OpenRaiser/Intern-Atlas huggingface dataset:
OpenLVLM-MIA: A Controlled Benchmark Revealing the Limits of Membership Inference Attacks on Large Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

OpenLVLM-MIA is a new benchmark that highlights fundamental challenges in evaluating membership inference attacks (MIA) against large vision-language models (LVLMs). While prior work has reported high attack success rates, our analysis suggests that these results often arise from detecting distributional bias introduced during dataset construction rather than from identifying true membership status. To address this issue, we introduce a controlled benchmark of 6{,}000 images where the distributions of member and non-member samples are carefully balanced, and ground-truth membership labels are provided across three distinct training stages. Experiments using OpenLVLM-MIA demonstrated that the performance of state-of-the-art MIA methods approached chance-level. OpenLVLM-MIA, designed to be transparent and unbiased benchmark, clarifies certain limitations of MIA research on LVLMs and provides a solid foundation for developing stronger privacy-preserving techniques.
RL makes MLLMs see better than SFT
Venue: arXiv cs.CV 2025
Year: 2025

A dominant assumption in Multimodal Language Model (MLLM) research is that its performance is largely inherited from the LLM backbone, given its immense parameter scale and remarkable capabilities. This has created a void in the understanding of the vision encoder, which determines how MLLMs perceive images. The recent shift in MLLM training paradigms, from Supervised Finetuning (SFT) to Reinforcement Learning (RL), magnifies this oversight-namely, the significant lack of analysis on how such training reshapes the vision encoder as well as the MLLM. To address this, we first investigate the impact of training strategies on MLLMs, where RL shows a clear advantage over SFT in strongly vision-related VQA benchmarks. Motivated by this, we conduct a critical yet under-explored analysis of the vision encoder of MLLMs through diverse and in-depth experiments, ranging from ImageNet classification and segmentation to gradient visualization. Our results demonstrate that MLLM's post-training strategy (i.e., SFT or RL) not only leads to distinct outcomes on MLLM downstream tasks, but also fundamentally reshapes MLLM's underlying visual representations. Specifically, the key finding of our study is that RL produces stronger and precisely localized visual representations compared to SFT, boosting the ability of the vision encoder for MLLM. We then reframe our findings into a simple recipe for building strong vision encoders for MLLMs, Preference-Instructed Vision OpTimization (PIVOT). When integrated into MLLMs, a PIVOT-trained vision encoder outperforms even larger and more heavily-trained counterparts, despite requiring less than 1% of the computational cost of standard vision pretraining. This result opens an effective and efficient path for advancing the vision backbones of MLLMs. Project page available at https://june-page.github.io/pivot/
Navigating through the hidden embedding space: steering LLMs to improve mental health assessment
Venue: arXiv cs.CL 2025
Year: 2025

The rapid evolution of Large Language Models (LLMs) is transforming AI, opening new opportunities in sensitive and high-impact areas such as Mental Health (MH). Yet, despite these advancements, recent evidence reveals that smaller-scale models still struggle to deliver optimal performance in domain-specific applications. In this study, we present a cost-efficient yet powerful approach to improve MH assessment capabilities of an LLM, without relying on any computationally intensive techniques. Our lightweight method consists of a linear transformation applied to a specific layer's activations, leveraging steering vectors to guide the model's output. Remarkably, this intervention enables the model to achieve improved results across two distinct tasks: (1) identifying whether a Reddit post is useful for detecting the presence or absence of depressive symptoms (relevance prediction task), and (2) completing a standardized psychological screening questionnaire for depression based on users' Reddit post history (questionnaire completion task). Results highlight the untapped potential of steering mechanisms as computationally efficient tools for LLMs' MH domain adaptation.
The Strongly Stable Roommates Problem and Linear Programming
Venue: arXiv cs.GT 2025
Year: 2025

The stable roommates problem is a non-bipartite version of the stable matching problem in a bipartite graph. In this paper, we consider the stable roommates problem with ties. In particular, we focus on strong stability, which is one of the main stability concepts in the stable roommates problem with ties. We propose a new polynomial-time algorithm for the problem of checking the existence of a strongly stable matching in the stable roommates problem with ties. More concretely, we extend the linear programming approach of Abeledo and Blum to the stable roommates problem with strict preferences to our problem.
LightGlueStick: a Fast and Robust Glue for Joint Point-Line Matching
Venue: arXiv cs.CV 2025
Year: 2025

Lines and points are complementary local features, whose combination has proven effective for applications such as SLAM and Structure-from-Motion. The backbone of these pipelines are the local feature matchers, establishing correspondences across images. Traditionally, point and line matching have been treated as independent tasks. Recently, GlueStick proposed a GNN-based network that simultaneously operates on points and lines to establish matches. While running a single joint matching reduced the overall computational complexity, the heavy architecture prevented real-time applications or deployment to edge devices. Inspired by recent progress in point matching, we propose LightGlueStick, a lightweight matcher for points and line segments. The key novel component in our architecture is the Attentional Line Message Passing (ALMP), which explicitly exposes the connectivity of the lines to the network, allowing for efficient communication between nodes. In thorough experiments we show that LightGlueStick establishes a new state-of-the-art across different benchmarks. The code is available at https://github.com/aubingazhib/LightGlueStick.
HGC-Avatar: Hierarchical Gaussian Compression for Streamable Dynamic 3D Avatars
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in 3D Gaussian Splatting (3DGS) have enabled fast, photorealistic rendering of dynamic 3D scenes, showing strong potential in immersive communication. However, in digital human encoding and transmission, the compression methods based on general 3DGS representations are limited by the lack of human priors, resulting in suboptimal bitrate efficiency and reconstruction quality at the decoder side, which hinders their application in streamable 3D avatar systems. We propose HGC-Avatar, a novel Hierarchical Gaussian Compression framework designed for efficient transmission and high-quality rendering of dynamic avatars. Our method disentangles the Gaussian representation into a structural layer, which maps poses to Gaussians via a StyleUNet-based generator, and a motion layer, which leverages the SMPL-X model to represent temporal pose variations compactly and semantically. This hierarchical design supports layer-wise compression, progressive decoding, and controllable rendering from diverse pose inputs such as video sequences or text. Since people are most concerned with facial realism, we incorporate a facial attention mechanism during StyleUNet training to preserve identity and expression details under low-bitrate constraints. Experimental results demonstrate that HGC-Avatar provides a streamable solution for rapid 3D avatar rendering, while significantly outperforming prior methods in both visual quality and compression efficiency.
Declarative Techniques for NL Queries over Heterogeneous Data
Venue: arXiv cs.DB 2025
Year: 2025

In many industrial settings, users wish to ask questions in natural language, the answers to which require assembling information from diverse structured data sources. With the advent of Large Language Models (LLMs), applications can now translate natural language questions into a set of API calls or database calls, execute them, and combine the results into an appropriate natural language response. However, these applications remain impractical in realistic industrial settings because they do not cope with the data source heterogeneity that typifies such environments. In this work, we simulate the heterogeneity of real industry settings by introducing two extensions of the popular Spider benchmark dataset that require a combination of database and API calls. Then, we introduce a declarative approach to handling such data heterogeneity and demonstrate that it copes with data source heterogeneity significantly better than state-of-the-art LLM-based agentic or imperative code generation systems. Our augmented benchmarks are available to the research community.
Shifting 'AI Policy' Preprints and Citation Trends in the U.S., U.K and E.U., and South Korea (2015-2024)
Venue: arXiv cs.DL 2025
Year: 2025

This study of literature focusing on 'AI Policy' over the past decade, found that citations of preprints, publications on platforms such as arXiv, have increased from five percent to forty percent across three major regions: the U.S., U.K. & E.U., and South Korea. We compare regional responses of preprint citations across the global disruptions of COVID-19 and the release of ChatGPT. We discuss driving factors and risks of preprint normalization, which follows the trend in computer science.
Interpreting the Dimensions of Speaker Embedding Space
Venue: arXiv cs.SD 2025
Year: 2025

Speaker embeddings are widely used in speaker verification systems and other applications where it is useful to characterise the voice of a speaker with a fixed-length vector. These embeddings tend to be treated as "black box" encodings, and how they relate to conventional acoustic and phonetic dimensions of voices has not been widely studied. In this paper we investigate how state-of-the-art speaker embedding systems represent the acoustic characteristics of speakers as described by conventional acoustic descriptors, age, and gender. Using a large corpus of 10,000 speakers and three embedding systems we show that a small set of 9 acoustic parameters chosen to be "interpretable" predict embeddings about the same as 7 principal components, corresponding to over 50% of variance in the data. We show that some principal dimensions operate differently for male and female speakers, suggesting there is implicit gender recognition within the embedding systems. However we show that speaker age is not well captured by embeddings, suggesting opportunities exist for improvements in their calculation.
Semi-Peaucellier Linkage and Differential Mechanism for Linear Pinching and Self-Adaptive Grasping
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents the SP-Diff parallel gripper system, addressing the limited adaptability of conventional end-effectors in intelligent industrial automation. The proposed design employs an innovative differential linkage mechanism with a modular symmetric dual-finger configuration to achieve linear-parallel grasping. By integrating a planetary gear transmission, the system enables synchronized linear motion and independent finger pose adjustment while maintaining structural rigidity, reducing Z-axis recalibration requirements by 30% compared to arc-trajectory grippers. The compact palm architecture incorporates a kinematically optimized parallelogram linkage and Differential mechanism, demonstrating adaptive grasping capabilities for diverse industrial workpieces and deformable objects such as citrus fruits. Future-ready interfaces are embedded for potential force/vision sensor integration to facilitate multimodal data acquisition (e.g., trajectory planning and object deformation) in digital twin frameworks. Designed as a flexible manufacturing solution, SP-Diff advances robotic end-effector intelligence through its adaptive architecture, showing promising applications in collaborative robotics, logistics automation, and specialized operational scenarios.
Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks
Venue: arXiv cs.LG 2025
Year: 2025

Recent claims of strong performance by Large Language Models (LLMs) on causal discovery are undermined by a key flaw: many evaluations rely on benchmarks likely included in pretraining corpora. Thus, apparent success suggests that LLM-only methods, which ignore observational data, outperform classical statistical approaches. We challenge this narrative by asking: Do LLMs truly reason about causal structure, and how can we measure it without memorization concerns? Can they be trusted for real-world scientific discovery? We argue that realizing LLMs' potential for causal analysis requires two shifts: (P.1) developing robust evaluation protocols based on recent scientific studies to guard against dataset leakage, and (P.2) designing hybrid methods that combine LLM-derived knowledge with data-driven statistics. To address P.1, we encourage evaluating discovery methods on novel, real-world scientific studies. We outline a practical recipe for extracting causal graphs from recent publications released after an LLM's training cutoff, ensuring relevance and preventing memorization while capturing both established and novel relations. Compared to benchmarks like BNLearn, where LLMs achieve near-perfect accuracy, they perform far worse on our curated graphs, underscoring the need for statistical grounding. Supporting P.2, we show that using LLM predictions as priors for the classical PC algorithm significantly improves accuracy over both LLM-only and purely statistical methods. We call on the community to adopt science-grounded, leakage-resistant benchmarks and invest in hybrid causal discovery methods suited to real-world inquiry.
$ρ$Hammer: Reviving RowHammer Attacks on New Architectures via Prefetching
Venue: arXiv cs.CR 2025
Year: 2025

Rowhammer is a critical vulnerability in dynamic random access memory (DRAM) that continues to pose a significant threat to various systems. However, we find that conventional load-based attacks are becoming highly ineffective on the most recent architectures such as Intel Alder and Raptor Lake. In this paper, we present $ρ$Hammer, a new Rowhammer framework that systematically overcomes three core challenges impeding attacks on these new architectures. First, we design an efficient and generic DRAM address mapping reverse-engineering method that uses selective pairwise measurements and structured deduction, enabling recovery of complex mappings within seconds on the latest memory controllers. Second, to break through the activation rate bottleneck of load-based hammering, we introduce a novel prefetch-based hammering paradigm that leverages the asynchronous nature of x86 prefetch instructions and is further enhanced by multi-bank parallelism to maximize throughput. Third, recognizing that speculative execution causes more severe disorder issues for prefetching, which cannot be simply mitigated by memory barriers, we develop a counter-speculation hammering technique using control-flow obfuscation and optimized NOP-based pseudo-barriers to maintain prefetch order with minimal overhead. Evaluations across four latest Intel architectures demonstrate $ρ$Hammer's breakthrough effectiveness: it induces up to 200K+ additional bit flips within 2-hour attack pattern fuzzing processes and has a 112x higher flip rate than the load-based hammering baselines on Comet and Rocket Lake. Also, we are the first to revive Rowhammer attacks on the latest Raptor Lake architecture, where baselines completely fail, achieving stable flip rates of 2,291/min and fast end-to-end exploitation.
Symmetry and Generalisation in Neural Approximations of Renormalisation Transformations
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning models have proven enormously successful at using multiple layers of representation to learn relevant features of structured data. Encoding physical symmetries into these models can improve performance on difficult tasks, and recent work has motivated the principle of parameter symmetry breaking and restoration as a unifying mechanism underlying their hierarchical learning dynamics. We evaluate the role of parameter symmetry and network expressivity in the generalisation behaviour of neural networks when learning a real-space renormalisation group (RG) transformation, using the central limit theorem (CLT) as a test case map. We consider simple multilayer perceptrons (MLPs) and graph neural networks (GNNs), and vary weight symmetries and activation functions across architectures. Our results reveal a competition between symmetry constraints and expressivity, with overly complex or overconstrained models generalising poorly. We analytically demonstrate this poor generalisation behaviour for certain constrained MLP architectures by recasting the CLT as a cumulant recursion relation and making use of an established framework to propagate cumulants through MLPs. We also empirically validate an extension of this framework from MLPs to GNNs, elucidating the internal information processing performed by these more complex models. These findings offer new insight into the learning dynamics of symmetric networks and their limitations in modelling structured physical transformations.
First Responders' Perceptions of Semantic Information for Situational Awareness in Robot-Assisted Emergency Response
Venue: arXiv cs.RO 2025
Year: 2025

This study investigates First Responders' (FRs) attitudes toward the use of semantic information and Situational Awareness (SA) in robotic systems during emergency operations. A structured questionnaire was administered to 22 FRs across eight countries, capturing their demographic profiles, general attitudes toward robots, and experiences with semantics-enhanced SA. Results show that most FRs expressed positive attitudes toward robots, and rated the usefulness of semantic information for building SA at an average of 3.6 out of 5. Semantic information was also valued for its role in predicting unforeseen emergencies (mean 3.9). Participants reported requiring an average of 74.6\% accuracy to trust semantic outputs and 67.8\% for them to be considered useful, revealing a willingness to use imperfect but informative AI support tools. To the best of our knowledge, this study offers novel insights by being one of the first to directly survey FRs on semantic-based SA in a cross-national context. It reveals the types of semantic information most valued in the field, such as object identity, spatial relationships, and risk context-and connects these preferences to the respondents' roles, experience, and education levels. The findings also expose a critical gap between lab-based robotics capabilities and the realities of field deployment, highlighting the need for more meaningful collaboration between FRs and robotics researchers. These insights contribute to the development of more user-aligned and situationally aware robotic systems for emergency response.
CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Secure federated learning (FL) preserves data privacy during distributed model training. However, deploying such frameworks across heterogeneous devices results in performance bottlenecks, due to straggler clients with limited computational or network capabilities, slowing training for all participating clients. This paper introduces the first straggler mitigation technique for secure aggregation with deep neural networks. We propose CLIP, a client-side invariant neuron pruning technique coupled with network-aware pruning, that addresses compute and network bottlenecks due to stragglers during training with minimal accuracy loss. Our technique accelerates secure FL training by 13% to 34% across multiple datasets (CIFAR10, Shakespeare, FEMNIST) with an accuracy impact of between 1.3% improvement to 2.6% reduction.
Resolution-Aware Retrieval Augmented Zero-Shot Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Zero-shot forecasting aims to predict outcomes for previously unseen conditions without direct historical data, posing a significant challenge for traditional forecasting methods. We introduce a Resolution-Aware Retrieval-Augmented Forecasting model that enhances predictive accuracy by leveraging spatial correlations and temporal frequency characteristics. By decomposing signals into different frequency components, our model employs resolution-aware retrieval, where lower-frequency components rely on broader spatial context, while higher-frequency components focus on local influences. This allows the model to dynamically retrieve relevant data and adapt to new locations with minimal historical context. Applied to microclimate forecasting, our model significantly outperforms traditional forecasting methods, numerical weather prediction models, and modern foundation time series models, achieving 71% lower MSE than HRRR and 34% lower MSE than Chronos on the ERA5 dataset. Our results highlight the effectiveness of retrieval-augmented and resolution-aware strategies, offering a scalable and data-efficient solution for zero-shot forecasting in microclimate modeling and beyond.
Cryptanalysis of a Privacy-Preserving Ride-Hailing Service from NSS 2022
Venue: arXiv cs.CR 2025
Year: 2025

Ride-Hailing Services (RHS) match a ride request initiated by a rider with a suitable driver responding to the ride request. A Privacy-Preserving RHS (PP-RHS) aims to facilitate ride matching while ensuring the privacy of riders' and drivers' location data w.r.t. the Service Provider (SP). At NSS 2022, Xie et al. proposed a PP-RHS. In this work, we demonstrate a passive attack on their PP-RHS protocol. Our attack allows the SP to completely recover the locations of the rider as well as that of the responding drivers in every ride request. Further, our attack is very efficient as it is independent of the security parameter.
SAMOSA: Sharpness Aware Minimization for Open Set Active learning
Venue: arXiv cs.LG 2025
Year: 2025

Modern machine learning solutions require extensive data collection where labeling remains costly. To reduce this burden, open set active learning approaches aim to select informative samples from a large pool of unlabeled data that includes irrelevant or unknown classes. In this context, we propose Sharpness Aware Minimization for Open Set Active Learning (SAMOSA) as an effective querying algorithm. Building on theoretical findings concerning the impact of data typicality on the generalization properties of traditional stochastic gradient descent (SGD) and sharpness-aware minimization (SAM), SAMOSA actively queries samples based on their typicality. SAMOSA effectively identifies atypical samples that belong to regions of the embedding manifold close to the model decision boundaries. Therefore, SAMOSA prioritizes the samples that are (i) highly informative for the targeted classes, and (ii) useful for distinguishing between targeted and unwanted classes. Extensive experiments show that SAMOSA achieves up to 3% accuracy improvement over the state of the art across several datasets, while not introducing computational overhead. The source code of our experiments is available at: https://anonymous.4open.science/r/samosa-DAF4