OpenRaiser/Intern-Atlas huggingface dataset:
Constraint Programming Models For Serial Batch Scheduling With Minimum Batch Size
Venue: arXiv cs.DC 2025
Year: 2025

In serial batch (s-batch) scheduling, jobs are grouped in batches and processed sequentially within their batch. This paper considers multiple parallel machines, nonidentical job weights and release times, and sequence-dependent setup times between batches of different families. Although s-batch has been widely studied in the literature, very few papers have taken into account a minimum batch size, typical in practical settings such as semiconductor manufacturing and the metal industry. The problem with this minimum batch size requirement has been mostly tackled with dynamic programming and meta-heuristics, and no article has ever used constraint programming (CP) to do so. This paper fills this gap by proposing, three CP models for s-batching with minimum batch size: (i) an \textit{Interval Assignment} model that computes and bounds the size of the batches using the presence literals of interval variables of the jobs. (ii) A \textit{Global} model that exclusively uses global constraints that track the size of the batches over time. (iii) And a \textit{Hybrid} model that combines the benefits of the extra global constraints with the efficiency of the sum-of-presences constraints to ensure the minimum batch sizes. The computational experiments on standard cases compare the three CP models with two existing mixed-integer programming (MIP) models from the literature. The results demonstrate the versatility of the proposed CP models to handle multiple variations of s-batching; and their ability to produce, in large instances, better solutions than the MIP models faster.
SafeMLRM: Demystifying Safety in Multi-modal Large Reasoning Models
Venue: arXiv cs.LG 2025
Year: 2025

The rapid advancement of multi-modal large reasoning models (MLRMs) -- enhanced versions of multimodal language models (MLLMs) equipped with reasoning capabilities -- has revolutionized diverse applications. However, their safety implications remain underexplored. While prior work has exposed critical vulnerabilities in unimodal reasoning models, MLRMs introduce distinct risks from cross-modal reasoning pathways. This work presents the first systematic safety analysis of MLRMs through large-scale empirical studies comparing MLRMs with their base MLLMs. Our experiments reveal three critical findings: (1) The Reasoning Tax: Acquiring reasoning capabilities catastrophically degrades inherited safety alignment. MLRMs exhibit 37.44% higher jailbreaking success rates than base MLLMs under adversarial attacks. (2) Safety Blind Spots: While safety degradation is pervasive, certain scenarios (e.g., Illegal Activity) suffer 25 times higher attack rates -- far exceeding the average 3.4 times increase, revealing scenario-specific vulnerabilities with alarming cross-model and datasets consistency. (3) Emergent Self-Correction: Despite tight reasoning-answer safety coupling, MLRMs demonstrate nascent self-correction -- 16.9% of jailbroken reasoning steps are overridden by safe answers, hinting at intrinsic safeguards. These findings underscore the urgency of scenario-aware safety auditing and mechanisms to amplify MLRMs' self-correction potential. To catalyze research, we open-source OpenSafeMLRM, the first toolkit for MLRM safety evaluation, providing unified interface for mainstream models, datasets, and jailbreaking methods. Our work calls for immediate efforts to harden reasoning-augmented AI, ensuring its transformative potential aligns with ethical safeguards.
Exploring utilization of generative AI for research and education in data-driven materials science
Venue: arXiv cs.CY 2025
Year: 2025

Generative AI has recently had a profound impact on various fields, including daily life, research, and education. To explore its efficient utilization in data-driven materials science, we organized a hackathon -- AIMHack2024 -- in July 2024. In this hackathon, researchers from fields such as materials science, information science, bioinformatics, and condensed matter physics worked together to explore how generative AI can facilitate research and education. Based on the results of the hackathon, this paper presents topics related to (1) conducting AI-assisted software trials, (2) building AI tutors for software, and (3) developing GUI applications for software. While generative AI continues to evolve rapidly, this paper provides an early record of its application in data-driven materials science and highlights strategies for integrating AI into research and education.
Soap Film-inspired Subdivisional Lattice Structure Construction
Venue: arXiv cs.CG 2025
Year: 2025

Lattice structures, distinguished by their customizable geometries at the microscale and outstanding mechanical performance, have found widespread application across various industries. One fundamental process in their design and manufacturing is constructing boundary representation (B-rep) models, which are essential for running advanced applications like simulation, optimization, and process planning. However, this construction process presents significant challenges due to the high complexity of lattice structures, particularly in generating nodal shapes where robustness and smoothness issues can arise from the complex intersections between struts. To address these challenges, this paper proposes a novel approach for lattice structure construction by cutting struts and filling void regions with subdivisional nodal shapes. Inspired by soap films, the method generates smooth, shape-preserving control meshes using Laplacian fairing and subdivides them through the point-normal Loop (PN-Loop) subdivision scheme to obtain subdivisional nodal shapes. The proposed method ensures robust model construction with reduced shape deviations, enhanced surface fairness, and smooth transitions between subdivisional nodal shapes and retained struts. The effectiveness of the method has been demonstrated by a series of examples and comparisons. The code will be open-sourced upon publication.
Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship
Venue: arXiv cs.CY 2025
Year: 2025

Extracting key concepts and their relationships from course information and materials facilitates the provision of visualizations and recommendations for learners who need to select the right courses to take from a large number of courses. However, identifying and extracting themes manually is labor-intensive and time-consuming. Previous machine learning-based methods to extract relevant concepts from courses heavily rely on detailed course materials, which necessitates labor-intensive preparation of course materials. This paper investigates the potential of LLMs such as GPT in automatically generating course concepts and their relations. Specifically, we design a suite of prompts and provide GPT with the course information with different levels of detail, thereby generating high-quality course concepts and identifying their relations. Furthermore, we comprehensively evaluate the quality of the generated concepts and relationships through extensive experiments. Our results demonstrate the viability of LLMs as a tool for supporting educational content selection and delivery.
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
Venue: arXiv cs.LG 2025
Year: 2025

Accommodating edge networks between IoT devices and the cloud server in Hierarchical Federated Learning (HFL) enhances communication efficiency without compromising data privacy. However, devices connected to the same edge often share geographic or contextual similarities, leading to varying edge-level data heterogeneity with different subsets of labels per edge, on top of device-level heterogeneity. This hierarchical non-Independent and Identically Distributed (non-IID) nature, which implies that each edge has its own optimization goal, has been overlooked in HFL research. Therefore, existing edge-accommodated HFL demonstrates inconsistent performance across edges in various hierarchical non-IID scenarios. To ensure robust performance with diverse edge-level non-IID data, we propose a Personalized Hierarchical Edge-enabled Federated Learning (PHE-FL), which personalizes each edge model to perform well on the unique class distributions specific to each edge. We evaluated PHE-FL across 4 scenarios with varying levels of edge-level non-IIDness, with extreme IoT device level non-IIDness. To accurately assess the effectiveness of our personalization approach, we deployed test sets on each edge server instead of the cloud server, and used both balanced and imbalanced test sets. Extensive experiments show that PHE-FL achieves up to 83 percent higher accuracy compared to existing federated learning approaches that incorporate edge networks, given the same number of training rounds. Moreover, PHE-FL exhibits improved stability, as evidenced by reduced accuracy fluctuations relative to the state-of-the-art FedAvg with two-level (edge and cloud) aggregation.
HAL-NeRF: High Accuracy Localization Leveraging Neural Radiance Fields
Venue: arXiv cs.CV 2025
Year: 2025

Precise camera localization is a critical task in XR applications and robotics. Using only the camera captures as input to a system is an inexpensive option that enables localization in large indoor and outdoor environments, but it presents challenges in achieving high accuracy. Specifically, camera relocalization methods, such as Absolute Pose Regression (APR), can localize cameras with a median translation error of more than $0.5m$ in outdoor scenes. This paper presents HAL-NeRF, a high-accuracy localization method that combines a CNN pose regressor with a refinement module based on a Monte Carlo particle filter. The Nerfacto model, an implementation of Neural Radiance Fields (NeRFs), is used to augment the data for training the pose regressor and to measure photometric loss in the particle filter refinement module. HAL-NeRF leverages Nerfacto's ability to synthesize high-quality novel views, significantly improving the performance of the localization pipeline. HAL-NeRF achieves state-of-the-art results that are conventionally measured as the average of the median per scene errors. The translation error was $0.025m$ and the rotation error was $0.59$ degrees and 0.04m and 0.58 degrees on the 7-Scenes dataset and Cambridge Landmarks datasets respectively, with the trade-off of increased computational time. This work highlights the potential of combining APR with NeRF-based refinement techniques to advance monocular camera relocalization accuracy.
Forecasting Conversation Derailments Through Generation
Venue: arXiv cs.CL 2025
Year: 2025

Forecasting conversation derailment can be useful in real-world settings such as online content moderation, conflict resolution, and business negotiations. However, despite language models' success at identifying offensive speech present in conversations, they struggle to forecast future conversation derailments. In contrast to prior work that predicts conversation outcomes solely based on the past conversation history, our approach samples multiple future conversation trajectories conditioned on existing conversation history using a fine-tuned LLM. It predicts the conversation outcome based on the consensus of these trajectories. We also experimented with leveraging socio-linguistic attributes, which reflect turn-level conversation dynamics, as guidance when generating future conversations. Our method of future conversation trajectories surpasses state-of-the-art results on English conversation derailment prediction benchmarks and demonstrates significant accuracy gains in ablation studies.
AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories
Venue: arXiv cs.LG 2025
Year: 2025

Web agents enable users to perform tasks on web browsers through natural language interaction. Evaluating web agents trajectories is an important problem, since it helps us determine whether the agent successfully completed the tasks. Rule-based methods are widely used for this purpose, but they are challenging to extend to new tasks and may not always recognize successful trajectories. We may achieve higher accuracy through human evaluation, but the process would be substantially slower and more expensive. Automatic evaluations with LLMs may avoid the challenges of designing new rules and manually annotating trajectories, enabling faster and cost-effective evaluation. However, it is unclear how effective they are at evaluating web agents. To this end, we propose AgentRewardBench, the first benchmark to assess the effectiveness of LLM judges for evaluating web agents. AgentRewardBench contains 1302 trajectories across 5 benchmarks and 4 LLMs. Each trajectory in AgentRewardBench is reviewed by an expert, who answers questions pertaining to the success, side effects, and repetitiveness of the agent. Using our benchmark, we evaluate 12 LLM judges and find that no single LLM excels across all benchmarks. We also find that the rule-based evaluation used by common benchmarks tends to underreport the success rate of web agents, highlighting a key weakness of rule-based evaluation and the need to develop more flexible automatic evaluations. We release the benchmark at: https://agent-reward-bench.github.io
Where Does Academic Database Research Go From Here?
Venue: arXiv cs.DB 2025
Year: 2025

Panel proposal for an open forum to discuss and debate the future of database research in the context of industry, other research communities, and AI. Includes summaries of past panels, positions from panelists, as well as positions from a sample of the data management community.
Should you use LLMs to simulate opinions? Quality checks for early-stage deliberation
Venue: arXiv cs.CY 2025
Year: 2025

The emergent capabilities of large language models (LLMs) have prompted interest in using them as surrogates for human subjects in opinion surveys. However, prior evaluations of LLM-based opinion simulation have relied heavily on costly, domain-specific survey data, and mixed empirical results leave their reliability in question. To enable cost-effective, early-stage evaluation, we introduce a quality control assessment designed to test the viability of LLM-simulated opinions on Likert-scale tasks without requiring large-scale human data for validation. This assessment comprises two key tests: \emph{logical consistency} and \emph{alignment with stakeholder expectations}, offering a low-cost, domain-adaptable validation tool. We apply our quality control assessment to an opinion simulation task relevant to AI-assisted content moderation and fact-checking workflows -- a socially impactful use case -- and evaluate seven LLMs using a baseline prompt engineering method (backstory prompting), as well as fine-tuning and in-context learning variants. None of the models or methods pass the full assessment, revealing several failure modes. We conclude with a discussion of the risk management implications and release \texttt{TopicMisinfo}, a benchmark dataset with paired human and LLM annotations simulated by various models and approaches, to support future research.
Factors Influencing Gender Representation in IT Faculty Programmes: Insights with a Focus on Software Engineering in a Nordic Context
Venue: arXiv cs.SE 2025
Year: 2025

Software engineering remains male-dominated despite efforts to attract and retain women. Many leave the field due to limited opportunities, unfair treatment, and challenging workplace cultures. Examining university life and choices is important, as these formative experiences shape career aspirations and can help address the root causes of underrepresentation in the industry. The study aimed to deepen understanding of the motivations behind women's choice of a career in IT, their experiences in academic life, and how these experiences influence their career decisions, all within a Nordic context. We used a combination of surveys in the bachelor programmes in the IT faculty and interviews with only women from software engineering (SE) to provide a comprehensive view of population experiences and a closer exploration of the experiences of a smaller sample with a focus on SE. Our results showed that family and personal interest are among the main factors motivating women to choose an IT programme. Further, women perceive more challenges following their chosen career path than men. We proposed high-level actions to address gender-related challenges and disparities based on our findings.
Generating Planning Feedback for Open-Ended Programming Exercises with LLMs
Venue: arXiv cs.CL 2025
Year: 2025

To complete an open-ended programming exercise, students need to both plan a high-level solution and implement it using the appropriate syntax. However, these problems are often autograded on the correctness of the final submission through test cases, and students cannot get feedback on their planning process. Large language models (LLM) may be able to generate this feedback by detecting the overall code structure even for submissions with syntax errors. To this end, we propose an approach that detects which high-level goals and patterns (i.e. programming plans) exist in a student program with LLMs. We show that both the full GPT-4o model and a small variant (GPT-4o-mini) can detect these plans with remarkable accuracy, outperforming baselines inspired by conventional approaches to code analysis. We further show that the smaller, cost-effective variant (GPT-4o-mini) achieves results on par with state-of-the-art (GPT-4o) after fine-tuning, creating promising implications for smaller models for real-time grading. These smaller models can be incorporated into autograders for open-ended code-writing exercises to provide feedback for students' implicit planning skills, even when their program is syntactically incorrect. Furthermore, LLMs may be useful in providing feedback for problems in other domains where students start with a set of high-level solution steps and iteratively compute the output, such as math and physics problems.
Robust Steganography from Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

Recent steganographic schemes, starting with Meteor (CCS'21), rely on leveraging large language models (LLMs) to resolve a historically-challenging task of disguising covert communication as ``innocent-looking'' natural-language communication. However, existing methods are vulnerable to ``re-randomization attacks,'' where slight changes to the communicated text, that might go unnoticed, completely destroy any hidden message. This is also a vulnerability in more traditional encryption-based stegosystems, where adversaries can modify the randomness of an encryption scheme to destroy the hidden message while preserving an acceptable covertext to ordinary users. In this work, we study the problem of robust steganography. We introduce formal definitions of weak and strong robust LLM-based steganography, corresponding to two threat models in which natural language serves as a covertext channel resistant to realistic re-randomization attacks. We then propose two constructions satisfying these notions. We design and implement our steganographic schemes that embed arbitrary secret messages into natural language text generated by LLMs, ensuring recoverability even under adversarial paraphrasing and rewording attacks. To support further research and real-world deployment, we release our implementation and datasets for public use.
A Formalism and Library for Database Visualization
Venue: arXiv cs.DB 2025
Year: 2025

Existing data visualization formalisms are restricted to single-table inputs, which makes existing visualization grammars like Vega-lite or ggplot2 tedious to use, have overly complex APIs, and unsound when visualization multi-table data. This paper presents the first visualization formalism to support databases as input -- in other words, *database visualization*. A visualization specification is defined as a mapping from database constraints (e.g., schemas, types, foreign keys) to visual representations of those constraints, and we state that a visualization is *faithful* if it visually preserves the underlying database constraints. This formalism explains how visualization designs are the result of implicit data modeling decisions. We further develop a javascript library called dvl and use a series of case studies to show its expressiveness over specialized visualization systems and existing grammar-based languages.
Game-Theoretic Coordination For Time-Critical Missions of UAV Systems
Venue: arXiv cs.MA 2025
Year: 2025

Cooperative missions involving Unmanned Aerial Vehicles (UAVs) in dynamic environments pose significant challenges in ensuring both coordination and agility. In this paper, we introduce a novel game-theoretic approach for time-critical missions, where each UAV optimizes a cost function that incorporates temporal and mission-specific constraints. The optimization is performed within a one-dimensional domain, significantly reducing the computational cost and enabling real-time application to complex and dynamic scenarios. The framework is distributed in structure, allowing to achieve global, system-wide coordination (a Nash equilibrium) by using only local information. For ideal systems, we prove the existence and exponential stability of the Nash equilibrium. Furthermore, we invoke model predictive control (MPC) for non-ideal scenarios. In particular, we propose a discrete-time optimization approach that tackles path-following errors and communication failures, ensuring reliable and agile performance in dynamic and uncertain environments. Simulation results demonstrate the effectiveness and agility of the approach in ensuring successful mission execution across diverse scenarios. Experiments using a motion capture system provide further validation under realistic conditions.
Universal Rate-Distortion-Classification Representations for Lossy Compression
Venue: arXiv cs.IT 2025
Year: 2025

In lossy compression, Wang et al. [1] recently introduced the rate-distortion-perception-classification function, which supports multi-task learning by jointly optimizing perceptual quality, classification accuracy, and reconstruction fidelity. Building on the concept of a universal encoder introduced in [2], we investigate the universal representations that enable a broad range of distortion-classification tradeoffs through a single shared encoder coupled with multiple task-specific decoders. We establish, through both theoretical analysis and numerical experiments, that for Gaussian source under mean squared error (MSE) distortion, the entire distortion-classification tradeoff region can be achieved using a single universal encoder. For general sources, we characterize the achievable region and identify conditions under which encoder reuse results in negligible distortion penalty. The experimental result on the MNIST dataset further supports our theoretical findings. We show that universal encoders can obtain distortion performance comparable to task-specific encoders. These results demonstrate the practicality and effectiveness of the proposed universal framework in multi-task compression scenarios.