OpenRaiser/Intern-Atlas huggingface dataset:
Contrastive MIM: A Contrastive Mutual Information Framework for Unified Generative and Discriminative Representation Learning
Venue: arXiv cs.LG 2025
Year: 2025

Learning representations that generalize well to unknown downstream tasks is a central challenge in representation learning. Existing approaches such as contrastive learning, self-supervised masking, and denoising auto-encoders address this challenge with varying trade-offs. In this paper, we introduce the {contrastive Mutual Information Machine} (cMIM), a probabilistic framework that augments the Mutual Information Machine (MIM) with a novel contrastive objective. While MIM maximizes mutual information between inputs and latent variables and encourages clustering of latent codes, its representations underperform on discriminative tasks compared to state-of-the-art alternatives. cMIM addresses this limitation by enforcing global discriminative structure while retaining MIM's generative strengths. We present two main contributions: (1) we propose cMIM, a contrastive extension of MIM that eliminates the need for positive data augmentation and is robust to batch size, unlike InfoNCE-based methods; (2) we introduce {informative embeddings}, a general technique for extracting enriched representations from encoder--decoder models that substantially improve discriminative performance without additional training, and which apply broadly beyond MIM. Empirical results demonstrate that cMIM consistently outperforms MIM and InfoNCE in classification and regression tasks, while preserving comparable reconstruction quality. These findings suggest that cMIM provides a unified framework for learning representations that are simultaneously effective for discriminative and generative applications.
Test-Time Modality Generalization for Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Generalizable medical image segmentation is essential for ensuring consistent performance across diverse unseen clinical settings. However, existing methods often overlook the capability to generalize effectively across arbitrary unseen modalities. In this paper, we introduce a novel Test-Time Modality Generalization (TTMG) framework, which comprises two core components: Modality-Aware Style Projection (MASP) and Modality-Sensitive Instance Whitening (MSIW), designed to enhance generalization in arbitrary unseen modality datasets. The MASP estimates the likelihood of a test instance belonging to each seen modality and maps it onto a distribution using modality-specific style bases, guiding its projection effectively. Furthermore, as high feature covariance hinders generalization to unseen modalities, the MSIW is applied during training to selectively suppress modality-sensitive information while retaining modality-invariant features. By integrating MASP and MSIW, the TTMG framework demonstrates robust generalization capabilities for medical image segmentation in unseen modalities a challenge that current methods have largely neglected. We evaluated TTMG alongside other domain generalization techniques across eleven datasets spanning four modalities (colonoscopy, ultrasound, dermoscopy, and radiology), consistently achieving superior segmentation performance across various modality combinations.
Joint Power Allocation and Phase Shift Design for Stacked Intelligent Metasurfaces-aided Cell-Free Massive MIMO Systems with MARL
Venue: arXiv cs.IT 2025
Year: 2025

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems offer high spectral efficiency (SE) through multiple distributed access points (APs). However, the large number of antennas increases power consumption. We propose incorporating stacked intelligent metasurfaces (SIM) into CF mMIMO systems as a cost-effective, energy-efficient solution. This paper focuses on optimizing the joint power allocation of APs and the phase shift of SIMs to maximize the sum SE. To address this complex problem, we introduce a fully distributed multi-agent reinforcement learning (MARL) algorithm. Our novel algorithm, the noisy value method with a recurrent policy in multi-agent policy optimization (NVR-MAPPO), enhances performance by encouraging diverse exploration under centralized training and decentralized execution. Simulations demonstrate that NVR-MAPPO significantly improves sum SE and robustness across various scenarios.
Unveiling Security Weaknesses in Autonomous Driving Systems: An In-Depth Empirical Study
Venue: arXiv cs.SE 2025
Year: 2025

The advent of Autonomous Driving Systems (ADS) has marked a significant shift towards intelligent transportation, with implications for public safety and traffic efficiency. While these systems integrate a variety of technologies and offer numerous benefits, their security is paramount, as vulnerabilities can have severe consequences for safety and trust. This study aims to systematically investigate potential security weaknesses in the codebases of prominent open-source ADS projects using CodeQL, a static code analysis tool. The goal is to identify common vulnerabilities, their distribution and persistence across versions to enhance the security of ADS. We selected three representative open-source ADS projects, Autoware, AirSim, and Apollo, based on their high GitHub star counts and Level 4 autonomous driving capabilities. Using CodeQL, we analyzed multiple versions of these projects to identify vulnerabilities, focusing on CWE categories such as CWE-190 (Integer Overflow or Wraparound) and CWE-20 (Improper Input Validation). We also tracked the lifecycle of these vulnerabilities across software versions. This approach allows us to systematically analyze vulnerabilities in projects, which has not been extensively explored in previous ADS research. Our analysis revealed that specific CWE categories, particularly CWE-190 (59.6%) and CWE-20 (16.1%), were prevalent across the selected ADS projects. These vulnerabilities often persisted for over six months, spanning multiple version iterations. The empirical assessment showed a direct link between the severity of these vulnerabilities and their tangible effects on ADS performance. These security issues among ADS still remain to be resolved. Our findings highlight the need for integrating static code analysis into ADS development to detect and mitigate common vulnerabilities.
Language-Informed Hyperspectral Image Synthesis for Imbalanced-Small Sample Classification via Semi-Supervised Conditional Diffusion Model
Venue: arXiv cs.CV 2025
Year: 2025

Data augmentation effectively addresses the imbalanced-small sample data (ISSD) problem in hyperspectral image classification (HSIC). While most methodologies extend features in the latent space, few leverage text-driven generation to create realistic and diverse samples. Recently, text-guided diffusion models have gained significant attention due to their ability to generate highly diverse and high-quality images based on text prompts in natural image synthesis. Motivated by this, this paper proposes Txt2HSI-LDM(VAE), a novel language-informed hyperspectral image synthesis method to address the ISSD in HSIC. The proposed approach uses a denoising diffusion model, which iteratively removes Gaussian noise to generate hyperspectral samples conditioned on textual descriptions. First, to address the high-dimensionality of hyperspectral data, a universal variational autoencoder (VAE) is designed to map the data into a low-dimensional latent space, which provides stable features and reduces the inference complexity of diffusion model. Second, a semi-supervised diffusion model is designed to fully take advantage of unlabeled data. Random polygon spatial clipping (RPSC) and uncertainty estimation of latent feature (LF-UE) are used to simulate the varying degrees of mixing. Third, the VAE decodes HSI from latent space generated by the diffusion model with the language conditions as input. In our experiments, we fully evaluate synthetic samples' effectiveness from statistical characteristics and data distribution in 2D-PCA space. Additionally, visual-linguistic cross-attention is visualized on the pixel level to prove that our proposed model can capture the spatial layout and geometry of the generated data. Experiments demonstrate that the performance of the proposed Txt2HSI-LDM(VAE) surpasses the classical backbone models, state-of-the-art CNNs, and semi-supervised methods.
Weakly Supervised Segmentation Framework for Thyroid Nodule Based on High-confidence Labels and High-rationality Losses
Venue: arXiv cs.CV 2025
Year: 2025

Weakly supervised segmentation methods can delineate thyroid nodules in ultrasound images efficiently using training data with coarse labels, but suffer from: 1) low-confidence pseudo-labels that follow topological priors, introducing significant label noise, and 2) low-rationality loss functions that rigidly compare segmentation with labels, ignoring discriminative information for nodules with diverse and complex shapes. To solve these issues, we clarify the objective and references for weakly supervised ultrasound image segmentation, presenting a framework with high-confidence pseudo-labels to represent topological and anatomical information and high-rationality losses to capture multi-level discriminative features. Specifically, we fuse geometric transformations of four-point annotations and MedSAM model results prompted by specific annotations to generate high-confidence box, foreground, and background labels. Our high-rationality learning strategy includes: 1) Alignment loss measuring spatial consistency between segmentation and box label, and topological continuity within the foreground label, guiding the network to perceive nodule location; 2) Contrastive loss pulling features from labeled foreground regions while pushing features from labeled foreground and background regions, guiding the network to learn nodule and background feature distribution; 3) Prototype correlation loss measuring consistency between correlation maps derived by comparing features with foreground and background prototypes, refining uncertain regions to accurate nodule edges. Experimental results show that our method achieves state-of-the-art performance on the TN3K and DDTI datasets. The code is available at https://github.com/bluehenglee/MLI-MSC.
Accurate Pose Estimation for Flight Platforms based on Divergent Multi-Aperture Imaging System
Venue: arXiv cs.CV 2025
Year: 2025

Vision-based pose estimation plays a crucial role in the autonomous navigation of flight platforms. However, the field of view and spatial resolution of the camera limit pose estimation accuracy. This paper designs a divergent multi-aperture imaging system (DMAIS), equivalent to a single imaging system to achieve simultaneous observation of a large field of view and high spatial resolution. The DMAIS overcomes traditional observation limitations, allowing accurate pose estimation for the flight platform. {Before conducting pose estimation, the DMAIS must be calibrated. To this end we propose a calibration method for DMAIS based on the 3D calibration field.} The calibration process determines the imaging parameters of the DMAIS, which allows us to model DMAIS as a generalized camera. Subsequently, a new algorithm for accurately determining the pose of flight platform is introduced. We transform the absolute pose estimation problem into a nonlinear minimization problem. New optimality conditions are established for solving this problem based on Lagrange multipliers. Finally, real calibration experiments show the effectiveness and accuracy of the proposed method. Results from real flight experiments validate the system's ability to achieve centimeter-level positioning accuracy and arc-minute-level orientation accuracy.
Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) have become the backbone of modern natural language processing but pose privacy concerns about leaking sensitive training data. Membership inference attacks (MIAs), which aim to infer whether a sample is included in a model's training dataset, can serve as a foundation for broader privacy threats. Existing defenses designed for traditional classification models do not account for the sequential nature of text data. As a result, they either require significant computational resources or fail to effectively mitigate privacy risks in LLMs. In this work, we propose \methodname, a lightweight yet effective empirical privacy defense for protecting training data of language models by leveraging token-specific characteristics. By analyzing token dynamics during training, we propose a token selection strategy that categorizes tokens into hard tokens for learning and memorized tokens for unlearning. Subsequently, our training-phase defense optimizes a novel dual-purpose token-level loss to achieve a Pareto-optimal balance between utility and privacy. Extensive experiments demonstrate that our approach not only provides strong protection against MIAs but also improves language modeling performance by around 10\% across various LLM architectures and datasets compared to the baselines.
Speculative Decoding and Beyond: An In-Depth Survey of Techniques
Venue: arXiv cs.CL 2025
Year: 2025

Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models, particularly for real-time applications. While traditional optimization approaches like pruning and quantization often compromise model quality, recent advances in generation-refinement frameworks demonstrate that this trade-off can be significantly mitigated. This survey presents a comprehensive taxonomy of generation-refinement frameworks, analyzing methods across autoregressive sequence tasks. We categorize methods based on their generation strategies (from simple n-gram prediction to sophisticated draft models) and refinement mechanisms (including single-pass verification and iterative approaches). Through systematic analysis of both algorithmic innovations and system-level implementations, we examine deployment strategies across computing environments and explore applications spanning text, images, and speech generation. This systematic examination of both theoretical frameworks and practical implementations provides a foundation for future research in efficient autoregressive decoding.
XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs
Venue: arXiv cs.CL 2025
Year: 2025

We introduce XCOMPS in this work, a multilingual conceptual minimal pair dataset covering 17 languages. Using this dataset, we evaluate LLMs' multilingual conceptual understanding through metalinguistic prompting, direct probability measurement, and neurolinguistic probing. By comparing base, instruction-tuned, and knowledge-distilled models, we find that: 1) LLMs exhibit weaker conceptual understanding for low-resource languages, and accuracy varies across languages despite being tested on the same concept sets. 2) LLMs excel at distinguishing concept-property pairs that are visibly different but exhibit a marked performance drop when negative pairs share subtle semantic similarities. 3) Instruction tuning improves performance in concept understanding but does not enhance internal competence; knowledge distillation can enhance internal competence in conceptual understanding for low-resource languages with limited gains in explicit task performance. 4) More morphologically complex languages yield lower concept understanding scores and require deeper layers for conceptual reasoning.
Automatic Temporal Segmentation for Post-Stroke Rehabilitation: A Keypoint Detection and Temporal Segmentation Approach for Small Datasets
Venue: arXiv cs.CV 2025
Year: 2025

Rehabilitation is essential and critical for post-stroke patients, addressing both physical and cognitive aspects. Stroke predominantly affects older adults, with 75% of cases occurring in individuals aged 65 and older, underscoring the urgent need for tailored rehabilitation strategies in aging populations. Despite the critical role therapists play in evaluating rehabilitation progress and ensuring the effectiveness of treatment, current assessment methods can often be subjective, inconsistent, and time-consuming, leading to delays in adjusting therapy protocols. This study aims to address these challenges by providing a solution for consistent and timely analysis. Specifically, we perform temporal segmentation of video recordings to capture detailed activities during stroke patients' rehabilitation. The main application scenario motivating this study is the clinical assessment of daily tabletop object interactions, which are crucial for post-stroke physical rehabilitation. To achieve this, we present a framework that leverages the biomechanics of movement during therapy sessions. Our solution divides the process into two main tasks: 2D keypoint detection to track patients' physical movements, and 1D time-series temporal segmentation to analyze these movements over time. This dual approach enables automated labeling with only a limited set of real-world data, addressing the challenges of variability in patient movements and limited dataset availability. By tackling these issues, our method shows strong potential for practical deployment in physical therapy settings, enhancing the speed and accuracy of rehabilitation assessments.
The erasure of intensive livestock farming in text-to-image generative AI
Venue: arXiv cs.CY 2025
Year: 2025

Generative AI (e.g., ChatGPT) is increasingly integrated into people's daily lives. While it is known that AI perpetuates biases against marginalized human groups, their impact on non-human animals remains understudied. We found that ChatGPT's text-to-image model (DALL-E 3) introduces a strong bias toward romanticizing livestock farming as dairy cows on pasture and pigs rooting in mud. This bias remained when we requested realistic depictions and was only mitigated when the automatic prompt revision was inhibited. Most farmed animal in industrialized countries are reared indoors with limited space per animal, which fail to resonate with societal values. Inhibiting prompt revision resulted in images that more closely reflected modern farming practices; for example, cows housed indoors accessing feed through metal headlocks, and pigs behind metal railings on concrete floors in indoor facilities. While OpenAI introduced prompt revision to mitigate bias, in the case of farmed animal production systems, it paradoxically introduces a strong bias towards unrealistic farming practices.
Advancements in Natural Language Processing for Automatic Text Summarization
Venue: arXiv cs.CL 2025
Year: 2025

The substantial growth of textual content in diverse domains and platforms has led to a considerable need for Automatic Text Summarization (ATS) techniques that aid in the process of text analysis. The effectiveness of text summarization models has been significantly enhanced in a variety of technical domains because of advancements in Natural Language Processing (NLP) and Deep Learning (DL). Despite this, the process of summarizing textual information continues to be significantly constrained by the intricate writing styles of a variety of texts, which involve a range of technical complexities. Text summarization techniques can be broadly categorized into two main types: abstractive summarization and extractive summarization. Extractive summarization involves directly extracting sentences, phrases, or segments of text from the content without making any changes. On the other hand, abstractive summarization is achieved by reconstructing the sentences, phrases, or segments from the original text using linguistic analysis. Through this study, a linguistically diverse categorizations of text summarization approaches have been addressed in a constructive manner. In this paper, the authors explored existing hybrid techniques that have employed both extractive and abstractive methodologies. In addition, the pros and cons of various approaches discussed in the literature are also investigated. Furthermore, the authors conducted a comparative analysis on different techniques and matrices to evaluate the generated summaries using language generation models. This survey endeavors to provide a comprehensive overview of ATS by presenting the progression of language processing regarding this task through a breakdown of diverse systems and architectures accompanied by technical and mathematical explanations of their operations.
Fair and Actionable Causal Prescription Ruleset
Venue: arXiv cs.DB 2025
Year: 2025

Prescriptions, or actionable recommendations, are commonly generated across various fields to influence key outcomes such as improving public health, enhancing economic policies, or increasing business efficiency. While traditional association-based methods may identify correlations, they often fail to reveal the underlying causal factors needed for informed decision-making. On the other hand, in decision-making for tasks with significant societal or economic impact, it is crucial to provide recommendations that are justifiable and equitable in terms of the outcome for both the protected and non-protected groups. Motivated by these two goals, this paper introduces a fairness-aware framework leveraging causal reasoning for generating a set of actionable prescription rules (ruleset) toward betterment of an outcome while preventing exacerbating inequalities for protected groups. By considering group and individual fairness metrics from the literature, we ensure that both protected and non-protected groups benefit from these recommendations, providing a balanced and equitable approach to decision-making. We employ efficient optimizations to explore the vast and complex search space considering both fairness and coverage of the ruleset. Empirical evaluation and case study on real-world datasets demonstrates the utility of our framework for different use cases.
Revisit the Stability of Vanilla Federated Learning Under Diverse Conditions
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) is a distributed machine learning paradigm enabling collaborative model training across decentralized clients while preserving data privacy. In this paper, we revisit the stability of the vanilla FedAvg algorithm under diverse conditions. Despite its conceptual simplicity, FedAvg exhibits remarkably stable performance compared to more advanced FL techniques. Our experiments assess the performance of various FL methods on blood cell and skin lesion classification tasks using Vision Transformer (ViT). Additionally, we evaluate the impact of different representative classification models and analyze sensitivity to hyperparameter variations. The results consistently demonstrate that, regardless of dataset, classification model employed, or hyperparameter settings, FedAvg maintains robust performance. Given its stability, robust performance without the need for extensive hyperparameter tuning, FedAvg is a safe and efficient choice for FL deployments in resource-constrained hospitals handling medical data. These findings underscore the enduring value of the vanilla FedAvg approach as a trusted baseline for clinical practice.