due to the complexity of EDA algorithms, the vast parameter space poses a huge challenge to chip design optimization, as the combination of even moderate numbers of parameters creates an enormous solution space to explore. Manual parameter selection remains industrial practice despite being excessively laborious and limited by expert experience. To address this issue, we present CROP, the first large language model (LLM)-powered automatic VLSI design flow tuning framework. Our approach includes: (1) a scalable methodology for transforming RTL source code into dense vector representations, (2) an embedding-based retrieval system for matching designs with semantically similar circuits, and (3) a retrieval-augmented generation (RAG)-enhanced LLM-guided parameter search system that constrains the search process with prior knowledge from similar designs. Experiment results demonstrate CROP's ability to achieve superior quality-of-results (QoR) with fewer iterations than existing approaches on industrial designs, including a 9.9% reduction in power consumption.
Reasoning or Not? A Comprehensive Evaluation of Reasoning LLMs for Dialogue Summarization
Venue: arXiv cs.CL 2025
Year: 2025

Dialogue summarization is a challenging task with significant practical value in customer service, meeting analysis, and conversational AI. Although large language models (LLMs) have achieved substantial progress in summarization tasks, the performance of step-by-step reasoning architectures-specifically Long Chain-of-Thought (CoT) implementations such as OpenAI-o1 and DeepSeek-R1-remains unexplored for dialogue scenarios requiring concurrent abstraction and conciseness. In this work, we present the first comprehensive and systematic evaluation of state-of-the-art reasoning LLMs and non-reasoning LLMs across three major paradigms-generic, role-oriented, and query-oriented dialogue summarization. Our study spans diverse languages, domains, and summary lengths, leveraging strong benchmarks (SAMSum, DialogSum, CSDS, and QMSum) and advanced evaluation protocols that include both LLM-based automatic metrics and human-inspired criteria. Contrary to trends in other reasoning-intensive tasks, our findings show that explicit stepwise reasoning does not consistently improve dialogue summarization quality. Instead, reasoning LLMs are often prone to verbosity, factual inconsistencies, and less concise summaries compared to their non-reasoning counterparts. Through scenario-specific analyses and detailed case studies, we further identify when and why explicit reasoning may fail to benefit-or even hinder-summarization in complex dialogue contexts. Our work provides new insights into the limitations of current reasoning LLMs and highlights the need for targeted modeling and evaluation strategies for real-world dialogue summarization.
Signalling Health for Improved Kubernetes Microservice Availability
Venue: arXiv cs.DC 2025
Year: 2025

Microservices are often deployed and managed by a container orchestrator that can detect and fix failures to maintain the service availability critical in many applications. In Poll-based Container Monitoring (PCM), the orchestrator periodically checks container health. While a common approach, PCM requires careful tuning, may degrade service availability, and can be slow to detect container health changes. An alternative is Signal-based Container Monitoring (SCM), where the container signals the orchestrator when its status changes. We present the design, implementation, and evaluation of an SCM approach for Kubernetes and empirically show that it has benefits over PCM, as predicted by a new mathematical model. We compare the service availability of SCM and PCM over six experiments using the SockShop benchmark. SCM does not require that polling intervals are tuned, and yet detects container failure 86\% faster than PCM and container readiness in a comparable time with limited resource overheads. We find PCM can erroneously detect failures, and this reduces service availability by 4\%. We propose that orchestrators offer SCM features for faster failure detection than PCM without erroneous detections or careful tuning.
Statistical Inference for Responsiveness Verification
Venue: arXiv cs.LG 2025
Year: 2025

Many safety failures in machine learning arise when models are used to assign predictions to people (often in settings like lending, hiring, or content moderation) without accounting for how individuals can change their inputs. In this work, we introduce a formal validation procedure for the responsiveness of predictions with respect to interventions on their features. Our procedure frames responsiveness as a type of sensitivity analysis in which practitioners control a set of changes by specifying constraints over interventions and distributions over downstream effects. We describe how to estimate responsiveness for the predictions of any model and any dataset using only black-box access, and how to use these estimates to support tasks such as falsification and failure probability estimation. We develop algorithms that construct these estimates by generating a uniform sample of reachable points, and demonstrate how they can promote safety in real-world applications such as recidivism prediction, organ transplant prioritization, and content moderation.
Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN
Venue: arXiv cs.RO 2025
Year: 2025

Trajectory planning in robotics is understood as generating a sequence of joint configurations that will lead a robotic agent, or its manipulator, from an initial state to the desired final state, thus completing a manipulation task while considering constraints like robot kinematics and the environment. Typically, this is achieved via sampling-based planners, which are computationally intensive. Recent advances demonstrate that trajectory planning can also be performed by supervised sequence learning of trajectories, often requiring only a single or fixed number of passes through a neural architecture, thus ensuring a bounded computation time. Such fully supervised approaches, however, perform imitation learning; they do not learn based on whether the trajectories can successfully reach a goal, but try to reproduce observed trajectories. In our work, we build on this approach and propose a cognitively inspired self-supervised learning scheme based on a recurrent architecture for building a trajectory model. We evaluate the feasibility of the proposed method on a task of kinematic planning for a robotic arm. The results suggest that the model is able to learn to generate trajectories only using given paired forward and inverse kinematics models, and indicate that this novel method could facilitate planning for more complex manipulation tasks requiring adaptive solutions.
Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

Common Business Oriented Language (COBOL) is a programming language used to develop business applications that are widely adopted by financial, business, and government agencies. Due to its age, complexity, and declining number of COBOL developers, maintaining COBOL codebases is becoming increasingly challenging. In particular, the lack of documentation makes it difficult for new developers to effectively understand and maintain COBOL systems. Existing research utilizes large language models (LLMs) to explain the functionality of code snippets. However, COBOL presents unique challenges due to its architectural and syntactical differences, which often cause its code to exceed the token window size of LLMs. In this work, we propose a multi-agent approach that leverages two LLM-based agents working collaboratively to generate explanations for functions, files, and the overall project. These agents incorporate together by utilizing contextual information from the codebase into the code explanation prompts. We evaluate the effectiveness of our approach using 14 open-source, real-world COBOL projects. Our results indicate that our approach performs significantly better than the baseline in function code explanation, with improvements of 12.67%, 18.59%, and 0.62% in terms of METEOR, chrF, and SentenceBERT scores, respectively. At the file level, our approach effectively explains both short and long COBOL files that exceed the token window size of LLMs and surpass the baseline by 4.21%, 10.72%, and 14.68% in explaining the purpose, functionality, and clarity of the generated explanation. At the project level, our approach generates explanations that convey the functionality and purpose of 82% of the selected projects.
EvalAssist: A Human-Centered Tool for LLM-as-a-Judge
Venue: arXiv cs.HC 2025
Year: 2025

With the broad availability of large language models and their ability to generate vast outputs using varied prompts and configurations, determining the best output for a given task requires an intensive evaluation process, one where machine learning practitioners must decide how to assess the outputs and then carefully carry out the evaluation. This process is both time-consuming and costly. As practitioners work with an increasing number of models, they must now evaluate outputs to determine which model and prompt performs best for a given task. LLMs are increasingly used as evaluators to filter training data, evaluate model performance, assess harms and risks, or assist human evaluators with detailed assessments. We present EvalAssist, a framework that simplifies the LLM-as-a-judge workflow. The system provides an online criteria development environment, where users can interactively build, test, and share custom evaluation criteria in a structured and portable format. We support a set of LLM-based evaluation pipelines that leverage off-the-shelf LLMs and use a prompt-chaining approach we developed and contributed to the UNITXT open-source library. Additionally, our system also includes specially trained evaluators to detect harms and risks in LLM outputs. We have deployed the system internally in our organization with several hundreds of users.
cVLA: Towards Efficient Camera-Space VLAs
Venue: arXiv cs.RO 2025
Year: 2025

Vision-Language-Action (VLA) models offer a compelling framework for tackling complex robotic manipulation tasks, but they are often expensive to train. In this paper, we propose a novel VLA approach that leverages the competitive performance of Vision Language Models (VLMs) on 2D images to directly infer robot end-effector poses in image frame coordinates. Unlike prior VLA models that output low-level controls, our model predicts trajectory waypoints, making it both more efficient to train and robot embodiment agnostic. Despite its lightweight design, our next-token prediction architecture effectively learns meaningful and executable robot trajectories. We further explore the underutilized potential of incorporating depth images, inference-time techniques such as decoding strategies, and demonstration-conditioned action generation. Our model is trained on a simulated dataset and exhibits strong sim-to-real transfer capabilities. We evaluate our approach using a combination of simulated and real data, demonstrating its effectiveness on a real robotic system.
GPS-DRIFT: Marine Surface Robot Localization using IMU-GPS Fusion and Invariant Filtering
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents an extension of the DRIFT invariant state estimation framework, enabling robust fusion of GPS and IMU data for accurate pose and heading estimation. Originally developed for testing and usage on a marine autonomous surface vehicle (ASV), this approach can also be utilized on other mobile systems. Building upon the original proprioceptive only DRIFT algorithm, we develop a symmetry-preserving sensor fusion pipeline utilizing the invariant extended Kalman filter (InEKF) to integrate global position updates from GPS directly into the correction step. Crucially, we introduce a novel heading correction mechanism that leverages GPS course-over-ground information in conjunction with IMU orientation, overcoming the inherent unobservability of yaw in dead-reckoning. The system was deployed and validated on a customized Blue Robotics BlueBoat, but the methodological focus is on the algorithmic approach to fusing exteroceptive and proprioceptive sensors for drift-free localization and reliable orientation estimation. This work provides an open source solution for accurate yaw observation and localization in challenging or GPS-degraded conditions, and lays the groundwork for future experimental and comparative studies.
GDC Cohort Copilot: An AI Copilot for Curating Cohorts from the Genomic Data Commons
Venue: arXiv cs.CL 2025
Year: 2025

The Genomic Data Commons (GDC) provides access to high quality, harmonized cancer genomics data through a unified curation and analysis platform centered around patient cohorts. While GDC users can interactively create complex cohorts through the graphical Cohort Builder, users (especially new ones) may struggle to find specific cohort descriptors across hundreds of possible fields and properties. However, users may be better able to describe their desired cohort in free-text natural language. We introduce GDC Cohort Copilot, an open-source copilot tool for curating cohorts from the GDC. GDC Cohort Copilot automatically generates the GDC cohort filter corresponding to a user-input natural language description of their desired cohort, before exporting the cohort back to the GDC for further analysis. An interactive user interface allows users to further refine the generated cohort. We develop and evaluate multiple large language models (LLMs) for GDC Cohort Copilot and demonstrate that our locally-served, open-source GDC Cohort LLM achieves better results than GPT-4o prompting in generating GDC cohorts. We implement and share GDC Cohort Copilot as a containerized Gradio app on HuggingFace Spaces, available at https://huggingface.co/spaces/uc-ctds/GDC-Cohort-Copilot. GDC Cohort LLM weights are available at https://huggingface.co/uc-ctds. All source code is available at https://github.com/uc-cdis/gdc-cohort-copilot.