OpenRaiser/Intern-Atlas huggingface dataset:
Realizing Graphs with Cut Constraints
Venue: arXiv cs.CC 2025
Year: 2025

Given a finite non-decreasing sequence $d=(d_1,\ldots,d_n)$ of natural numbers, the Graph Realization problem asks whether $d$ is a graphic sequence, i.e., there exists a labeled simple graph such that $(d_1,\ldots,d_n)$ is the degree sequence of this graph. Such a problem can be solved in polynomial time due to the Erdős and Gallai characterization of graphic sequences. Since vertex degree is the size of a trivial edge cut, we consider a natural generalization of Graph Realization, where we are given a finite sequence $d=(d_1,\ldots,d_n)$ of natural numbers (representing the trivial edge cut sizes) and a list of nontrivial cut constraints $\mathcal{L}$ composed of pairs $(S_j,\ell_j)$ where $S_j\subset \{v_1,\ldots,v_n\}$, and $\ell_j$ is a natural number. In such a problem, we are asked whether there is a simple graph with vertex set $V=\{v_1,\ldots,v_n\}$ such that $v_i$ has degree $d_i$ and $\partial(S_j)$ is an edge cut of size $\ell_j$, for each $(S_j,\ell_j)\in \mathcal{L}$. We show that such a problem is polynomial-time solvable whenever each $S_j$ has size at most three. Conversely, assuming P $\neq$ NP, we prove that it cannot be solved in polynomial time when $\mathcal{L}$ contains pairs with sets of size four, and our hardness result holds even assuming that each $d_i$ of $d$ equals $1$.
Let's Talk Futures: A Literature Review of HCI's Future-Orientation
Venue: arXiv cs.HC 2025
Year: 2025

HCI is future-oriented by nature: it explores new human--technology interactions and applies the findings to promote and shape vital visions of society. Still, the visions of futures in HCI publications seem largely implicit, techno-deterministic, narrow, and lacking in roadmaps and attention to uncertainties. A literature review centered on this problem examined futuring and its forms in the ACM Digital Library's most frequently cited HCI publications. This analysis entailed developing the four-category framework SPIN, informed by futures studies literature. The results confirm that, while technology indeed drives futuring in HCI, a growing body of HCI research is coming to challenge techno-centric visions. Emerging foci of HCI futuring demonstrate active exploration of uncertainty, a focus on human experience, and contestation of dominant narratives. The paper concludes with insight illuminating factors behind techno-centrism's continued dominance of HCI discourse, as grounding for five opportunities for the field to expand its contribution to futures and anticipation research.
Optimal Microcontroller Usage in Reconfigurable Intelligent Surface: Batteryless IoT Systems Case Study
Venue: arXiv cs.IT 2025
Year: 2025

To enhance wireless communication in IoT systems using reconfigurable intelligent surfaces (RISs), efficient control of programmable passive and active elements is essential. However, increasing RIS elements requires more microcontrollers, raising complexity and cost. This paper proposes a modular approach ("Module"), where each microcontroller controls a module of optimal active or passive elements. The module size is determined using a non-linear energy harvesting model, where a batteryless IoT (b-IoT) sensor harvests energy from base station (BS) RF signals. We optimize the number of modules (microcontrollers) to minimize energy consumption while satisfying energy harvesting and information causality constraints. Simulations show that RIS module-assisted energy harvesting improves IoT system performance by ~100% compared to models without RIS panels.
LoRA Training Provably Converges to a Low-Rank Global Minimum or It Fails Loudly (But it Probably Won't Fail)
Venue: arXiv cs.LG 2025
Year: 2025

Low-rank adaptation (LoRA) has become a standard approach for fine-tuning large foundation models. However, our theoretical understanding of LoRA remains limited as prior analyses of LoRA's training dynamics either rely on linearization arguments or consider highly simplified setups. In this work, we analyze the LoRA loss landscape without such restrictive assumptions. We define two regimes: a "special regime", which includes idealized setups where linearization arguments hold, and a "generic regime" representing more realistic setups where linearization arguments do not hold. In the generic regime, we show that LoRA training converges to a global minimizer with low rank and small magnitude, or a qualitatively distinct solution with high rank and large magnitude. Finally, we argue that the zero-initialization and weight decay in LoRA training induce an implicit bias toward the low-rank, small-magnitude region of the parameter space -- where global minima lie -- thus shedding light on why LoRA training usually succeeds in finding global minima.
A Survey of Reinforcement Learning for Optimization in Automation
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain.
Deterministic Independent Sets in the Semi-Streaming Model
Venue: arXiv cs.DS 2025
Year: 2025

We consider the independent set problem in the semi-streaming model. For any input graph $G=(V, E)$ with $n$ vertices, an independent set is a set of vertices with no edges between any two elements. In the semi-streaming model, $G$ is presented as a stream of edges and any algorithm must use $\tilde O(n)$ bits of memory to output a large independent set at the end of the stream. Prior work has designed various semi-streaming algorithms for finding independent sets. Due to the hardness of finding maximum and maximal independent sets in the semi-streaming model, the focus has primarily been on finding independent sets in terms of certain parameters, such as the maximum degree $Δ$. In particular, there is a simple randomized algorithm that obtains independent sets of size $\frac n{Δ+1}$ in expectation, which can also be achieved with high probability using more complicated algorithms. For deterministic algorithms, the best bounds are significantly weaker. In fact, the best we currently know is a straightforward algorithm that finds an $\tildeΩ\left(\frac n{Δ^2}\right)$ size independent set. We show that this straightforward algorithm is nearly optimal by proving that any deterministic semi-streaming algorithm can only output an $\tilde O\left(\frac n{Δ^2}\right)$ size independent set. Our result proves a strong separation between the power of deterministic and randomized semi-streaming algorithms for the independent set problem.
RTD-Conjecture and Concept Classes Induced by Graphs
Venue: arXiv cs.DM 2025
Year: 2025

It is conjectured that the recursive teaching dimension of any finite concept class is upper-bounded by the VC-dimension of this class times a universal constant. In this paper, we confirm this conjecture for two rich families of concept classes where each class is induced by some graph $G$. For each $G$, we consider the class whose concepts represent stars in $G$ as well as the class whose concepts represent connected sets in $G$. We show that, for concept classes of this kind, the recursive teaching dimension either equals the VC-dimension or is less by $1$.
Learning to Predict Global Atrial Fibrillation Dynamics from Sparse Measurements
Venue: arXiv cs.LG 2025
Year: 2025

Catheter ablation of Atrial Fibrillation (AF) consists of a one-size-fits-all treatment with limited success in persistent AF. This may be due to our inability to map the dynamics of AF with the limited resolution and coverage provided by sequential contact mapping catheters, preventing effective patient phenotyping for personalised, targeted ablation. Here we introduce FibMap, a graph recurrent neural network model that reconstructs global AF dynamics from sparse measurements. Trained and validated on 51 non-contact whole atria recordings, FibMap reconstructs whole atria dynamics from 10% surface coverage, achieving a 210% lower mean absolute error and an order of magnitude higher performance in tracking phase singularities compared to baseline methods. Clinical utility of FibMap is demonstrated on real-world contact mapping recordings, achieving reconstruction fidelity comparable to non-contact mapping. FibMap's state-spaces and patient-specific parameters offer insights for electrophenotyping AF. Integrating FibMap into clinical practice could enable personalised AF care and improve outcomes.
Standardisation of Convex Ultrasound Data Through Geometric Analysis and Augmentation
Venue: arXiv cs.CV 2025
Year: 2025

The application of ultrasound in healthcare has seen increased diversity and importance. Unlike other medical imaging modalities, ultrasound research and development has historically lagged, particularly in the case of applications with data-driven algorithms. A significant issue with ultrasound is the extreme variability of the images, due to the number of different machines available and the possible combination of parameter settings. One outcome of this is the lack of standardised and benchmarking ultrasound datasets. The method proposed in this article is an approach to alleviating this issue of disorganisation. For this purpose, the issue of ultrasound data sparsity is examined and a novel perspective, approach, and solution is proposed; involving the extraction of the underlying ultrasound plane within the image and representing it using annulus sector geometry. An application of this methodology is proposed, which is the extraction of scan lines and the linearisation of convex planes. Validation of the robustness of the proposed method is performed on both private and public data. The impact of deformation and the invertibility of augmentation using the estimated annulus sector parameters is also studied. Keywords: Ultrasound, Annulus Sector, Augmentation, Linearisation.
Registration, Detection, and Deregistration: Analyzing DNS Abuse for Phishing Attacks
Venue: arXiv cs.CR 2025
Year: 2025

Phishing continues to pose a significant cybersecurity threat. While blocklists currently serve as a primary defense, due to their reactive, passive nature, these delayed responses leave phishing websites operational long enough to harm potential victims. It is essential to address this fundamental challenge at the root, particularly in phishing domains. Domain registration presents a crucial intervention point, as domains serve as the primary gateway between users and websites. We conduct a comprehensive longitudinal analysis of 690,502 unique phishing domains, spanning a 39 month period, to examine their characteristics and behavioral patterns throughout their lifecycle-from initial registration to detection and eventual deregistration. We find that 66.1% of the domains in our dataset are maliciously registered, leveraging cost-effective TLDs and targeting brands by mimicking their domain names under alternative TLDs (e.g., .top and .tk) instead of the TLDs under which the brand domains are registered (e.g., .com and .ru). We also observe minimal improvements in detection speed for maliciously registered domains compared to compromised domains. Detection times vary widely across blocklists, and phishing domains remain accessible for an average of 11.5 days after detection, prolonging their potential impact. Our systematic investigation uncovers key patterns from registration through detection to deregistration, which could be leveraged to enhance anti-phishing active defenses at the DNS level.
KIMAs: A Configurable Knowledge Integrated Multi-Agent System
Venue: arXiv cs.AI 2025
Year: 2025

Knowledge-intensive conversations supported by large language models (LLMs) have become one of the most popular and helpful applications that can assist people in different aspects. Many current knowledge-intensive applications are centered on retrieval-augmented generation (RAG) techniques. While many open-source RAG frameworks facilitate the development of RAG-based applications, they often fall short in handling practical scenarios complicated by heterogeneous data in topics and formats, conversational context management, and the requirement of low-latency response times. This technical report presents a configurable knowledge integrated multi-agent system, KIMAs, to address these challenges. KIMAs features a flexible and configurable system for integrating diverse knowledge sources with 1) context management and query rewrite mechanisms to improve retrieval accuracy and multi-turn conversational coherency, 2) efficient knowledge routing and retrieval, 3) simple but effective filter and reference generation mechanisms, and 4) optimized parallelizable multi-agent pipeline execution. Our work provides a scalable framework for advancing the deployment of LLMs in real-world settings. To show how KIMAs can help developers build knowledge-intensive applications with different scales and emphases, we demonstrate how we configure the system to three applications already running in practice with reliable performance.
RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets
Venue: arXiv cs.CV 2025
Year: 2025

We present RigAnything, a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints and skeleton topologies and assigning skinning weights in a template-free manner. Unlike most existing auto-rigging methods, which rely on predefined skeleton templates and are limited to specific categories like humanoid, RigAnything approaches the rigging problem in an autoregressive manner, iteratively predicting the next joint based on the global input shape and the previous prediction. While autoregressive models are typically used to generate sequential data, RigAnything extends its application to effectively learn and represent skeletons, which are inherently tree structures. To achieve this, we organize the joints in a breadth-first search (BFS) order, enabling the skeleton to be defined as a sequence of 3D locations and the parent index. Furthermore, our model improves the accuracy of position prediction by leveraging diffusion modeling, ensuring precise and consistent placement of joints within the hierarchy. This formulation allows the autoregressive model to efficiently capture both spatial and hierarchical relationships within the skeleton. Trained end-to-end on both RigNet and Objaverse datasets, RigAnything demonstrates state-of-the-art performance across diverse object types, including humanoids, quadrupeds, marine creatures, insects, and many more, surpassing prior methods in quality, robustness, generalizability, and efficiency. It achieves significantly faster performance than existing auto-rigging methods, completing rigging in under a few seconds per shape. Please check our website for more details: https://www.liuisabella.com/RigAnything
Online Social Support Detection in Spanish Social Media Texts
Venue: arXiv cs.CL 2025
Year: 2025

The advent of social media has transformed communication, enabling individuals to share their experiences, seek support, and participate in diverse discussions. While extensive research has focused on identifying harmful content like hate speech, the recognition and promotion of positive and supportive interactions remain largely unexplored. This study proposes an innovative approach to detecting online social support in Spanish-language social media texts. We introduce the first annotated dataset specifically created for this task, comprising 3,189 YouTube comments classified as supportive or non-supportive. To address data imbalance, we employed GPT-4o to generate paraphrased comments and create a balanced dataset. We then evaluated social support classification using traditional machine learning models, deep learning architectures, and transformer-based models, including GPT-4o, but only on the unbalanced dataset. Subsequently, we utilized a transformer model to compare the performance between the balanced and unbalanced datasets. Our findings indicate that the balanced dataset yielded improved results for Task 2 (Individual and Group) and Task 3 (Nation, Other, LGBTQ, Black Community, Women, Religion), whereas GPT-4o performed best for Task 1 (Social Support and Non-Support). This study highlights the significance of fostering a supportive online environment and lays the groundwork for future research in automated social support detection.
Language Shift or Maintenance? An Intergenerational Study of the Tibetan Community in Saudi Arabia
Venue: arXiv cs.CL 2025
Year: 2025

The present study provides the first-ever report on the language shift from Tibetan to Arabic among descendants of Tibetan families who migrated from the Tibet region to Saudi Arabia around 70 years ago. The aim of this study was to determine whether three age groups had adopted different practices in terms of maintaining Tibetan or shifting to Hijazi Arabic. To this end, 96 male and female members of the Tibetan community responded to a questionnaire in which they were asked about their code choice in different domains (home, neighbourhood, friends and relatives, expressing emotion, and performing religious rituals). The data revealed significant intergenerational differences between members of the community in terms of the extent of the shift to Arabic, with Tibetan rarely used by younger members and older members making only slightly more use of it. The difference between the three age groups was significant, at a p-value of .001.
Are Smarter LLMs Safer? Exploring Safety-Reasoning Trade-offs in Prompting and Fine-Tuning
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable success across various NLP benchmarks. However, excelling in complex tasks that require nuanced reasoning and precise decision-making demands more than raw language proficiency--LLMs must reason, i.e., think logically, draw from past experiences, and synthesize information to reach conclusions and take action. To enhance reasoning abilities, approaches such as prompting and fine-tuning have been widely explored. While these methods have led to clear improvements in reasoning, their impact on LLM safety remains less understood. In this work, we investigate the interplay between reasoning and safety in LLMs. We highlight the latent safety risks that arise as reasoning capabilities improve, shedding light on previously overlooked vulnerabilities. At the same time, we explore how reasoning itself can be leveraged to enhance safety, uncovering potential mitigation strategies. By examining both the risks and opportunities in reasoning-driven LLM safety, our study provides valuable insights for developing models that are not only more capable but also more trustworthy in real-world deployments.
Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer
Venue: arXiv cs.LG 2025
Year: 2025

Prostate cancer (Pca) continues to be a leading cause of cancer-related mortality in men, and the limitations in precision of traditional diagnostic methods such as the Digital Rectal Exam (DRE), Prostate-Specific Antigen (PSA) testing, and biopsies underscore the critical importance of accurate staging detection in enhancing treatment outcomes and improving patient prognosis. This study leverages machine learning and deep learning approaches, along with feature selection and extraction methods, to enhance PCa pathological staging predictions using RNA sequencing data from The Cancer Genome Atlas (TCGA). Gene expression profiles from 486 tumors were analyzed using advanced algorithms, including Random Forest (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The performance of the study is measured with respect to the F1-score, as well as precision and recall, all of which are calculated as weighted averages. The results reveal that the highest test F1-score, approximately 83%, was achieved by the Random Forest algorithm, followed by Logistic Regression at 80%, while both Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM) scored around 79%. Furthermore, deep learning models with data augmentation achieved an accuracy of 71. 23%, while PCA-based dimensionality reduction reached an accuracy of 69.86%. This research highlights the potential of AI-driven approaches in clinical oncology, paving the way for more reliable diagnostic tools that can ultimately improve patient outcomes.
Large Language Models and Provenance Metadata for Determining the Relevance of Images and Videos in News Stories
Venue: arXiv cs.CL 2025
Year: 2025

The most effective misinformation campaigns are multimodal, often combining text with images and videos taken out of context -- or fabricating them entirely -- to support a given narrative. Contemporary methods for detecting misinformation, whether in deepfakes or text articles, often miss the interplay between multiple modalities. Built around a large language model, the system proposed in this paper addresses these challenges. It analyzes both the article's text and the provenance metadata of included images and videos to determine whether they are relevant. We open-source the system prototype and interactive web interface.
QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language
Venue: arXiv cs.CR 2025
Year: 2025

Recent advances in large language models (LLMs) have demonstrated remarkable potential in the field of natural language processing. Unfortunately, LLMs face significant security and ethical risks. Although techniques such as safety alignment are developed for defense, prior researches reveal the possibility of bypassing such defenses through well-designed jailbreak attacks. In this paper, we propose QueryAttack, a novel framework to examine the generalizability of safety alignment. By treating LLMs as knowledge databases, we translate malicious queries in natural language into structured non-natural query language to bypass the safety alignment mechanisms of LLMs. We conduct extensive experiments on mainstream LLMs, and the results show that QueryAttack not only can achieve high attack success rates (ASRs), but also can jailbreak various defense methods. Furthermore, we tailor a defense method against QueryAttack, which can reduce ASR by up to $64\%$ on GPT-4-1106. Our code is available at https://github.com/horizonsinzqs/QueryAttack.