OpenRaiser/Intern-Atlas huggingface dataset:
Effective Application of Normalized Min-Sum Decoding for Short BCH Codes
Venue: arXiv cs.IT 2025
Year: 2025

This paper introduces an enhanced normalized min-sum decoder designed to address the performance and complexity challenges associated with developing parallelizable decoders for short BCH codes in high-throughput applications. The decoder optimizes the standard parity-check matrix using heuristic binary summation and random cyclic row shifts, resulting in a Tanner graph with low density, controlled redundancy, and minimized length-4 cycles. The impact of row redundancy and rank deficiency in the dual code's minimum-weight codewords on decoding performance is analyzed. To improve convergence, three random automorphisms are applied simultaneously to the inputs, with the resulting messages merged at the end of each iteration. Extensive simulations demonstrate that, for BCH codes with block lengths of 63 and 127, the enhanced normalized min-sum decoder achieves a 1-2 dB performance gain and 100X faster convergence compared to existing parallel and iterative decoders. Additionally, a hybrid decoding scheme is proposed, which selectively activates order statistics decoding when the enhanced normalized min-sum decoder fails. This hybrid approach is shown to approach maximum-likelihood performance while retaining the advantages of the normalized min-sum decoder across a broad SNR range.
SoftPatch+: Fully Unsupervised Anomaly Classification and Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Although mainstream unsupervised anomaly detection (AD) (including image-level classification and pixel-level segmentation)algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper is the first to consider fully unsupervised industrial anomaly detection (i.e., unsupervised AD with noisy data). To solve this problem, we proposed memory-based unsupervised AD methods, SoftPatch and SoftPatch+, which efficiently denoise the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset, and SoftPatch+ has more robust performance which is articularly useful in real-world industrial inspection scenarios with high levels of noise (from 10% to 40%). Comprehensive experiments conducted in diverse noise scenarios demonstrate that both SoftPatch and SoftPatch+ outperform the state-of-the-art AD methods on the MVTecAD, ViSA, and BTAD benchmarks. Furthermore, the performance of SoftPatch and SoftPatch+ is comparable to that of the noise-free methods in conventional unsupervised AD setting. The code of the proposed methods can be found at https://github.com/TencentYoutuResearch/AnomalyDetection-SoftPatch.
TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization
Venue: arXiv cs.SD 2025
Year: 2025

We introduce TangoFlux, an efficient Text-to-Audio (TTA) generative model with 515M parameters, capable of generating up to 30 seconds of 44.1kHz audio in just 3.7 seconds on a single A40 GPU. A key challenge in aligning TTA models lies in the difficulty of creating preference pairs, as TTA lacks structured mechanisms like verifiable rewards or gold-standard answers available for Large Language Models (LLMs). To address this, we propose CLAP-Ranked Preference Optimization (CRPO), a novel framework that iteratively generates and optimizes preference data to enhance TTA alignment. We demonstrate that the audio preference dataset generated using CRPO outperforms existing alternatives. With this framework, TangoFlux achieves state-of-the-art performance across both objective and subjective benchmarks. We open source all code and models to support further research in TTA generation.
Improving Low-Resource Machine Translation via Cross-Linguistic Transfer from Typologically Similar High-Resource Languages
Venue: arXiv cs.CL 2025
Year: 2025

This study examines the cross-linguistic effectiveness of transfer learning for low-resource machine translation by fine-tuning models initially trained on typologically similar high-resource languages, using limited data from the target low-resource language. We hypothesize that linguistic similarity enables efficient adaptation, reducing the need for extensive training data. To test this, we conduct experiments on five typologically diverse language pairs spanning distinct families: Semitic (Modern Standard Arabic to Levantine Arabic), Bantu (Hausa to Zulu), Romance (Spanish to Catalan), Slavic (Slovak to Macedonian), and a language isolate (Eastern Armenian to Western Armenian). Results show that transfer learning consistently improves translation quality across all pairs, confirming its applicability beyond closely related languages. As a secondary analysis, we vary key hyperparameters learning rate, batch size, number of epochs, and weight decay to ensure results are not dependent on a single configuration. We find that moderate batch sizes (e.g., 32) are often optimal for similar pairs, smaller sizes benefit less similar pairs, and excessively high learning rates can destabilize training. These findings provide empirical evidence for the generalizability of transfer learning across language families and offer practical guidance for building machine translation systems in low-resource settings with minimal tuning effort.
ELECTRA and GPT-4o: Cost-Effective Partners for Sentiment Analysis
Venue: arXiv cs.CL 2025
Year: 2025

Bidirectional transformers excel at sentiment analysis, and Large Language Models (LLM) are effective zero-shot learners. Might they perform better as a team? This paper explores collaborative approaches between ELECTRA and GPT-4o for three-way sentiment classification. We fine-tuned (FT) four models (ELECTRA Base/Large, GPT-4o/4o-mini) using a mix of reviews from Stanford Sentiment Treebank (SST) and DynaSent. We provided input from ELECTRA to GPT as: predicted label, probabilities, and retrieved examples. Sharing ELECTRA Base FT predictions with GPT-4o-mini significantly improved performance over either model alone (82.50 macro F1 vs. 79.14 ELECTRA Base FT, 79.41 GPT-4o-mini) and yielded the lowest cost/performance ratio (\$0.12/F1 point). However, when GPT models were fine-tuned, including predictions decreased performance. GPT-4o FT-M was the top performer (86.99), with GPT-4o-mini FT close behind (86.70) at much less cost (\$0.38 vs. \$1.59/F1 point). Our results show that augmenting prompts with predictions from fine-tuned encoders is an efficient way to boost performance, and a fine-tuned GPT-4o-mini is nearly as good as GPT-4o FT at 76% less cost. Both are affordable options for projects with limited resources.
Systematic Evaluation and Guidelines for Segment Anything Model in Surgical Video Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Surgical video segmentation is critical for AI to interpret spatial-temporal dynamics in surgery, yet model performance is constrained by limited annotated data. The SAM2 model, pretrained on natural videos, offers potential for zero-shot surgical segmentation, but its applicability in complex surgical environments, with challenges like tissue deformation and instrument variability, remains unexplored. We present the first comprehensive evaluation of the zero-shot capability of SAM2 in 9 surgical datasets (17 surgery types), covering laparoscopic, endoscopic, and robotic procedures. We analyze various prompting (points, boxes, mask) and {finetuning (dense, sparse) strategies}, robustness to surgical challenges, and generalization across procedures and anatomies. Key findings reveal that while SAM2 demonstrates notable zero-shot adaptability in structured scenarios (e.g., instrument segmentation, {multi-organ segmentation}, and scene segmentation), its performance varies under dynamic surgical conditions, highlighting gaps in handling temporal coherence and domain-specific artifacts. These results highlight future pathways to adaptive data-efficient solutions for the surgical data science field.
VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling
Venue: arXiv cs.CV 2025
Year: 2025

Long-context video modeling is critical for multimodal large language models (MLLMs), enabling them to process movies, online video streams, and so on. Despite its advances, handling long videos remains challenging due to the difficulty in efficiently understanding the extremely long video context. This paper aims to address this issue from aspects of model architecture, training data, training strategy and evaluation benchmark. First, we propose a novel Hierarchical video token Compression (HiCo) method, which leverages visual redundancy in long videos to compress long video context from Clip-level to Video-level, reducing the computation significantly while preserving essential details, achieving an extreme compression ratio of approximately 1/50 with almost no performance loss. Second, we introduce a multi-stage short-to-long learning scheme, a large-scale dataset of real-world long videos named LongVid, and a challenging ``Multi-Hop Needle-In-A-Video-Haystack'' benchmark. Finally, we build a powerful video MLLM named VideoChat-Flash, which shows a leading performance on both mainstream long and short video benchmarks at the 2B and 7B model scale. It first gets 99.1% accuracy over 10,000 frames in NIAH among open-source models.
Labels Generated by Large Language Models Help Measure People's Empathy in Vitro
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have revolutionised many fields, with LLM-as-a-service (LLMSaaS) offering accessible, general-purpose solutions without costly task-specific training. In contrast to the widely studied prompt engineering for directly solving tasks (in vivo), this paper explores LLMs' potential for in-vitro applications: using LLM-generated labels to improve supervised training of mainstream models. We examine two strategies - (1) noisy label correction and (2) training data augmentation - in empathy computing, an emerging task to predict psychology-based questionnaire outcomes from inputs like textual narratives. Crowdsourced datasets in this domain often suffer from noisy labels that misrepresent underlying empathy. We show that replacing or supplementing these crowdsourced labels with LLM-generated labels, developed using psychology-based scale-aware prompts, achieves statistically significant accuracy improvements. Notably, the RoBERTa pre-trained language model (PLM) trained with noise-reduced labels yields a state-of-the-art Pearson correlation coefficient of 0.648 on the public NewsEmp benchmarks. This paper further analyses evaluation metric selection and demographic biases to help guide the future development of more equitable empathy computing models. Code and LLM-generated labels are available at https://github.com/hasan-rakibul/LLMPathy.
Dynamics of Adversarial Attacks on Large Language Model-Based Search Engines
Venue: arXiv cs.CL 2025
Year: 2025

The increasing integration of Large Language Model (LLM) based search engines has transformed the landscape of information retrieval. However, these systems are vulnerable to adversarial attacks, especially ranking manipulation attacks, where attackers craft webpage content to manipulate the LLM's ranking and promote specific content, gaining an unfair advantage over competitors. In this paper, we study the dynamics of ranking manipulation attacks. We frame this problem as an Infinitely Repeated Prisoners' Dilemma, where multiple players strategically decide whether to cooperate or attack. We analyze the conditions under which cooperation can be sustained, identifying key factors such as attack costs, discount rates, attack success rates, and trigger strategies that influence player behavior. We identify tipping points in the system dynamics, demonstrating that cooperation is more likely to be sustained when players are forward-looking. However, from a defense perspective, we find that simply reducing attack success probabilities can, paradoxically, incentivize attacks under certain conditions. Furthermore, defensive measures to cap the upper bound of attack success rates may prove futile in some scenarios. These insights highlight the complexity of securing LLM-based systems. Our work provides a theoretical foundation and practical insights for understanding and mitigating their vulnerabilities, while emphasizing the importance of adaptive security strategies and thoughtful ecosystem design.
Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform
Venue: arXiv cs.AI 2025
Year: 2025

This study proposes the design and implementation of a multimodal LLM-based Multi-Agent System (MAS) leveraging a No-Code platform to address the practical constraints and significant entry barriers associated with AI adoption in enterprises. Advanced AI technologies, such as Large Language Models (LLMs), often pose challenges due to their technical complexity and high implementation costs, making them difficult for many organizations to adopt. To overcome these limitations, this research develops a No-Code-based Multi-Agent System designed to enable users without programming knowledge to easily build and manage AI systems. The study examines various use cases to validate the applicability of AI in business processes, including code generation from image-based notes, Advanced RAG-based question-answering systems, text-based image generation, and video generation using images and prompts. These systems lower the barriers to AI adoption, empowering not only professional developers but also general users to harness AI for significantly improved productivity and efficiency. By demonstrating the scalability and accessibility of No-Code platforms, this study advances the democratization of AI technologies within enterprises and validates the practical applicability of Multi-Agent Systems, ultimately contributing to the widespread adoption of AI across various industries.
Residual connections provably mitigate oversmoothing in graph neural networks
Venue: arXiv cs.LG 2025
Year: 2025

Graph neural networks (GNNs) have achieved remarkable empirical success in processing and representing graph-structured data across various domains. However, a significant challenge known as "oversmoothing" persists, where vertex features become nearly indistinguishable in deep GNNs, severely restricting their expressive power and practical utility. In this work, we analyze the asymptotic oversmoothing rates of deep GNNs with and without residual connections by deriving explicit convergence rates for a normalized vertex similarity measure. Our analytical framework is grounded in the multiplicative ergodic theorem. Furthermore, we demonstrate that adding residual connections effectively mitigates or prevents oversmoothing across several broad families of parameter distributions. The theoretical findings are strongly supported by numerical experiments.
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
Venue: arXiv cs.CL 2025
Year: 2025

Counterfactual examples are widely used in natural language processing (NLP) as valuable data to improve models, and in explainable artificial intelligence (XAI) to understand model behavior. The automated generation of counterfactual examples remains a challenging task even for large language models (LLMs), despite their impressive performance on many tasks. In this paper, we first introduce ZeroCF, a faithful approach for leveraging important words derived from feature attribution methods to generate counterfactual examples in a zero-shot setting. Second, we present a new framework, FitCF, which further verifies aforementioned counterfactuals by label flip verification and then inserts them as demonstrations for few-shot prompting, outperforming two state-of-the-art baselines. Through ablation studies, we identify the importance of each of FitCF's core components in improving the quality of counterfactuals, as assessed through flip rate, perplexity, and similarity measures. Furthermore, we show the effectiveness of LIME and Integrated Gradients as backbone attribution methods for FitCF and find that the number of demonstrations has the largest effect on performance. Finally, we reveal a strong correlation between the faithfulness of feature attribution scores and the quality of generated counterfactuals, which we hope will serve as an important finding for future research in this direction.
Navigating Nuance: In Quest for Political Truth
Venue: arXiv cs.CL 2025
Year: 2025

This study investigates the several nuanced rationales for countering the rise of political bias. We evaluate the performance of the Llama-3 (70B) language model on the Media Bias Identification Benchmark (MBIB), based on a novel prompting technique that incorporates subtle reasons for identifying political leaning. Our findings underscore the challenges of detecting political bias and highlight the potential of transfer learning methods to enhance future models. Through our framework, we achieve a comparable performance with the supervised and fully fine-tuned ConvBERT model, which is the state-of-the-art model, performing best among other baseline models for the political bias task on MBIB. By demonstrating the effectiveness of our approach, we contribute to the development of more robust tools for mitigating the spread of misinformation and polarization. Our codes and dataset are made publicly available in github.
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity
Venue: arXiv cs.CR 2025
Year: 2025

The rapid proliferation of Industrial Internet of Things (IIoT) systems necessitates advanced, interpretable, and scalable intrusion detection systems (IDS) to combat emerging cyber threats. Traditional IDS face challenges such as high computational demands, limited explainability, and inflexibility against evolving attack patterns. To address these limitations, this study introduces the Lightweight Explainable Network Security framework (LENS-XAI), which combines robust intrusion detection with enhanced interpretability and scalability. LENS-XAI integrates knowledge distillation, variational autoencoder models, and attribution-based explainability techniques to achieve high detection accuracy and transparency in decision-making. By leveraging a training set comprising 10% of the available data, the framework optimizes computational efficiency without sacrificing performance. Experimental evaluation on four benchmark datasets: Edge-IIoTset, UKM-IDS20, CTU-13, and NSL-KDD, demonstrates the framework's superior performance, achieving detection accuracies of 95.34%, 99.92%, 98.42%, and 99.34%, respectively. Additionally, the framework excels in reducing false positives and adapting to complex attack scenarios, outperforming existing state-of-the-art methods. Key strengths of LENS-XAI include its lightweight design, suitable for resource-constrained environments, and its scalability across diverse IIoT and cybersecurity contexts. Moreover, the explainability module enhances trust and transparency, critical for practical deployment in dynamic and sensitive applications. This research contributes significantly to advancing IDS by addressing computational efficiency, feature interpretability, and real-world applicability. Future work could focus on extending the framework to ensemble AI systems for distributed environments, further enhancing its robustness and adaptability.
Make Shuffling Great Again: A Side-Channel Resistant Fisher-Yates Algorithm for Protecting Neural Networks
Venue: arXiv cs.CR 2025
Year: 2025

Neural network models implemented in embedded devices have been shown to be susceptible to side-channel attacks (SCAs), allowing recovery of proprietary model parameters, such as weights and biases. There are already available countermeasure methods currently used for protecting cryptographic implementations that can be tailored to protect embedded neural network models. Shuffling, a hiding-based countermeasure that randomly shuffles the order of computations, was shown to be vulnerable to SCA when the Fisher-Yates algorithm is used. In this paper, we propose a design of an SCA-secure version of the Fisher-Yates algorithm. By integrating the masking technique for modular reduction and Blakely's method for modular multiplication, we effectively remove the vulnerability in the division operation that led to side-channel leakage in the original version of the algorithm. We experimentally evaluate that the countermeasure is effective against SCA by implementing a correlation power analysis attack on an embedded neural network model implemented on ARM Cortex-M4. Compared to the original proposal, the memory overhead is $2\times$ the biggest layer of the network, while the time overhead varies from $4\%$ to $0.49\%$ for a layer with $100$ and $1000$ neurons, respectively.
An Immersive Virtual Reality Bimanual Telerobotic System With Haptic Feedback
Venue: arXiv cs.HC 2025
Year: 2025

In robotic bimanual teleoperation, multimodal sensory feedback plays a crucial role, providing operators with a more immersive operating experience, reducing cognitive burden, and improving operating efficiency. In this study, we develop an immersive bilateral isomorphic bimanual telerobotic system, which comprises dual arm and dual dexterous hands, with visual and haptic force feedback. To assess the performance of this system, we carried out a series of experiments and investigated the user's teleoperation experience. The results demonstrate that haptic force feedback enhances physical perception capabilities and complex task operating abilities. In addition, it compensates for visual perception deficiencies and reduces the operator's work burden. Consequently, our proposed system achieves more intuitive, realistic and immersive teleoperation, improves operating efficiency, and expands the complexity of tasks that robots can perform through teleoperation.
Recognizing Artistic Style of Archaeological Image Fragments Using Deep Style Extrapolation
Venue: arXiv cs.CV 2025
Year: 2025

Ancient artworks obtained in archaeological excavations usually suffer from a certain degree of fragmentation and physical degradation. Often, fragments of multiple artifacts from different periods or artistic styles could be found on the same site. With each fragment containing only partial information about its source, and pieces from different objects being mixed, categorizing broken artifacts based on their visual cues could be a challenging task, even for professionals. As classification is a common function of many machine learning models, the power of modern architectures can be harnessed for efficient and accurate fragment classification. In this work, we present a generalized deep-learning framework for predicting the artistic style of image fragments, achieving state-of-the-art results for pieces with varying styles and geometries.