OpenRaiser/Intern-Atlas huggingface dataset:
Fine-Grained Detection of AI-Generated Text Using Sentence-Level Segmentation
Venue: arXiv cs.CL 2025
Year: 2025

Generation of Artificial Intelligence (AI) texts in important works has become a common practice that can be used to misuse and abuse AI at various levels. Traditional AI detectors often rely on document-level classification, which struggles to identify AI content in hybrid or slightly edited texts designed to avoid detection, leading to concerns about the model's efficiency, which makes it hard to distinguish between human-written and AI-generated texts. A sentence-level sequence labeling model proposed to detect transitions between human- and AI-generated text, leveraging nuanced linguistic signals overlooked by document-level classifiers. By this method, detecting and segmenting AI and human-written text within a single document at the token-level granularity is achieved. Our model combines the state-of-the-art pre-trained Transformer models, incorporating Neural Networks (NN) and Conditional Random Fields (CRFs). This approach extends the power of transformers to extract semantic and syntactic patterns, and the neural network component to capture enhanced sequence-level representations, thereby improving the boundary predictions by the CRF layer, which enhances sequence recognition and further identification of the partition between Human- and AI-generated texts. The evaluation is performed on two publicly available benchmark datasets containing collaborative human and AI-generated texts. Our experimental comparisons are with zero-shot detectors and the existing state-of-the-art models, along with rigorous ablation studies to justify that this approach, in particular, can accurately detect the spans of AI texts in a completely collaborative text. All our source code and the processed datasets are available in our GitHub repository.
Trainee Action Recognition through Interaction Analysis in CCATT Mixed-Reality Training
Venue: arXiv cs.CV 2025
Year: 2025

This study examines how Critical Care Air Transport Team (CCATT) members are trained using mixed-reality simulations that replicate the high-pressure conditions of aeromedical evacuation. Each team - a physician, nurse, and respiratory therapist - must stabilize severely injured soldiers by managing ventilators, IV pumps, and suction devices during flight. Proficient performance requires clinical expertise and cognitive skills, such as situational awareness, rapid decision-making, effective communication, and coordinated task management, all of which must be maintained under stress. Recent advances in simulation and multimodal data analytics enable more objective and comprehensive performance evaluation. In contrast, traditional instructor-led assessments are subjective and may overlook critical events, thereby limiting generalizability and consistency. However, AI-based automated and more objective evaluation metrics still demand human input to train the AI algorithms to assess complex team dynamics in the presence of environmental noise and the need for accurate re-identification in multi-person tracking. To address these challenges, we introduce a systematic, data-driven assessment framework that combines Cognitive Task Analysis (CTA) with Multimodal Learning Analytics (MMLA). We have developed a domain-specific CTA model for CCATT training and a vision-based action recognition pipeline using a fine-tuned Human-Object Interaction model, the Cascade Disentangling Network (CDN), to detect and track trainee-equipment interactions over time. These interactions automatically yield performance indicators (e.g., reaction time, task duration), which are mapped onto a hierarchical CTA model tailored to CCATT operations, enabling interpretable, domain-relevant performance evaluations.
GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Venue: arXiv cs.LG 2025
Year: 2025

Multi-objective optimization (MOO) is essential for solving complex real-world problems involving multiple conflicting objectives. However, many practical applications - including engineering design, autonomous systems, and machine learning - often yield non-convex, degenerate, or discontinuous Pareto frontiers, which involve traditional scalarization and Pareto Set Learning (PSL) methods that struggle to approximate accurately. Existing PSL approaches perform well on convex fronts but tend to fail in capturing the diversity and structure of irregular Pareto sets commonly observed in real-world scenarios. In this paper, we propose Gaussian-PSL, a novel framework that integrates Gaussian Splatting into PSL to address the challenges posed by non-convex Pareto frontiers. Our method dynamically partitions the preference vector space, enabling simple MLP networks to learn localized features within each region, which are then integrated by an additional MLP aggregator. This partition-aware strategy enhances both exploration and convergence, reduces sensi- tivity to initialization, and improves robustness against local optima. We first provide the mathematical formulation for controllable Pareto set learning using Gaussian Splat- ting. Then, we introduce the Gaussian-PSL architecture and evaluate its performance on synthetic and real-world multi-objective benchmarks. Experimental results demonstrate that our approach outperforms standard PSL models in learning irregular Pareto fronts while maintaining computational efficiency and model simplicity. This work offers a new direction for effective and scalable MOO under challenging frontier geometries.
Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
Venue: arXiv cs.AI 2025
Year: 2025

Test-time scaling (TTS) has been shown to improve the performance of large language models (LLMs) by sampling and aggregating diverse reasoning paths. However, existing research has overlooked a critical issue: selection bias of reasoning strategies during scaling. Specifically, when generating reasoning processes, LLMs tend to follow certain strategies (e.g., algebraic solutions for math problems) while neglecting other valid alternatives (e.g., geometric solutions), resulting in insufficient exploration of the solution space. To further understand the impact of this bias, we present a theoretical analysis that reveals when it undermines the effectiveness of test-time scaling. Motivated by this theoretical insight, we introduce TTS-Uniform, a framework designed to mitigate the selection bias of reasoning strategies. It (i) identifies potential strategies, (ii) uniformly allocates the sampling budget across them, and (iii) filters out unstable strategies prior to aggregation. Experimental results show that TTS-Uniform significantly enhances scaling effectiveness across multiple mainstream LLMs and benchmark datasets.
Shilling Recommender Systems by Generating Side-feature-aware Fake User Profiles
Venue: arXiv cs.IR 2025
Year: 2025

Recommender systems (RS) greatly influence users' consumption decisions, making them attractive targets for malicious shilling attacks that inject fake user profiles to manipulate recommendations. Existing shilling methods can generate effective and stealthy fake profiles when training data only contain rating matrix, but they lack comprehensive solutions for scenarios where side features are present and utilized by the recommender. To address this gap, we extend the Leg-UP framework by enhancing the generator architecture to incorporate side features, enabling the generation of side-feature-aware fake user profiles. Experiments on benchmarks show that our method achieves strong attack performance while maintaining stealthiness.
Sketching approximations and LP approximations for finite CSPs are related
Venue: arXiv cs.CC 2025
Year: 2025

We identify a connection between the approximability of CSPs in two models: (i) sublinear space streaming algorithms, and (ii) the basic LP relaxation. We show that whenever the basic LP admits an integrality gap, there is an $Ω(\sqrt{n})$-space sketching lower bound. We also show that all existing linear space streaming lower bounds for Max-CSPs can be lifted to integrality gap instances for basic LPs. For bounded-degree graphs, by combining the distributed algorithm of Yoshida (STOC 2011) for approximately solving the basic LP with techniques described in Saxena, Singer, Sudan, and Velusamy (SODA 2025) for simulating a distributed algorithm by a sublinear space streaming algorithm on bounded-degree instances of Max-DICUT, it appears that there are sublinear space streaming algorithms implementing the basic LP, for every CSP. Based on our results, we conjecture the following dichotomy theorem: Whenever the basic LP admits an integrality gap, there is a linear space single-pass streaming lower bound, and when the LP is roundable, there is a sublinear space streaming algorithm.
Can multimodal representation learning by alignment preserve modality-specific information?
Venue: arXiv cs.CV 2025
Year: 2025

Combining multimodal data is a key issue in a wide range of machine learning tasks, including many remote sensing problems. In Earth observation, early multimodal data fusion methods were based on specific neural network architectures and supervised learning. Ever since, the scarcity of labeled data has motivated self-supervised learning techniques. State-of-the-art multimodal representation learning techniques leverage the spatial alignment between satellite data from different modalities acquired over the same geographic area in order to foster a semantic alignment in the latent space. In this paper, we investigate how this methods can preserve task-relevant information that is not shared across modalities. First, we show, under simplifying assumptions, when alignment strategies fundamentally lead to an information loss. Then, we support our theoretical insight through numerical experiments in more realistic settings. With those theoretical and empirical evidences, we hope to support new developments in contrastive learning for the combination of multimodal satellite data. Our code and data is publicly available at https://github.com/Romain3Ch216/alg_maclean_25.
DragOSM: Extract Building Roofs and Footprints from Aerial Images by Aligning Historical Labels
Venue: arXiv cs.CV 2025
Year: 2025

Extracting polygonal roofs and footprints from remote sensing images is critical for large-scale urban analysis. Most existing methods rely on segmentation-based models that assume clear semantic boundaries of roofs, but these approaches struggle in off- nadir images, where the roof and footprint are significantly displaced, and facade pixels are fused with the roof boundary. With the increasing availability of open vector map annotations, e.g., OpenStreetMap, utilizing historical labels for off-nadir image annotation has become viable because remote sensing images are georeferenced once captured. However, these historical labels commonly suffer from significant positional discrepancies with new images and only have one annotation (roof or footprint), which fails to describe the correct structures of a building. To address these discrepancies, we first introduce a concept of an alignment token, which encodes the correction vector to guide the label correction. Based on this concept, we then propose Drag OpenStreetMap Labels (DragOSM), a novel model designed to align dislocated historical labels with roofs and footprints. Specifically, DragOSM formulates the label alignment as an interactive denoising process, modeling the positional discrepancy as a Gaussian distribution. During training, it learns to correct these errors by simulating misalignment with random Gaussian perturbations; during inference, it iteratively refines the positions of input labels. To validate our method, we further present a new dataset, Repairing Buildings in OSM (ReBO), comprising 179,265 buildings with both OpenStreetMap and manually corrected annotations across 5,473 images from 41 cities. Experimental results on ReBO demonstrate the effectiveness of DragOSM. Code, dataset, and trained models are publicly available at https://github.com/likaiucas/DragOSM.git.
The Reverse File System: Towards open cost-effective secure WORM storage devices for logging
Venue: arXiv cs.CR 2025
Year: 2025

Write Once Read Many (WORM) properties for storage devices are desirable to ensure data immutability for applications such as secure logging, regulatory compliance, archival storage, and other types of backup systems. WORM devices guarantee that data, once written, cannot be altered or deleted. However, implementing secure and compatible WORM storage remains a challenge. Traditional solutions often rely on specialized hardware, which is either costly, closed, or inaccessible to the general public. Distributed approaches, while promising, introduce additional risks such as denial-of-service vulnerabilities and operational complexity. We introduce Socarrat, a novel, cost-effective, and local WORM storage solution that leverages a simple external USB device (specifically, a single-board computer running Linux with USB On-The-Go support). The resulting device can be connected via USB, appearing as an ordinary external disk formatted with an ext4 or exFAT file system, without requiring any specialized software or drivers. By isolating the WORM enforcement mechanism in a dedicated USB hardware module, Socarrat significantly reduces the attack surface and ensures that even privileged attackers cannot modify or erase stored data. In addition to the WORM capacity, the system is designed to be tamper-evident, becoming resilient against advanced attacks. This work describes a novel approach, the Reverse File System, based on inferring the file system operations occurring at higher layers in the host computer where Socarrat is mounted. The paper also describes the current Socarrat prototype, implemented in Go and available as free/libre software. Finally, it provides a complete evaluation of the logging performance on different single-board computers.
Towards Seeing Bones at Radio Frequency
Venue: arXiv cs.GR 2025
Year: 2025

Wireless sensing literature has long aspired to achieve X-ray-like vision at radio frequencies. Yet, state-of-the-art wireless sensing literature has yet to generate the archetypal X-ray image: one of the bones beneath flesh. In this paper, we explore MCT, a penetration-based RF-imaging system for imaging bones at mm-resolution, one that significantly exceeds prior penetration-based RF imaging literature. Indeed the long wavelength, significant attenuation and complex diffraction that occur as RF propagates through flesh, have long limited imaging resolution (to several centimeters at best). We address these concerns through a novel penetration-based synthetic aperture algorithm, coupled with a learning-based pipeline to correct for diffraction-induced artifacts. A detailed evaluation of meat models demonstrates a resolution improvement from sub-decimeter to sub-centimeter over prior art in RF penetrative imaging.
Detection of Misreporting Attacks on Software-Defined Immersive Environments
Venue: arXiv cs.NI 2025
Year: 2025

The ability to centrally control network infrastructure using a programmable middleware has made Software-Defined Networking (SDN) ideal for emerging applications, such as immersive environments. However, such flexibility introduces new vulnerabilities, such as switch misreporting led load imbalance, which in turn make such immersive environment vulnerable to severe quality degradation. In this paper, we present a hybrid machine learning (ML)-based network anomaly detection framework that identifies such stealthy misreporting by capturing temporal inconsistencies in switch-reported loads, and thereby counter potentially catastrophic quality degradation of hosted immersive application. The detection system combines unsupervised anomaly scoring with supervised classification to robustly distinguish malicious behavior. Data collected from a realistic testbed deployment under both benign and adversarial conditions is used to train and evaluate the model. Experimental results show that the framework achieves high recall in detecting misreporting behavior, making it effective for early and reliable detection in SDN environments.
NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

While vision-language models (VLMs) excel at tasks involving single images or short videos, they still struggle with Long Video Question Answering (LVQA) due to its demand for complex multi-step temporal reasoning. Vanilla approaches, which simply sample frames uniformly and feed them to a VLM along with the question, incur significant token overhead. This forces aggressive downsampling of long videos, causing models to miss fine-grained visual structure, subtle event transitions, and key temporal cues. Recent works attempt to overcome these limitations through heuristic approaches; however, they lack explicit mechanisms for encoding temporal relationships and fail to provide any formal guarantees that the sampled context actually encodes the compositional or causal logic required by the question. To address these foundational gaps, we introduce NeuS-QA, a training-free, plug-and-play neuro-symbolic pipeline for LVQA. NeuS-QA first translates a natural language question into a logic specification that models the temporal relationship between frame-level events. Next, we construct a video automaton to model the video's frame-by-frame event progression, and finally employ model checking to compare the automaton against the specification to identify all video segments that satisfy the question's logical requirements. Only these logic-verified segments are submitted to the VLM, thus improving interpretability, reducing hallucinations, and enabling compositional reasoning without modifying or fine-tuning the model. Experiments on the LongVideoBench and CinePile LVQA benchmarks show that NeuS-QA significantly improves performance by over 10%, particularly on questions involving event ordering, causality, and multi-step reasoning. We open-source our code at https://utaustin-swarmlab.github.io/NeuS-QA/.
A Knowledge Graph-based Retrieval-Augmented Generation Framework for Algorithm Selection in the Facility Layout Problem
Venue: arXiv cs.IR 2025
Year: 2025

Selecting a solution algorithm for the Facility Layout Problem (FLP), an NP-hard optimization problem with multiobjective trade-off, is a complex task that requires deep expert knowledge. The performance of a given algorithm depends on the specific characteristics of the problem, such as the number of facilities, objectives, and constraints. This creates a need for a data-driven recommendation method to guide algorithm selection in automated design systems. This paper introduces a new recommendation method to make this expertise accessible, based on a Knowledge Graph-Based Retrieval-Augmented Generation (KG-RAG) framework. In this framework, a domain-specific knowledge graph (KG) is constructed from the literature. The method then employs a multifaceted retrieval mechanism to gather relevant evidence from this KG using three distinct approaches: precise graph-based search, flexible vector-based search, and cluster-based high-level search. The retrieved evidence is utilized by a Large Language Model (LLM) to generate algorithm recommendations based on data-driven reasoning. This KG-RAG framework is tested on a use case consisting of six problems comprising of complex multi-objective and multi-constraint FLP case. The results are compared with the Gemini 1.5 Flash chatbot. The results show that KG-RAG achieves an average reasoning score of 4.7 out of 5 compared to 3.3 for the baseline chatbot.
TMD-TTS: A Unified Tibetan Multi-Dialect Text-to-Speech Synthesis for Ü-Tsang, Amdo and Kham Speech Dataset Generation
Venue: arXiv cs.CL 2025
Year: 2025

Tibetan is a low-resource language with limited parallel speech corpora spanning its three major dialects (Ü-Tsang, Amdo, and Kham), limiting progress in speech modeling. To address this issue, we propose TMD-TTS, a unified Tibetan multi-dialect text-to-speech (TTS) framework that synthesizes parallel dialectal speech from explicit dialect labels. Our method features a dialect fusion module and a Dialect-Specialized Dynamic Routing Network (DSDR-Net) to capture fine-grained acoustic and linguistic variations across dialects. Extensive objective and subjective evaluations demonstrate that TMD-TTS significantly outperforms baselines in dialectal expressiveness. We further validate the quality and utility of the synthesized speech through a challenging Speech-to-Speech Dialect Conversion (S2SDC) task.
ARK-V1: An LLM-Agent for Knowledge Graph Question Answering Requiring Commonsense Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) show strong reasoning abilities but rely on internalized knowledge that is often insufficient, outdated, or incorrect when trying to answer a question that requires specific domain knowledge. Knowledge Graphs (KGs) provide structured external knowledge, yet their complexity and multi-hop reasoning requirements make integration challenging. We present ARK-V1, a simple KG-agent that iteratively explores graphs to answer natural language queries. We evaluate several not fine-tuned state-of-the art LLMs as backbones for ARK-V1 on the CoLoTa dataset, which requires both KG-based and commonsense reasoning over long-tail entities. ARK-V1 achieves substantially higher conditional accuracies than Chain-of-Thought baselines, and larger backbone models show a clear trend toward better coverage, correctness, and stability.
Learning to Rank with Top-$K$ Fairness
Venue: arXiv cs.LG 2025
Year: 2025

Fairness in ranking models is crucial, as disparities in exposure can disproportionately affect protected groups. Most fairness-aware ranking systems focus on ensuring comparable average exposure for groups across the entire ranked list, which may not fully address real-world concerns. For example, when a ranking model is used for allocating resources among candidates or disaster hotspots, decision-makers often prioritize only the top-$K$ ranked items, while the ranking beyond top-$K$ becomes less relevant. In this paper, we propose a list-wise learning-to-rank framework that addresses the issues of inequalities in top-$K$ rankings at training time. Specifically, we propose a top-$K$ exposure disparity measure that extends the classic exposure disparity metric in a ranked list. We then learn a ranker to balance relevance and fairness in top-$K$ rankings. Since direct top-$K$ selection is computationally expensive for a large number of items, we transform the non-differentiable selection process into a differentiable objective function and develop efficient stochastic optimization algorithms to achieve both high accuracy and sufficient fairness. Extensive experiments demonstrate that our method outperforms existing methods.
GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer
Venue: arXiv cs.CV 2025
Year: 2025

Despite Bengali being the sixth most spoken language in the world, handwritten text recognition (HTR) systems for Bengali remain severely underdeveloped. The complexity of Bengali script--featuring conjuncts, diacritics, and highly variable handwriting styles--combined with a scarcity of annotated datasets makes this task particularly challenging. We present GraDeT-HTR, a resource-efficient Bengali handwritten text recognition system based on a Grapheme-aware Decoder-only Transformer architecture. To address the unique challenges of Bengali script, we augment the performance of a decoder-only transformer by integrating a grapheme-based tokenizer and demonstrate that it significantly improves recognition accuracy compared to conventional subword tokenizers. Our model is pretrained on large-scale synthetic data and fine-tuned on real human-annotated samples, achieving state-of-the-art performance on multiple benchmark datasets.