OpenRaiser/Intern-Atlas huggingface dataset:
A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Mamba, with its selective State Space Models (SSMs), offers a more computationally efficient solution than Transformers for long-range dependency modeling. However, there is still a debate about its effectiveness in high-resolution 3D medical image segmentation. In this study, we present a comprehensive investigation into Mamba's capabilities in 3D medical image segmentation by tackling three pivotal questions: Can Mamba replace Transformers? Can it elevate multi-scale representation learning? Is complex scanning necessary to unlock its full potential? We evaluate Mamba's performance across three large public benchmarks-AMOS, TotalSegmentator, and BraTS. Our findings reveal that UlikeMamba, a U-shape Mamba-based network, consistently surpasses UlikeTrans, a U-shape Transformer-based network, particularly when enhanced with custom-designed 3D depthwise convolutions, boosting accuracy and computational efficiency. Further, our proposed multi-scale Mamba block demonstrates superior performance in capturing both fine-grained details and global context, especially in complex segmentation tasks, surpassing Transformer-based counterparts. We also critically assess complex scanning strategies, finding that simpler methods often suffice, while our Tri-scan approach delivers notable advantages in the most challenging scenarios. By integrating these advancements, we introduce a new network for 3D medical image segmentation, positioning Mamba as a transformative force that outperforms leading models such as nnUNet, CoTr, and U-Mamba, offering competitive accuracy with superior computational efficiency. This study provides key insights into Mamba's unique advantages, paving the way for more efficient and accurate approaches to 3D medical imaging.
Membership Inference Attacks on Large-Scale Models: A Survey
Venue: arXiv cs.LG 2025
Year: 2025

As large-scale models such as Large Language Models (LLMs) and Large Multimodal Models (LMMs) see increasing deployment, their privacy risks remain underexplored. Membership Inference Attacks (MIAs), which reveal whether a data point was used in training the target model, are an important technique for exposing or assessing privacy risks and have been shown to be effective across diverse machine learning algorithms. However, despite extensive studies on MIAs in classic models, there remains a lack of systematic surveys addressing their effectiveness and limitations in large-scale models. To address this gap, we provide the first comprehensive review of MIAs targeting LLMs and LMMs, analyzing attacks by model type, adversarial knowledge, and strategy. Unlike prior surveys, we further examine MIAs across multiple stages of the model pipeline, including pre-training, fine-tuning, alignment, and Retrieval-Augmented Generation (RAG). Finally, we identify open challenges and propose future research directions for strengthening privacy resilience in large-scale models.
ImageSet2Text: Describing Sets of Images through Text
Venue: arXiv cs.CV 2025
Year: 2025

In the era of large-scale visual data, understanding collections of images is a challenging yet important task. To this end, we introduce ImageSet2Text, a novel method to automatically generate natural language descriptions of image sets. Based on large language models, visual-question answering chains, an external lexical graph, and CLIP-based verification, ImageSet2Text iteratively extracts key concepts from image subsets and organizes them into a structured concept graph. We conduct extensive experiments evaluating the quality of the generated descriptions in terms of accuracy, completeness, and user satisfaction. We also examine the method's behavior through ablation studies, scalability assessments, and failure analyses. Results demonstrate that ImageSet2Text combines data-driven AI and symbolic representations to reliably summarize large image collections for a wide range of applications.
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic Reconstruction
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal learning combining pathology images and genomic sequences enhances cancer survival analysis but faces clinical implementation barriers due to limited access to genomic sequencing in under-resourced regions. To enable survival prediction using only whole-slide images (WSI), we propose the Visual-Genomic Answering-Guided Transformer (VGAT), a framework integrating Visual Question Answering (VQA) techniques for genomic modality reconstruction. By adapting VQA's text feature extraction approach, we derive stable genomic representations that circumvent dimensionality challenges in raw genomic data. Simultaneously, a cluster-based visual prompt module selectively enhances discriminative WSI patches, addressing noise from unfiltered image regions. Evaluated across five TCGA datasets, VGAT outperforms existing WSI-only methods, demonstrating the viability of genomic-informed inference without sequencing. This approach bridges multimodal research and clinical feasibility in resource-constrained settings. The code link is https://github.com/CZZZZZZZZZZZZZZZZZ/VGAT.
Integrating Prefetcher Selection with Dynamic Request Allocation Improves Prefetching Efficiency
Venue: arXiv cs.AR 2025
Year: 2025

Hardware prefetching plays a critical role in hiding the off-chip DRAM latency. The complexity of applications results in a wide variety of memory access patterns, prompting the development of numerous cache-prefetching algorithms. Consequently, commercial processors often employ a hybrid of these algorithms to enhance the overall prefetching performance. Nonetheless, since these prefetchers share hardware resources, conflicts arising from competing prefetching requests can negate the benefits of hardware prefetching. Under such circumstances, several prefetcher selection algorithms have been proposed to mitigate conflicts between prefetchers. However, these prior solutions suffer from two limitations. First, the input demand request allocation is inaccurate. Second, the prefetcher selection criteria are coarse-grained. In this paper, we address both limitations by introducing an efficient and widely applicable prefetcher selection algorithm--Alecto, which tailors the demand requests for each prefetcher. Every demand request is first sent to Alecto to identify suitable prefetchers before being routed to prefetchers for training and prefetching. Our analysis shows that Alecto is adept at not only harmonizing prefetching accuracy, coverage, and timeliness but also significantly enhancing the utilization of the prefetcher table, which is vital for temporal prefetching. Alecto outperforms the state-of-the-art RL-based prefetcher selection algorithm--Bandit by 2.76% in single-core, and 7.56% in eight-core. For memory-intensive benchmarks, Alecto outperforms Bandit by 5.25%. Alecto consistently delivers state-of-the-art performance in scheduling various types of cache prefetchers. In addition to the performance improvement, Alecto can reduce the energy consumption associated with accessing the prefetchers' table by 48%, while only adding less than 1 KB of storage overhead.
Quantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM
Venue: arXiv cs.LG 2025
Year: 2025

Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms are ambiguous or common across multiple conditions. In this work, we move beyond correlation to investigate the causal influence of key symptoms-specifically "chest pain" on diagnostic predictions. Leveraging the CausaLM framework, we generate counterfactual text representations in which target concepts are effectively "forgotten" enabling a principled estimation of the causal effect of that concept on a model's predicted disease distribution. By employing Textual Representation-based Average Treatment Effect (TReATE), we quantify how the presence or absence of a symptom shapes the model's diagnostic outcomes, and contrast these findings against correlation-based baselines such as CONEXP. Our results offer deeper insight into the decision-making behavior of clinical NLP models and have the potential to inform more trustworthy, interpretable, and causally-grounded decision support tools in medical practice.
Estimation of the Acoustic Field in a Uniform Duct with Mean Flow using Neural Networks
Venue: arXiv cs.CE 2025
Year: 2025

The study of sound propagation in a uniform duct having a mean flow has many applications, such as in the design of gas turbines, heating, ventilation and air conditioning ducts, automotive intake and exhaust systems, and in the modeling of speech. In this paper, the convective effects of the mean flow on the plane wave acoustic field inside a uniform duct were studied using artificial neural networks. The governing differential equation and the associated boundary conditions form a constrained optimization problem. It is converted to an unconstrained optimization problem and solved by approximating the acoustic field variable to a neural network. The complex-valued acoustic pressure and particle velocity were predicted at different frequencies, and validated against the analytical solution and the finite element models. The effect of the mean flow is studied in terms of the acoustic impedance. A closed-form expression that describes the influence of various factors on the acoustic field is derived.
Anvil: A General-Purpose Timing-Safe Hardware Description Language
Venue: arXiv cs.AR 2025
Year: 2025

Expressing hardware designs using hardware description languages (HDLs) routinely involves using stateless signals whose values change according to their underlying registers. Unintended behaviours can arise when the stored values in these underlying registers are mutated while their dependent signals are expected to remain constant across multiple cycles. Such timing hazards are common because, with a few exceptions, existing HDLs lack abstractions for values that remain unchanged over multiple clock cycles, delegating this responsibility to hardware designers. Designers must then carefully decide whether a value should remain unchanged, sometimes even across hardware modules. This paper proposes Anvil, an HDL which statically prevents timing hazards with a novel type system. Anvil is the only HDL we know of that guarantees timing safety, i.e., absence of timing hazards, without sacrificing expressiveness for cycle-level timing control or dynamic timing behaviours. Unlike many HLS languages that abstract away the differences between registers and signals, Anvil's type system exposes them fully while capturing the timing relationships between register value mutations and signal usages to enforce timing safety. This, in turn, enables safe composition of communicating hardware modules by static enforcement of timing contracts that encode timing constraints on shared signals. Such timing contracts can be specified parametric on abstract time points that can vary during run-time, allowing the type system to statically express dynamic timing behaviour. We have implemented Anvil and successfully used it to implement key timing-sensitive modules, comparing them against open-source SystemVerilog counterparts to demonstrate the practicality and expressiveness of the generated hardware.
TrackThinkDashboard: Understanding Student Self-Regulated Learning in Programming Study
Venue: arXiv cs.HC 2025
Year: 2025

In programming education, fostering self-regulated learning (SRL) skills is essential for both students and teachers. This paper introduces TrackThinkDashboard, an application designed to visualize the learning workflow by integrating web browsing and programming logs into one unified view. The system aims to (1) help students monitor and reflect on their problem-solving processes, identify knowledge gaps, and cultivate effective SRL strategies; and (2) enable teachers to identify at-risk learners more effectively and provide targeted, data-driven guidance. We conducted a study with 33 participants (32 male, 1 female) from Japanese universities, including individuals with and without prior programming experience, to explore differences in web browsing and coding patterns. The dashboards revealed multiple learning approaches, such as trial-and-error and trial-and-search methods, and highlighted how domain knowledge influenced the overall activity flow. We discuss how this visualization tool can be used continuously or in one-off experiments, consider associated privacy implications, and explore opportunities for expanding data sources to gain richer behavioral insights.
The $g$-good-neighbor diagnosability of product networks under the PMC model
Venue: arXiv cs.DM 2025
Year: 2025

The concept of neighbor connectivity originated from the assessment of the subversion of espionage networks caused by underground resistance movements, and it has now been applied to measure the disruption of networks caused by cascading failures through neighbors. In this paper, we give two necessary and sufficient conditions of the existance of $g$-good-neighbor diagnosability. We introduce a new concept called $g$-good neighbor cut-component number (gc number for short), which has close relation with $g$-good-neighbor diagnosability. Sharp lower and upper bounds of the gc number of general graphs in terms of the $g$-good neighbor connectivity is given, which provides a formula to compute the $g$-good-neighbor diagnosability for general graphs (therefore for Cartesian product graphs). As their applications, we get the exact values or bounds for the gc numbers and $g$-good-neighbor diagnosability of grid, torus networks and generalized cubes.
SparSamp: Efficient Provably Secure Steganography Based on Sparse Sampling
Venue: arXiv cs.CR 2025
Year: 2025

Steganography embeds confidential data within seemingly innocuous communications. Provable security in steganography, a long-sought goal, has become feasible with deep generative models. However, existing methods face a critical trade-off between security and efficiency. This paper introduces SparSamp, an efficient provably secure steganography method based on sparse sampling. SparSamp embeds messages by combining them with pseudo-random numbers to obtain message-derived random numbers for sampling. It enhances extraction accuracy and embedding capacity by increasing the sampling intervals and making the sampling process sparse. SparSamp preserves the original probability distribution of the generative model, thus ensuring security. It introduces only $O(1)$ additional complexity per sampling step, enabling the fastest embedding speed without compromising generation speed. SparSamp is designed to be plug-and-play; message embedding can be achieved by simply replacing the sampling component of an existing generative model with SparSamp. We implemented SparSamp in text, image, and audio generation models. It can achieve embedding speeds of up to 755 bits/second with GPT-2, 5046 bits/second with DDPM, and 9,223 bits/second with WaveRNN.
RoboFlamingo-Plus: Fusion of Depth and RGB Perception with Vision-Language Models for Enhanced Robotic Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

As robotic technologies advancing towards more complex multimodal interactions and manipulation tasks, the integration of advanced Vision-Language Models (VLMs) has become a key driver in the field. Despite progress with current methods, challenges persist in fusing depth and RGB information within 3D environments and executing tasks guided by linguistic instructions. In response to these challenges, we have enhanced the existing RoboFlamingo framework by introducing RoboFlamingo-Plus, which incorporates depth data into VLMs to significantly improve robotic manipulation performance. Our research achieves a nuanced fusion of RGB and depth information by integrating a pre-trained Vision Transformer (ViT) with a resampling technique, closely aligning this combined data with linguistic cues for superior multimodal understanding. The novelty of RoboFlamingo-Plus lies in its adaptation of inputs for depth data processing, leveraging a pre-trained resampler for depth feature extraction, and employing cross-attention mechanisms for optimal feature integration. These improvements allow RoboFlamingo-Plus to not only deeply understand 3D environments but also easily perform complex, language-guided tasks in challenging settings. Experimental results show that RoboFlamingo-Plus boosts robotic manipulation by 10-20% over current methods, marking a significant advancement. Codes and model weights are public at RoboFlamingo-Plus.
Towards Imperceptible Adversarial Attacks for Time Series Classification with Local Perturbations and Frequency Analysis
Venue: arXiv cs.CR 2025
Year: 2025

Adversarial attacks in time series classification (TSC) models have recently gained attention due to their potential to compromise model robustness. Imperceptibility is crucial, as adversarial examples detected by the human vision system (HVS) can render attacks ineffective. Many existing methods fail to produce high-quality imperceptible examples, often generating perturbations with more perceptible low-frequency components, like square waves, and global perturbations that reduce stealthiness. This paper aims to improve the imperceptibility of adversarial attacks on TSC models by addressing frequency components and time series locality. We propose the Shapelet-based Frequency-domain Attack (SFAttack), which uses local perturbations focused on time series shapelets to enhance discriminative information and stealthiness. Additionally, we introduce a low-frequency constraint to confine perturbations to high-frequency components, enhancing imperceptibility.
FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in Large Language Models (LLMs) have significantly enhanced interactions between users and models. These advancements concurrently underscore the need for rigorous safety evaluations due to the manifestation of social biases, which can lead to harmful societal impacts. Despite these concerns, existing benchmarks may overlook the intrinsic weaknesses of LLMs, which can generate biased responses even with simple adversarial instructions. To address this critical gap, we introduce a new benchmark, Fairness Benchmark in LLM under Extreme Scenarios (FLEX), designed to test whether LLMs can sustain fairness even when exposed to prompts constructed to induce bias. To thoroughly evaluate the robustness of LLMs, we integrate prompts that amplify potential biases into the fairness assessment. Comparative experiments between FLEX and existing benchmarks demonstrate that traditional evaluations may underestimate the inherent risks in models. This highlights the need for more stringent LLM evaluation benchmarks to guarantee safety and fairness.
Multi-agent Application System in Office Collaboration Scenarios
Venue: arXiv cs.AI 2025
Year: 2025

This paper introduces a multi-agent application system designed to enhance office collaboration efficiency and work quality. The system integrates artificial intelligence, machine learning, and natural language processing technologies, achieving functionalities such as task allocation, progress monitoring, and information sharing. The agents within the system are capable of providing personalized collaboration support based on team members' needs and incorporate data analysis tools to improve decision-making quality. The paper also proposes an intelligent agent architecture that separates Plan and Solver, and through techniques such as multi-turn query rewriting and business tool retrieval, it enhances the agent's multi-intent and multi-turn dialogue capabilities. Furthermore, the paper details the design of tools and multi-turn dialogue in the context of office collaboration scenarios, and validates the system's effectiveness through experiments and evaluations. Ultimately, the system has demonstrated outstanding performance in real business applications, particularly in query understanding, task planning, and tool calling. Looking forward, the system is expected to play a more significant role in addressing complex interaction issues within dynamic environments and large-scale multi-agent systems.
Video Anomaly Detection with Contours -- A Study
Venue: arXiv cs.CV 2025
Year: 2025

In Pose-based Video Anomaly Detection prior art is rooted on the assumption that abnormal events can be mostly regarded as a result of uncommon human behavior. Opposed to utilizing skeleton representations of humans, however, we investigate the potential of learning recurrent motion patterns of normal human behavior using 2D contours. Keeping all advantages of pose-based methods, such as increased object anonymization, the shift from human skeletons to contours is hypothesized to leave the opportunity to cover more object categories open for future research. We propose formulating the problem as a regression and a classification task, and additionally explore two distinct data representation techniques for contours. To further reduce the computational complexity of Pose-based Video Anomaly Detection solutions, all methods in this study are based on shallow Neural Networks from the field of Deep Learning, and evaluated on the three most prominent benchmark datasets within Video Anomaly Detection and their human-related counterparts, totaling six datasets. Our results indicate that this novel perspective on Pose-based Video Anomaly Detection marks a promising direction for future research.
Boosting the Transferability of Audio Adversarial Examples with Acoustic Representation Optimization
Venue: arXiv cs.SD 2025
Year: 2025

With the widespread application of automatic speech recognition (ASR) systems, their vulnerability to adversarial attacks has been extensively studied. However, most existing adversarial examples are generated on specific individual models, resulting in a lack of transferability. In real-world scenarios, attackers often cannot access detailed information about the target model, making query-based attacks unfeasible. To address this challenge, we propose a technique called Acoustic Representation Optimization that aligns adversarial perturbations with low-level acoustic characteristics derived from speech representation models. Rather than relying on model-specific, higher-layer abstractions, our approach leverages fundamental acoustic representations that remain consistent across diverse ASR architectures. By enforcing an acoustic representation loss to guide perturbations toward these robust, lower-level representations, we enhance the cross-model transferability of adversarial examples without degrading audio quality. Our method is plug-and-play and can be integrated with any existing attack methods. We evaluate our approach on three modern ASR models, and the experimental results demonstrate that our method significantly improves the transferability of adversarial examples generated by previous methods while preserving the audio quality.