OpenRaiser/Intern-Atlas huggingface dataset:
LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) have become indispensable in real-world applications. However, their widespread adoption raises significant safety concerns, particularly in responding to socially harmful questions. Despite substantial efforts to improve model safety through alignment, aligned models can still have their safety protections undermined by subsequent fine-tuning - even when the additional training data appears benign. In this paper, we empirically demonstrate that this vulnerability stems from the sensitivity of safety-critical low-rank subspaces in LLM parameters to fine-tuning. Building on this insight, we propose a novel training-free method, termed Low-Rank Extrapolation (LoX), to enhance safety robustness by extrapolating the safety subspace of an aligned LLM. Our experimental results confirm the effectiveness of LoX, demonstrating significant improvements in robustness against both benign and malicious fine-tuning attacks while preserving the model's adaptability to new tasks. For instance, LoX leads to 11% to 54% absolute reductions in attack success rates (ASR) facing benign or malicious fine-tuning attacks. By investigating the ASR landscape of parameters, we attribute the success of LoX to that the extrapolation moves LLM parameters to a flatter zone, thereby less sensitive to perturbations. The code is available at github.com/VITA-Group/LoX.
GRIM: Task-Oriented Grasping with Conditioning on Generative Examples
Venue: arXiv cs.RO 2025
Year: 2025

Task-Oriented Grasping (TOG) requires robots to select grasps that are functionally appropriate for a specified task - a challenge that demands an understanding of task semantics, object affordances, and functional constraints. We present GRIM (Grasp Re-alignment via Iterative Matching), a training-free framework that addresses these challenges by leveraging Video Generation Models (VGMs) together with a retrieve-align-transfer pipeline. Beyond leveraging VGMs, GRIM can construct a memory of object-task exemplars sourced from web images, human demonstrations, or generative models. The retrieved task-oriented grasp is then transferred and refined by evaluating it against a set of geometrically stable candidate grasps to ensure both functional suitability and physical feasibility. GRIM demonstrates strong generalization and achieves state-of-the-art performance on standard TOG benchmarks. Project website: https://grim-tog.github.io
A survey of Chernoff and Hoeffding bounds
Venue: arXiv cs.DM 2025
Year: 2025

This is a survey paper that discusses the original bounds of the seminal papers by Chernoff and Hoeffding. Moreover, it includes a variety of derivative bounds in a variety of forms. Complete proofs are provided as needed. The intent is to provide a repository of reference bounds for the interested researcher.
GFLC: Graph-based Fairness-aware Label Correction for Fair Classification
Venue: arXiv cs.LG 2025
Year: 2025

Fairness in machine learning (ML) has a critical importance for building trustworthy machine learning system as artificial intelligence (AI) systems increasingly impact various aspects of society, including healthcare decisions and legal judgments. Moreover, numerous studies demonstrate evidence of unfair outcomes in ML and the need for more robust fairness-aware methods. However, the data we use to train and develop debiasing techniques often contains biased and noisy labels. As a result, the label bias in the training data affects model performance and misrepresents the fairness of classifiers during testing. To tackle this problem, our paper presents Graph-based Fairness-aware Label Correction (GFLC), an efficient method for correcting label noise while preserving demographic parity in datasets. In particular, our approach combines three key components: prediction confidence measure, graph-based regularization through Ricci-flow-optimized graph Laplacians, and explicit demographic parity incentives. Our experimental findings show the effectiveness of our proposed approach and show significant improvements in the trade-off between performance and fairness metrics compared to the baseline.
SR-NCL: an Area-/Energy-Efficient Resilient NCL Architecture Based on Selective Redundancy
Venue: arXiv cs.AR 2025
Year: 2025

Duplication-based redundancy schemes have proven to be effective in designing fully-resilient Quasi-delay Insensitive (QDI) asynchronous circuits. The complete resiliency, however, is accompanied by significant energy, latency, and area overhead. This paper presents a novel error-tolerant Null Convention Logic (NCL) architecture based on selective redundancy. Results demonstrate the efficacy of the proposed method in terms of area and energy utilization as compared to existing duplication-based NCL designs, targeting an image processing application.
Oldies but Goldies: The Potential of Character N-grams for Romanian Texts
Venue: arXiv cs.CL 2025
Year: 2025

This study addresses the problem of authorship attribution for Romanian texts using the ROST corpus, a standard benchmark in the field. We systematically evaluate six machine learning techniques: Support Vector Machine (SVM), Logistic Regression (LR), k-Nearest Neighbors (k-NN), Decision Trees (DT), Random Forests (RF), and Artificial Neural Networks (ANN), employing character n-gram features for classification. Among these, the ANN model achieved the highest performance, including perfect classification in four out of fifteen runs when using 5-gram features. These results demonstrate that lightweight, interpretable character n-gram approaches can deliver state-of-the-art accuracy for Romanian authorship attribution, rivaling more complex methods. Our findings highlight the potential of simple stylometric features in resource, constrained or under-studied language settings.
BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Rotations have become essential to state-of-the-art quantization pipelines for large language models (LLMs) by effectively smoothing outliers in weights and activations. However, further optimizing the rotation parameters offers only limited performance gains and introduces significant training overhead: due to rotation parameter sharing, full-model must be loaded simultaneously to enable backpropagation, resulting in substantial memory consumption and limited practical utility. In this work, we identify two fundamental limitations of current rotational quantization methods: (i) rotation fails to align channel means, resulting in wider quantization bounds and increased rounding errors; and (ii) rotation makes the activation distribution more Gaussian-like, increasing energy loss caused by clipping errors. To address these issues, we introduce \textbf{BASE-Q}, a simple yet powerful approach that combines bias correction and asymmetric scaling to effectively reduce rounding and clipping errors. Furthermore, BASE-Q enables blockwise optimization, eliminating the need for memory-intensive full-model backpropagation. Extensive experiments on various LLMs and benchmarks demonstrate the effectiveness of BASE-Q, narrowing the accuracy gap to full-precision models by 50.5\%, 42.9\%, and 29.2\% compared to QuaRot, SpinQuant, and OSTQuant, respectively. The code will be released soon.
An application of machine learning to the motion response prediction of floating assets
Venue: arXiv cs.LG 2025
Year: 2025

The real-time prediction of floating offshore asset behavior under stochastic metocean conditions remains a significant challenge in offshore engineering. While traditional empirical and frequency-domain methods work well in benign conditions, they struggle with both extreme sea states and nonlinear responses. This study presents a supervised machine learning approach using multivariate regression to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth. We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver, trained on approximately $10^6$ samples spanning 100 features. The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions, significantly outperforming traditional frequency-domain methods. The framework has been successfully deployed on an operational facility, demonstrating its efficacy for real-time vessel monitoring and operational decision-making in offshore environments.
Adaptive Two Sided Laplace Transforms: A Learnable, Interpretable, and Scalable Replacement for Self-Attention
Venue: arXiv cs.LG 2025
Year: 2025

We propose an innovative, learnable two-sided short-time Laplace transform (STLT) mechanism to supplant the traditional self attention in transformer-based LLMs. Our STLT introduces trainable parameters for each Laplace node, enabling end-to-end learning of decay rates , oscillatory frequencies, and window bandwidth T. This flexibility allows the model to dynamically adapt token relevance half lives and frequency responses during training. By selecting S learnable nodes and leveraging fast recursive convolution, we achieve an effective complexity of in time and memory. We further incorporate an efficient FFT-based computation of the relevance matrix and an adaptive node allocation mechanism to dynamically adjust the number of active Laplace nodes. Empirical results on language modeling (WikiText\-103, Project Gutenberg), machine translation (WMT'14 En\-De), and long document question answering (NarrativeQA) demonstrate that our learnable STLT achieves perplexities and scores on par with or better than existing efficient transformers while naturally extending to context lengths exceeding 100k tokens or more limited only by available hardware. Ablation studies confirm the importance of learnable parameters and adaptive node allocation. The proposed approach combines interpretability, through explicit decay and frequency parameters, with scalability and robustness, offering a pathway towards ultra-long-sequence language modeling without the computational bottleneck of self-attention.
daDPO: Distribution-Aware DPO for Distilling Conversational Abilities
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) have demonstrated exceptional performance across various applications, but their conversational abilities decline sharply as model size decreases, presenting a barrier to their deployment in resource-constrained environments. Knowledge distillation with Direct Preference Optimization (dDPO) has emerged as a promising approach to enhancing the conversational abilities of smaller models using a larger teacher model. However, current methods primarily focus on 'black-box' KD, which only uses the teacher's responses, overlooking the output distribution offered by the teacher. This paper addresses this gap by introducing daDPO (Distribution-Aware DPO), a unified method for preference optimization and distribution-based distillation. We provide rigorous theoretical analysis and empirical validation, showing that daDPO outperforms existing methods in restoring performance for pruned models and enhancing smaller LLM models. Notably, in in-domain evaluation, our method enables a 20% pruned Vicuna1.5-7B to achieve near-teacher performance (-7.3% preference rate compared to that of dDPO's -31%), and allows Qwen2.5-1.5B to occasionally outperform its 7B teacher model (14.0% win rate).
OAgents: An Empirical Study of Building Effective Agents
Venue: arXiv cs.AI 2025
Year: 2025

Recently, Agentic AI has become an increasingly popular research field. However, we argue that current agent research practices lack standardization and scientific rigor, making it hard to conduct fair comparisons among methods. As a result, it is still unclear how different design choices in agent frameworks affect effectiveness, and measuring their progress remains challenging. In this work, we conduct a systematic empirical study on GAIA benchmark and BrowseComp to examine the impact of popular design choices in key agent components in a fair and rigorous manner. We find that the lack of a standard evaluation protocol makes previous works, even open-sourced ones, non-reproducible, with significant variance between random runs. Therefore, we introduce a more robust evaluation protocol to stabilize comparisons. Our study reveals which components and designs are crucial for effective agents, while others are redundant, despite seeming logical. Based on our findings, we build and open-source OAgents, a new foundation agent framework that achieves state-of-the-art performance among open-source projects. OAgents offers a modular design for various agent components, promoting future research in Agentic AI.
Graphics4Science: Computer Graphics for Scientific Impacts
Venue: arXiv cs.GR 2025
Year: 2025

Computer graphics, often associated with films, games, and visual effects, has long been a powerful tool for addressing scientific challenges--from its origins in 3D visualization for medical imaging to its role in modern computational modeling and simulation. This course explores the deep and evolving relationship between computer graphics and science, highlighting past achievements, ongoing contributions, and open questions that remain. We show how core methods, such as geometric reasoning and physical modeling, provide inductive biases that help address challenges in both fields, especially in data-scarce settings. To that end, we aim to reframe graphics as a modeling language for science by bridging vocabulary gaps between the two communities. Designed for both newcomers and experts, Graphics4Science invites the graphics community to engage with science, tackle high-impact problems where graphics expertise can make a difference, and contribute to the future of scientific discovery. Additional details are available on the course website: https://graphics4science.github.io
Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning
Venue: arXiv cs.IT 2025
Year: 2025

Six-dimensional movable antenna (6DMA) has been identified as a new disruptive technology for future wireless systems to support a large number of users with only a few antennas. However, the intricate relationships between the signal carrier wavelength and the transceiver region size lead to inaccuracies in traditional far-field 6DMA channel model, causing discrepancies between the model predictions and the hybrid-field channel characteristics in practical 6DMA systems, where users might be in the far-field region relative to the antennas on the same 6DMA surface, while simultaneously being in the near-field region relative to different 6DMA surfaces. Moreover, due to the high-dimensional channel and the coupled position and rotation constraints, the estimation of the 6DMA channel and the joint design of the 6DMA positions and rotations and the transmit beamforming at the base station (BS) incur extremely high computational complexity. To address these issues, we propose an efficient hybrid-field generalized 6DMA channel model, which accounts for planar-wave propagation within individual 6DMA surfaces and spherical-wave propagation among different 6DMA surfaces. Furthermore, by leveraging directional sparsity, we propose a low-overhead channel estimation algorithm that efficiently constructs a complete channel map for all potential antenna position-rotation pairs while limiting the training overhead incurred by antenna movement. In addition, we propose a low-complexity design leveraging deep reinforcement learning (DRL), which facilitates the joint design of the 6DMA positions, rotations, and beamforming in a unified manner. Numerical results demonstrate that the proposed hybrid-field channel model and channel estimation algorithm outperform existing approaches and that the DRL-enhanced 6DMA system significantly surpasses flexible antenna systems.
AI-based modular warning machine for risk identification in proximity healthcare
Venue: arXiv cs.LG 2025
Year: 2025

"DHEAL-COM - Digital Health Solutions in Community Medicine" is a research and technology project funded by the Italian Department of Health for the development of digital solutions of interest in proximity healthcare. The activity within the DHEAL-COM framework allows scientists to gather a notable amount of multi-modal data whose interpretation can be performed by means of machine learning algorithms. The present study illustrates a general automated pipeline made of numerous unsupervised and supervised methods that can ingest such data, provide predictive results, and facilitate model interpretations via feature identification.
Rethinking LLM Training through Information Geometry and Quantum Metrics
Venue: arXiv cs.CL 2025
Year: 2025

Optimization in large language models (LLMs) unfolds over high-dimensional parameter spaces with non-Euclidean structure. Information geometry frames this landscape using the Fisher information metric, enabling more principled learning via natural gradient descent. Though often impractical, this geometric lens clarifies phenomena such as sharp minima, generalization, and observed scaling laws. We argue that curvature-based approaches deepen our understanding of LLM training. Finally, we speculate on quantum analogies based on the Fubini-Study metric and Quantum Fisher Information, hinting at efficient optimization in quantum-enhanced systems.
Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Venue: arXiv cs.HC 2025
Year: 2025

Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (EMAs), passive sensing, and contextual features to develop emotion recognition models, which rely on EMA responses as ground truth. Due to this, it is crucial to consider EMA compliance when conducting a successful mHealth study. Utilizing machine learning is one approach that can solve this problem by sending EMAs based on the predicted likelihood of a response. However, literature suggests that this approach may lead to prompting participants more frequently during emotions associated with responsiveness, thereby narrowing the range of emotions collected. We propose a multi-objective function that utilizes machine learning to identify optimal times for sending EMAs. The function identifies optimal moments by combining predicted response likelihood with model uncertainty in emotion predictions. Uncertainty would lead the function to prioritize time points when the model is less confident, which often corresponds to underrepresented emotions. We demonstrate that this objective function would result in EMAs being sent when participants are responsive and experiencing less commonly observed emotions. The evaluation is conducted offline using two datasets: (1) 91 spousal caregivers of individuals with Alzheimer's Disease and Related dementias (ADRD), (2) 45 healthy participants. Results show that the multi-objective function tends to be higher when participants respond to EMAs and report less commonly observed emotions. This suggests that using the proposed objective function to guide EMA delivery could improve receptivity rates and capture a broader range of emotions.
ADAM-Dehaze: Adaptive Density-Aware Multi-Stage Dehazing for Improved Object Detection in Foggy Conditions
Venue: arXiv cs.CV 2025
Year: 2025

Adverse weather conditions, particularly fog, pose a significant challenge to autonomous vehicles, surveillance systems, and other safety-critical applications by severely degrading visual information. We introduce ADAM-Dehaze, an adaptive, density-aware dehazing framework that jointly optimizes image restoration and object detection under varying fog intensities. A lightweight Haze Density Estimation Network (HDEN) classifies each input as light, medium, or heavy fog. Based on this score, the system dynamically routes the image through one of three CORUN branches: Light, Medium, or Complex, each tailored to its haze regime. A novel adaptive loss balances physical-model coherence and perceptual fidelity, ensuring both accurate defogging and preservation of fine details. On Cityscapes and the real-world RTTS benchmark, ADAM-Dehaze improves PSNR by up to 2.1 dB, reduces FADE by 30 percent, and increases object detection mAP by up to 13 points, while cutting inference time by 20 percent. These results highlight the importance of intensity-specific processing and seamless integration with downstream vision tasks. Code available at: https://github.com/talha-alam/ADAM-Dehaze.
Delta: A Learned Mixed Cost-based Query Optimization Framework
Venue: arXiv cs.DB 2025
Year: 2025

Query optimizer is a crucial module for database management systems. Existing optimizers exhibit two flawed paradigms: (1) cost-based optimizers use dynamic programming with cost models but face search space explosion and heuristic pruning constraints; (2) value-based ones train value networks to enable efficient beam search, but incur higher training costs and lower accuracy. They also lack mechanisms to detect queries where they may perform poorly. To determine more efficient plans, we propose Delta, a mixed cost-based query optimization framework that consists of a compatible query detector and a two-stage planner. Delta first employs a Mahalanobis distancebased detector to preemptively filter out incompatible queries where the planner might perform poorly. For compatible queries, Delta activates its two-stage mixed cost-based planner. Stage I serves as a coarse-grained filter to generate high-quality candidate plans based on the value network via beam search, relaxing precision requirements and narrowing the search space. Stage II employs a fine-grained ranker to determine the best plan from the candidate plans based on a learned cost model. Moreover, to reduce training costs, we reuse and augment the training data from stage I to train the model in stage II. Experimental results on three workloads demonstrate that Delta identifies higher-quality plans, achieving an average 2.34x speedup over PostgreSQL and outperforming the state-of-the-art learned methods by 2.21x.
PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps
Venue: arXiv cs.RO 2025
Year: 2025

We propose PRISM-Loc - a lightweight and robust approach for localization in large outdoor environments that combines a compact topological representation with a novel scan-matching and curb-detection module operating on raw LiDAR scans. The method is designed for resource-constrained platforms and emphasizes real-time performance and resilience to common urban sensing challenges. It provides accurate localization in compact topological maps using global place recognition and an original scan matching technique. Experiments on standard benchmarks and on an embedded platform demonstrate the effectiveness of our approach. Our method achieves a 99\% success rate on the large-scale ITLP-Campus dataset while running at 150 ms per localization and using a 20 MB map for localization. We highlight three main contributions: (1) a compact representation for city-scale localization; (2) a novel curb detection and scan matching pipeline operating directly on raw LiDAR points; (3) a thorough evaluation of our method with performance analysis.