OpenRaiser/Intern-Atlas huggingface dataset:
Parallel Hierarchical Agglomerative Clustering in Low Dimensions
Venue: arXiv cs.DS 2025
Year: 2025

Hierarchical Agglomerative Clustering (HAC) is an extensively studied and widely used method for hierarchical clustering in $\mathbb{R}^k$ based on repeatedly merging the closest pair of clusters according to an input linkage function $d$. Highly parallel (i.e., NC) algorithms are known for $(1+ε)$-approximate HAC (where near-minimum rather than minimum pairs are merged) for certain linkage functions that monotonically increase as merges are performed. However, no such algorithms are known for many important but non-monotone linkage functions such as centroid and Ward's linkage. In this work, we show that a general class of non-monotone linkage functions -- which include centroid and Ward's distance -- admit efficient NC algorithms for $(1+ε)$-approximate HAC in low dimensions. Our algorithms are based on a structural result which may be of independent interest: the height of the hierarchy resulting from any constant-approximate HAC on $n$ points for this class of linkage functions is at most $\operatorname{poly}(\log n)$ as long as $k = O(\log \log n / \log \log \log n)$. Complementing our upper bounds, we show that NC algorithms for HAC with these linkage functions in \emph{arbitrary} dimensions are unlikely to exist by showing that HAC is CC-hard when $d$ is centroid distance and $k = n$.
Irredundant $k$-Fold Cross-Validation
Venue: arXiv cs.LG 2025
Year: 2025

In traditional k-fold cross-validation, each instance is used ($k-1$) times for training and once for testing, leading to redundancy that lets many instances disproportionately influence the learning phase. We introduce Irredundant $k$-fold cross-validation, a novel method that guarantees each instance is used exactly once for training and once for testing across the entire validation procedure. This approach ensures a more balanced utilization of the dataset, mitigates overfitting due to instance repetition, and enables sharper distinctions in comparative model analysis. The method preserves stratification and remains model-agnostic, i.e., compatible with any classifier. Experimental results demonstrate that it delivers consistent performance estimates across diverse datasets -- comparable to $k$-fold cross-validation -- while providing less optimistic variance estimates because training partitions are non-overlapping, and significantly reducing the overall computational cost.
What Can Grokking Teach Us About Learning Under Nonstationarity?
Venue: arXiv cs.LG 2025
Year: 2025

In continual learning problems, it is often necessary to overwrite components of a neural network's learned representation in response to changes in the data stream; however, neural networks often exhibit \primacy bias, whereby early training data hinders the network's ability to generalize on later tasks. While feature-learning dynamics of nonstationary learning problems are not well studied, the emergence of feature-learning dynamics is known to drive the phenomenon of grokking, wherein neural networks initially memorize their training data and only later exhibit perfect generalization. This work conjectures that the same feature-learning dynamics which facilitate generalization in grokking also underlie the ability to overwrite previous learned features as well, and methods which accelerate grokking by facilitating feature-learning dynamics are promising candidates for addressing primacy bias in non-stationary learning problems. We then propose a straightforward method to induce feature-learning dynamics as needed throughout training by increasing the effective learning rate, i.e. the ratio between parameter and update norms. We show that this approach both facilitates feature-learning and improves generalization in a variety of settings, including grokking, warm-starting neural network training, and reinforcement learning tasks.
ModShift: Model Privacy via Designed Shifts
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, shifts are introduced to preserve model privacy against an eavesdropper in federated learning. Model learning is treated as a parameter estimation problem. This perspective allows us to derive the Fisher Information matrix of the model updates from the shifted updates and drive them to singularity, thus posing a hard estimation problem for Eve. The shifts are securely shared with the central server to maintain model accuracy at the server and participating devices. A convergence test is proposed to detect if model updates have been tampered with and we show that our scheme passes this test. Numerical results show that our scheme achieves a higher model shift when compared to a noise injection scheme while requiring a lesser bandwidth secret channel.
The Effect of Pointer Analysis on Semantic Conflict Detection
Venue: arXiv cs.SE 2025
Year: 2025

Current merge tools don't detect semantic conflicts, which occur when changes from different developers are textually integrated but semantically interfere with each other. Although researchers have proposed static analyses for detecting semantic conflicts, these analyses suffer from significant false positive rates. To understand whether such false positives could be reduced by using pointer analysis in the implementation of semantic conflict static analyses, we conduct an empirical study. We implement the same analysis with and without pointer analysis, run them on two datasets, observe how often they differ, and compare their accuracy and computational performance. Although pointer analysis is known to improve precision in static analysis, we find that its effect on semantic conflict detection can be drastic: we observe a significant reduction in timeouts and false positives, but also a significant increase in false negatives, with prohibitive drops in recall and F1-score. These results suggest that, in the context of semantic conflict detection, we should explore hybrid analysis techniques, combining aspects of both implementations we compare in our study.
Product-Congruence Games: A Unified Impartial-Game Framework for RSA ($ϕ$-MuM) and AES (poly-MuM)
Venue: arXiv cs.DM 2025
Year: 2025

RSA exponent reduction and AES S-box inversion share a hidden commonality: both are governed by the same impartial combinatorial principle, which we call a Product-Congruence Game (PCG). A Product-Congruence Game tracks play via the modular or finite-field product of heap values, providing a single invariant that unifies the algebraic cores of these two ubiquitous symmetric and asymmetric cryptosystems. We instantiate this framework with two companion games. First, $ϕ$-MuM, in which a left-associated "multi-secret" RSA exponent chain compresses into the game of Multiplicative Modular Nim, PCG($k,\{1\}$), where $k = ord_N(g)$. The losing predicate then factorizes via the Chinese remainder theorem, mirroring RSA's structure. Second, poly-MuM, our model for finite-field inversion such as the AES S-box. For poly-MuM we prove the single-hole property inside its threshold region, implying that the Sprague-Grundy values are multiplicative under disjunctive sums in that region. Beyond these instances, we establish four structural theorems for a general Product-Congruence Game PCG($m,R$): (i) single-heap repair above the modulus, (ii) ultimate period $m$ per coordinate, (iii) exact and asymptotic losing densities, and (iv) confinement of optimal play to a finite indeterminacy region. An operation-alignment collapse principle explains why some variants degenerate to a single aggregate while MuM, $ϕ$-MuM and poly-MuM retain rich local structure. All ingredients (multiplicative orders, the Chinese remainder theorem, finite fields) are classical; the contribution is the unified aggregation-compression viewpoint that embeds both RSA and AES inside one impartial-game framework, together with the structural and collapse theorems.
Hybrid-Domain Synergistic Transformer for Hyperspectral Image Denoising
Venue: arXiv cs.CV 2025
Year: 2025

Hyperspectral image denoising faces the challenge of multi-dimensional coupling of spatially non-uniform noise and spectral correlation interference. Existing deep learning methods mostly focus on RGB images and struggle to effectively handle the unique spatial-spectral characteristics and complex noise distributions of hyperspectral images (HSI). This paper proposes an HSI denoising framework, Hybrid-Domain Synergistic Transformer Network (HDST), based on frequency domain enhancement and multiscale modeling, achieving three-dimensional collaborative processing of spatial, frequency and channel domains. The method innovatively integrates three key mechanisms: (1) introducing an FFT preprocessing module with multi-band convolution to extract cross-band correlations and decouple spectral noise components; (2) designing a dynamic cross-domain attention module that adaptively fuses spatial domain texture features and frequency domain noise priors through a learnable gating mechanism; (3) building a hierarchical architecture where shallow layers capture global noise statistics using multiscale atrous convolution, and deep layers achieve detail recovery through frequency domain postprocessing. Experiments on both real and synthetic datasets demonstrate that HDST significantly improves denoising performance while maintaining computational efficiency, validating the effectiveness of the proposed method. This research provides new insights and a universal framework for addressing complex noise coupling issues in HSI and other high-dimensional visual data. The code is available at https://github.com/lhy-cn/HDST-HSIDenoise.
Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion
Venue: arXiv cs.NI 2025
Year: 2025

In response to Distributed Denial of Service (DDoS) attacks, recent research efforts increasingly rely on Machine Learning (ML)-based solutions, whose effectiveness largely depends on the quality of labeled training datasets. To address the scarcity of such datasets, data augmentation with synthetic traces is often employed. However, current synthetic trace generation methods struggle to capture the complex temporal patterns and spatial distributions exhibited in emerging DDoS attacks. This results in insufficient resemblance to real traces and unsatisfied detection accuracy when applied to ML tasks. In this paper, we propose Dual-Stream Temporal-Field Diffusion (DSTF-Diffusion), a multi-view, multi-stream network traffic generative model based on diffusion models, featuring two main streams: The field stream utilizes spatial mapping to bridge network data characteristics with pre-trained realms of stable diffusion models, effectively translating complex network interactions into formats that stable diffusion can process, while the spatial stream adopts a dynamic temporal modeling approach, meticulously capturing the intrinsic temporal patterns of network traffic. Extensive experiments demonstrate that data generated by our model exhibits higher statistical similarity to originals compared to current state-of-the-art solutions, and enhance performances on a wide range of downstream tasks.
An Automated Deep Segmentation and Spatial-Statistics Approach for Post-Blast Rock Fragmentation Assessment
Venue: arXiv cs.CV 2025
Year: 2025

We introduce an end-to-end pipeline that leverages a fine-tuned YOLO12l-seg model -- trained on over 500 annotated post-blast images -- to deliver real-time instance segmentation (Box mAP@0.5 ~ 0.769, Mask mAP@0.5 ~ 0.800 at ~ 15 FPS). High-fidelity masks are converted into normalized 3D coordinates, from which we extract multi-metric spatial descriptors: principal component directions, kernel density hotspots, size-depth regression, and Delaunay edge statistics. We present four representative examples to illustrate key fragmentation patterns. Experimental results confirm the framework's accuracy, robustness to small-object crowding, and feasibility for rapid, automated blast-effect assessment in field conditions.
An Optimal Transport-Based Method for Computing LM Rate and Its Convergence Analysis
Venue: arXiv cs.IT 2025
Year: 2025

The mismatch capacity characterizes the highest information rate of the channel under a prescribed decoding metric and serves as a critical performance indicator in numerous practical communication scenarios. Compared to the commonly used Generalized Mutual Information (GMI), the Lower bound on the Mismatch capacity (LM rate) generally provides a tighter lower bound on the mismatch capacity. However, the efficient computation of the LM rate is significantly more challenging than that of the GMI, particularly as the size of the channel input alphabet increases. This growth in complexity renders standard numerical methods (e.g., interior point methods) computationally intensive and, in some cases, impractical. In this work, we reformulate the computation of the LM rate as a special instance of the optimal transport (OT) problem with an additional constraint. Building on this formulation, we develop a novel numerical algorithm based on the Sinkhorn algorithm, which is well known for its efficiency in solving entropy regularized optimization problems. We further provide the convergence analysis of the proposed algorithm, revealing that the algorithm has a sub-linear convergence rate. Numerical experiments demonstrate the feasibility and efficiency of the proposed algorithm for the computation of the LM rate.
Relating System Safety and Machine Learnt Model Performance
Venue: arXiv cs.SE 2025
Year: 2025

The prediction quality of machine learnt models and the functionality they ultimately enable (e.g., object detection), is typically evaluated using a variety of quantitative metrics that are specified in the associated model performance requirements. When integrating such models into aeronautical applications, a top-down safety assessment process must influence both the model performance metrics selected, and their acceptable range of values. Often, however, the relationship of system safety objectives to model performance requirements and the associated metrics is unclear. Using an example of an aircraft emergency braking system containing a machine learnt component (MLC) responsible for object detection and alerting, this paper first describes a simple abstraction of the required MLC behavior. Then, based on that abstraction, an initial method is given to derive the minimum safety-related performance requirements, the associated metrics, and their targets for the both MLC and its underlying deep neural network, such that they meet the quantitative safety objectives obtained from the safety assessment process. We give rationale as to why the proposed method should be considered valid, also clarifying the assumptions made, the constraints on applicability, and the implications for verification.
Dynamite: Real-Time Debriefing Slide Authoring through AI-Enhanced Multimodal Interaction
Venue: arXiv cs.HC 2025
Year: 2025

Facilitating class-wide debriefings after small-group discussions is a common strategy in ethics education. Instructor interviews revealed that effective debriefings should highlight frequently discussed themes and surface underrepresented viewpoints, making accurate representations of insight occurrence essential. Yet authoring presentations in real time is cognitively overwhelming due to the volume of data and tight time constraints. We present Dynamite, an AI-assisted system that enables semantic updates to instructor-authored slides during live classroom discussions. These updates are powered by semantic data binding, which links slide content to evolving discussion data, and semantic suggestions, which offer revision options aligned with pedagogical goals. In a within-subject in-lab study with 12 participants, Dynamite outperformed a text-based AI baseline in content accuracy and quality. Participants used voice and sketch input to quickly organize semantic blocks, then applied suggestions to accelerate refinement as data stabilized.
High-Performance Parallel Optimization of the Fish School Behaviour on the Setonix Platform Using OpenMP
Venue: arXiv cs.DC 2025
Year: 2025

This paper presents an in-depth investigation into the high-performance parallel optimization of the Fish School Behaviour (FSB) algorithm on the Setonix supercomputing platform using the OpenMP framework. Given the increasing demand for enhanced computational capabilities for complex, large-scale calculations across diverse domains, there's an imperative need for optimized parallel algorithms and computing structures. The FSB algorithm, inspired by nature's social behavior patterns, provides an ideal platform for parallelization due to its iterative and computationally intensive nature. This study leverages the capabilities of the Setonix platform and the OpenMP framework to analyze various aspects of multi-threading, such as thread counts, scheduling strategies, and OpenMP constructs, aiming to discern patterns and strategies that can elevate program performance. Experiments were designed to rigorously test different configurations, and our results not only offer insights for parallel optimization of FSB on Setonix but also provide valuable references for other parallel computational research using OpenMP. Looking forward, other factors, such as cache behavior and thread scheduling strategies at micro and macro levels, hold potential for further exploration and optimization.
SoK: Root Cause of $1 Billion Loss in Smart Contract Real-World Attacks via a Systematic Literature Review of Vulnerabilities
Venue: arXiv cs.CR 2025
Year: 2025

While catastrophic attacks on Ethereum persist, vulnerability research remains fixated on implementation-level smart contract bugs, creating a gap between academic understanding of vulnerabilities and the root causes of high-impact, real-world incidents. To address this, we employ a two-pronged methodology: first, a systematic literature review of 71 academic papers to build a catalog of 24 active and 5 deprecated vulnerabilities. Second, we conduct an in-depth, empirical analysis of 50 of the most severe real-world attacks between 2022 and 2025, collectively incurring over $1.09B in losses, to identify their root causes. We introduce the concept of "exploit chains" by revealing that many incidents are not caused by isolated vulnerabilities but by combinations of human, operational, and economic design flaws that link with implementation bugs to enable an attack. Our analysis yields insights on how decentralized applications are exploited in practice, leading to a novel, four-tier root-cause framework that moves beyond code-level vulnerabilities. We find that real-world successful attacks on Ethereum (and related networks) trace back to one of the four tiers of (1) protocol logic design, (2) lifecycle and governance, (3) external dependencies, and (4) classic smart contract vulnerabilities. We investigate the suitability of this multi-tier incident root-cause framework via a case study.
MoCTEFuse: Illumination-Gated Mixture of Chiral Transformer Experts for Multi-Level Infrared and Visible Image Fusion
Venue: arXiv cs.CV 2025
Year: 2025

While illumination changes inevitably affect the quality of infrared and visible image fusion, many outstanding methods still ignore this factor and directly merge the information from source images, leading to modality bias in the fused results. To this end, we propose a dynamic multi-level image fusion network called MoCTEFuse, which applies an illumination-gated Mixture of Chiral Transformer Experts (MoCTE) to adaptively preserve texture details and object contrasts in balance. MoCTE consists of high- and low-illumination expert subnetworks, each built upon the Chiral Transformer Fusion Block (CTFB). Guided by the illumination gating signals, CTFB dynamically switches between the primary and auxiliary modalities as well as assigning them corresponding weights with its asymmetric cross-attention mechanism. Meanwhile, it is stacked at multiple stages to progressively aggregate and refine modality-specific and cross-modality information. To facilitate robust training, we propose a competitive loss function that integrates illumination distributions with three levels of sub-loss terms. Extensive experiments conducted on the DroneVehicle, MSRS, TNO and RoadScene datasets show MoCTEFuse's superior fusion performance. Finally, it achieves the best detection mean Average Precision (mAP) of 70.93% on the MFNet dataset and 45.14% on the DroneVehicle dataset. The code and model are released at https://github.com/Bitlijinfu/MoCTEFuse.
Color histogram equalization and fine-tuning to improve expression recognition of (partially occluded) faces on sign language datasets
Venue: arXiv cs.CV 2025
Year: 2025

The goal of this investigation is to quantify to what extent computer vision methods can correctly classify facial expressions on a sign language dataset. We extend our experiments by recognizing expressions using only the upper or lower part of the face, which is needed to further investigate the difference in emotion manifestation between hearing and deaf subjects. To take into account the peculiar color profile of a dataset, our method introduces a color normalization stage based on histogram equalization and fine-tuning. The results show the ability to correctly recognize facial expressions with 83.8% mean sensitivity and very little variance (.042) among classes. Like for humans, recognition of expressions from the lower half of the face (79.6%) is higher than that from the upper half (77.9%). Noticeably, the classification accuracy from the upper half of the face is higher than human level.
IQ Test for LLMs: An Evaluation Framework for Uncovering Core Skills in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Current evaluations of large language models (LLMs) rely on benchmark scores, but it is difficult to interpret what these individual scores reveal about a model's overall skills. Specifically, as a community we lack understanding of how tasks relate to one another, what they measure in common, how they differ, or which ones are redundant. As a result, models are often assessed via a single score averaged across benchmarks, an approach that fails to capture the models' wholistic strengths and limitations. Here, we propose a new evaluation paradigm that uses factor analysis to identify latent skills driving performance across benchmarks. We apply this method to a comprehensive new leaderboard showcasing the performance of 60 LLMs on 44 tasks, and identify a small set of latent skills that largely explain performance. Finally, we turn these insights into practical tools that identify redundant tasks, aid in model selection, and profile models along each latent skill.
Co-NAML-LSTUR: A Combined Model with Attentive Multi-View Learning and Long- and Short-term User Representations for News Recommendation
Venue: arXiv cs.CL 2025
Year: 2025

News recommendation systems play a critical role in alleviating information overload by delivering personalized content. A key challenge lies in jointly modeling multi-view representations of news articles and capturing the dynamic, dual-scale nature of user interests-encompassing both short- and long-term preferences. Prior methods often rely on single-view features or insufficiently model user behavior across time. In this work, we introduce Co-NAML-LSTUR, a hybrid news recommendation framework that integrates NAML for attentive multi-view news encoding and LSTUR for hierarchical user modeling, designed for training on limited data resources. Our approach leverages BERT-based embeddings to enhance semantic representation. We evaluate Co-NAML-LSTUR on two widely used benchmarks, MIND-small and MIND-large. Results show that our model significantly outperforms strong baselines, achieving improvements over NRMS by 1.55% in AUC and 1.15% in MRR, and over NAML by 2.45% in AUC and 1.71% in MRR. These findings highlight the effectiveness of our efficiency-focused hybrid model, which combines multi-view news modeling with dual-scale user representations for practical, resource-limited resources rather than a claim to absolute state-of-the-art (SOTA). The implementation of our model is publicly available at https://github.com/MinhNguyenDS/Co-NAML-LSTUR