OpenRaiser/Intern-Atlas huggingface dataset:
What If, But Privately: Private Counterfactual Retrieval
Venue: arXiv cs.IT 2025
Year: 2025

Transparency and explainability are two important aspects to be considered when employing black-box machine learning models in high-stake applications. Providing counterfactual explanations is one way of catering this requirement. However, this also poses a threat to the privacy of the institution that is providing the explanation, as well as the user who is requesting it. In this work, we are primarily concerned with the user's privacy who wants to retrieve a counterfactual instance, without revealing their feature vector to the institution. Our framework retrieves the exact nearest neighbor counterfactual explanation from a database of accepted points while achieving perfect, information-theoretic, privacy for the user. First, we introduce the problem of private counterfactual retrieval (PCR) and propose a baseline PCR scheme that keeps the user's feature vector information-theoretically private from the institution. Building on this, we propose two other schemes that reduce the amount of information leaked about the institution database to the user, compared to the baseline scheme. Second, we relax the assumption of mutability of all features, and consider the setting of immutable PCR (I-PCR). Here, the user retrieves the nearest counterfactual without altering a private subset of their features, which constitutes the immutable set, while keeping their feature vector and immutable set private from the institution. For this, we propose two schemes that preserve the user's privacy information-theoretically, but ensure varying degrees of database privacy. Third, we extend our PCR and I-PCR schemes to incorporate user's preference on transforming their attributes, so that a more actionable explanation can be received. Finally, we present numerical results to support our theoretical findings, and compare the database leakage of the proposed schemes.
No LLM Solved Yu Tsumura's 554th Problem
Venue: arXiv cs.LG 2025
Year: 2025

We show, contrary to the optimism about LLM's problem-solving abilities, fueled by the recent gold medals that were attained, that a problem exists -- Yu Tsumura's 554th problem -- that a) is within the scope of an IMO problem in terms of proof sophistication, b) is not a combinatorics problem which has caused issues for LLMs, c) requires fewer proof techniques than typical hard IMO problems, d) has a publicly available solution (likely in the training data of LLMs), and e) that cannot be readily solved by any existing off-the-shelf LLM (commercial or open-source).
LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation
Venue: arXiv cs.CV 2025
Year: 2025

Controllable ultra-long video generation is a fundamental yet challenging task. Although existing methods are effective for short clips, they struggle to scale due to issues such as temporal inconsistency and visual degradation. In this paper, we initially investigate and identify three key factors: separate noise initialization, independent control signal normalization, and the limitations of single-modality guidance. To address these issues, we propose LongVie, an end-to-end autoregressive framework for controllable long video generation. LongVie introduces two core designs to ensure temporal consistency: 1) a unified noise initialization strategy that maintains consistent generation across clips, and 2) global control signal normalization that enforces alignment in the control space throughout the entire video. To mitigate visual degradation, LongVie employs 3) a multi-modal control framework that integrates both dense (e.g., depth maps) and sparse (e.g., keypoints) control signals, complemented by 4) a degradation-aware training strategy that adaptively balances modality contributions over time to preserve visual quality. We also introduce LongVGenBench, a comprehensive benchmark consisting of 100 high-resolution videos spanning diverse real-world and synthetic environments, each lasting over one minute. Extensive experiments show that LongVie achieves state-of-the-art performance in long-range controllability, consistency, and quality.
Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective
Venue: arXiv cs.IR 2025
Year: 2025

The large language model (LLM) powered recommendation paradigm has been proposed to address the limitations of traditional recommender systems, which often struggle to handle cold start users or items with new IDs. Despite its effectiveness, this study uncovers that LLM empowered recommender systems are vulnerable to reconstruction attacks that can expose both system and user privacy. To examine this threat, we present the first systematic study on inversion attacks targeting LLM empowered recommender systems, where adversaries attempt to reconstruct original prompts that contain personal preferences, interaction histories, and demographic attributes by exploiting the output logits of recommendation models. We reproduce the vec2text framework and optimize it using our proposed method called Similarity Guided Refinement, enabling more accurate reconstruction of textual prompts from model generated logits. Extensive experiments across two domains (movies and books) and two representative LLM based recommendation models demonstrate that our method achieves high fidelity reconstructions. Specifically, we can recover nearly 65 percent of the user interacted items and correctly infer age and gender in 87 percent of the cases. The experiments also reveal that privacy leakage is largely insensitive to the victim model's performance but highly dependent on domain consistency and prompt complexity. These findings expose critical privacy vulnerabilities in LLM empowered recommender systems.
"Think First, Verify Always": Training Humans to Face AI Risks
Venue: arXiv cs.HC 2025
Year: 2025

Artificial intelligence enables unprecedented attacks on human cognition, yet cybersecurity remains predominantly device-centric. This paper introduces the "Think First, Verify Always" (TFVA) protocol, which repositions humans as 'Firewall Zero', the first line of defense against AI-enabled threats. The protocol is grounded in five operational principles: Awareness, Integrity, Judgment, Ethical Responsibility, and Transparency (AIJET). A randomized controlled trial (n=151) demonstrated that a minimal 3-minute intervention produced statistically significant improvements in cognitive security task performance, with participants showing an absolute +7.87% gains compared to controls. These results suggest that brief, principles-based training can rapidly enhance human resilience against AI-driven cognitive manipulation. We recommend that GenAI platforms embed "Think First, Verify Always" as a standard prompt, replacing passive warnings with actionable protocols to enhance trustworthy and ethical AI use. By bridging the gap between technical cybersecurity and human factors, the TFVA protocol establishes human-empowered security as a vital component of trustworthy AI systems.
Relationship between Perceived Maneuverability and Involuntary Eye Movements under Systematically Varied Time Constants of Ride-on Machinery
Venue: arXiv cs.HC 2025
Year: 2025

Studies suggest that involuntary eye movements exhibit greater stability during active motion compared to passive motion, and this effect may also apply to the operation of ride-on machinery. Moreover, a study suggested that experimentally manipulating the sense of agency (SoA) by introducing delays may influence the stability of involuntary eye movements. Although a preliminary investigation examined involuntary eye movements and perceived maneuverability under two distinct machine dynamics with preserved SoA, it remains unclear how systematic variations in motion dynamics influence these factors. Therefore, the purpose of the present research was to investigate whether systematic variations in the dynamic properties of a ride-on machine, where the perceived maneuverability is modulated, influence the accuracy of involuntary eye movements in human operators. Participants rode a yaw-rotational platform whose time constant from joystick input to motor torque of a rotational machine was systematically manipulated. During the operation, eye movements were recorded while participants fixated on a visual target. After each condition, participants provided subjective ratings of maneuverability and cognitive load. As the platform's time constant increased, the perceived maneuverability scores decreased while the cognitive loads increased. Concurrently, involuntary eye movement accuracy decreased. Moderate to weak positive correlations emerged between the perceived maneuverability scores and the eye movement gain and accuracy, while a weak negative correlation was found with cognitive load.
Enhancing Diameter Measurement Accuracy in Machine Vision Applications
Venue: arXiv cs.CV 2025
Year: 2025

In camera measurement systems, specialized equipment such as telecentric lenses is often employed to measure parts with narrow tolerances. However, despite the use of such equipment, measurement errors can occur due to mechanical and software-related factors within the system. These errors are particularly evident in applications where parts of different diameters are measured using the same setup. This study proposes two innovative approaches to enhance measurement accuracy using multiple known reference parts: a conversion factor-based method and a pixel-based method. In the first approach, the conversion factor is estimated from known references to calculate the diameter (mm) of the unknown part. In the second approach, the diameter (mm) is directly estimated using pixel-based diameter information from the references. The experimental setup includes an industrial-grade camera and telecentric lenses. Tests conducted on glass samples (1-12 mm) and metal workpieces (3-24 mm) show that measurement errors, which originally ranged from 13-114 micrometers, were reduced to 1-2 micrometers using the proposed methods. By utilizing only a few known reference parts, the proposed approach enables high-accuracy measurement of all parts within the camera's field of view. Additionally, this method enhances the existing diameter measurement literature by significantly reducing error rates and improving measurement reliability.
StorySync: Training-Free Subject Consistency in Text-to-Image Generation via Region Harmonization
Venue: arXiv cs.CV 2025
Year: 2025

Generating a coherent sequence of images that tells a visual story, using text-to-image diffusion models, often faces the critical challenge of maintaining subject consistency across all story scenes. Existing approaches, which typically rely on fine-tuning or retraining models, are computationally expensive, time-consuming, and often interfere with the model's pre-existing capabilities. In this paper, we follow a training-free approach and propose an efficient consistent-subject-generation method. This approach works seamlessly with pre-trained diffusion models by introducing masked cross-image attention sharing to dynamically align subject features across a batch of images, and Regional Feature Harmonization to refine visually similar details for improved subject consistency. Experimental results demonstrate that our approach successfully generates visually consistent subjects across a variety of scenarios while maintaining the creative abilities of the diffusion model.
Modular Transformer Architecture for Precision Agriculture Imaging
Venue: arXiv cs.CV 2025
Year: 2025

This paper addresses the critical need for efficient and accurate weed segmentation from drone video in precision agriculture. A quality-aware modular deep-learning framework is proposed that addresses common image degradation by analyzing quality conditions-such as blur and noise-and routing inputs through specialized pre-processing and transformer models optimized for each degradation type. The system first analyzes drone images for noise and blur using Mean Absolute Deviation and the Laplacian. Data is then dynamically routed to one of three vision transformer models: a baseline for clean images, a modified transformer with Fisher Vector encoding for noise reduction, or another with an unrolled Lucy-Richardson decoder to correct blur. This novel routing strategy allows the system to outperform existing CNN-based methods in both segmentation quality and computational efficiency, demonstrating a significant advancement in deep-learning applications for agriculture.
Generating Synthetic Invoices via Layout-Preserving Content Replacement
Venue: arXiv cs.CV 2025
Year: 2025

The performance of machine learning models for automated invoice processing is critically dependent on large-scale, diverse datasets. However, the acquisition of such datasets is often constrained by privacy regulations and the high cost of manual annotation. To address this, we present a novel pipeline for generating high-fidelity, synthetic invoice documents and their corresponding structured data. Our method first utilizes Optical Character Recognition (OCR) to extract the text content and precise spatial layout from a source invoice. Select data fields are then replaced with contextually realistic, synthetic content generated by a large language model (LLM). Finally, we employ an inpainting technique to erase the original text from the image and render the new, synthetic text in its place, preserving the exact layout and font characteristics. This process yields a pair of outputs: a visually realistic new invoice image and a perfectly aligned structured data file (JSON) reflecting the synthetic content. Our approach provides a scalable and automated solution to amplify small, private datasets, enabling the creation of large, varied corpora for training more robust and accurate document intelligence models.
FlashCommunication V2: Bit Splitting and Spike Reserving for Any Bit Communication
Venue: arXiv cs.DC 2025
Year: 2025

Nowadays, communication bottlenecks have emerged as a critical challenge in the distributed training and deployment of large language models (LLMs). This paper introduces FlashCommunication V2, a novel communication paradigm enabling efficient cross-GPU transmission at arbitrary bit widths. Its core innovations lie in the proposed bit splitting and spike reserving techniques, which address the challenges of low-bit quantization. Bit splitting decomposes irregular bit widths into basic units, ensuring compatibility with hardware capabilities and thus enabling transmission at any bit width. Spike reserving, on the other hand, retains numerical outliers (i.e., minima and maxima) as floating-point numbers, which shrinks the dynamic numerical range and pushes the quantization limits to 2-bit with acceptable losses. FlashCommunication V2 significantly enhances the flexibility and resource utilization of communication systems. Through meticulous software-hardware co-design, it delivers robust performance and reduced overhead across both NVLink-based and PCIe-based architectures, achieving a maximum 3.2$\times$ speedup in AllReduce and 2$\times$ in All2All communication.
CoughViT: A Self-Supervised Vision Transformer for Cough Audio Representation Learning
Venue: arXiv cs.SD 2025
Year: 2025

Physicians routinely assess respiratory sounds during the diagnostic process, providing insight into the condition of a patient's airways. In recent years, AI-based diagnostic systems operating on respiratory sounds, have demonstrated success in respiratory disease detection. These systems represent a crucial advancement in early and accessible diagnosis which is essential for timely treatment. However, label and data scarcity remain key challenges, especially for conditions beyond COVID-19, limiting diagnostic performance and reliable evaluation. In this paper, we propose CoughViT, a novel pre-training framework for learning general-purpose cough sound representations, to enhance diagnostic performance in tasks with limited data. To address label scarcity, we employ masked data modelling to train a feature encoder in a self-supervised learning manner. We evaluate our approach against other pre-training strategies on three diagnostically important cough classification tasks. Experimental results show that our representations match or exceed current state-of-the-art supervised audio representations in enhancing performance on downstream tasks.
Bernoulli-LoRA: A Theoretical Framework for Randomized Low-Rank Adaptation
Venue: arXiv cs.LG 2025
Year: 2025

Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large foundational models to specific tasks, particularly as model sizes continue to grow exponentially. Among PEFT methods, Low-Rank Adaptation (LoRA) (arXiv:2106.09685) stands out for its effectiveness and simplicity, expressing adaptations as a product of two low-rank matrices. While extensive empirical studies demonstrate LoRA's practical utility, theoretical understanding of such methods remains limited. Recent work on RAC-LoRA (arXiv:2410.08305) took initial steps toward rigorous analysis. In this work, we introduce Bernoulli-LoRA, a novel theoretical framework that unifies and extends existing LoRA approaches. Our method introduces a probabilistic Bernoulli mechanism for selecting which matrix to update. This approach encompasses and generalizes various existing update strategies while maintaining theoretical tractability. Under standard assumptions from non-convex optimization literature, we analyze several variants of our framework: Bernoulli-LoRA-GD, Bernoulli-LoRA-SGD, Bernoulli-LoRA-PAGE, Bernoulli-LoRA-MVR, Bernoulli-LoRA-QGD, Bernoulli-LoRA-MARINA, and Bernoulli-LoRA-EF21, establishing convergence guarantees for each variant. Additionally, we extend our analysis to convex non-smooth functions, providing convergence rates for both constant and adaptive (Polyak-type) stepsizes. Through extensive experiments on various tasks, we validate our theoretical findings and demonstrate the practical efficacy of our approach. This work is a step toward developing theoretically grounded yet practically effective PEFT methods.
Evaluating Software Supply Chain Security in Research Software
Venue: arXiv cs.SE 2025
Year: 2025

The security of research software is essential for ensuring the integrity and reproducibility of scientific results. However, research software security is still largely unexplored. Due to its dependence on open source components and distributed development practices, research software is particularly vulnerable to supply chain attacks. This study analyses 3,248 high-quality, largely peer-reviewed research software repositories using the OpenSSF Scorecard. We find a generally weak security posture with an average score of 3.5/10. Important practices, such as signed releases and branch protection, are rarely implemented. Finally, we present actionable, low-effort recommendations that can help research teams improve software security and mitigate potential threats to scientific integrity.
MI9: An Integrated Runtime Governance Framework for Agentic AI
Venue: arXiv cs.AI 2025
Year: 2025

Agentic AI systems capable of reasoning, planning, and executing actions present fundamentally distinct governance challenges compared to traditional AI models. Unlike conventional AI, these systems exhibit emergent and unexpected behaviors during runtime, introducing novel agent-related risks that cannot be fully anticipated through pre-deployment governance alone. To address this critical gap, we introduce MI9, the first fully integrated runtime governance framework designed specifically for safety and alignment of agentic AI systems. MI9 introduces real-time controls through six integrated components: agency-risk index, agent-semantic telemetry capture, continuous authorization monitoring, Finite-State-Machine (FSM)-based conformance engines, goal-conditioned drift detection, and graduated containment strategies. Operating transparently across heterogeneous agent architectures, MI9 enables the systematic, safe, and responsible deployment of agentic systems in production environments where conventional governance approaches fall short, providing the foundational infrastructure for safe agentic AI deployment at scale. Detailed analysis through a diverse set of scenarios demonstrates MI9's systematic coverage of governance challenges that existing approaches fail to address, establishing the technical foundation for comprehensive agentic AI oversight.
Reinforcement Learning for Target Zone Blood Glucose Control
Venue: arXiv cs.LG 2025
Year: 2025

Managing physiological variables within clinically safe target zones is a central challenge in healthcare, particularly for chronic conditions such as Type 1 Diabetes Mellitus (T1DM). Reinforcement learning (RL) offers promise for personalising treatment, but struggles with the delayed and heterogeneous effects of interventions. We propose a novel RL framework to study and support decision-making in T1DM technologies, such as automated insulin delivery. Our approach captures the complex temporal dynamics of treatment by unifying two control modalities: \textit{impulse control} for discrete, fast-acting interventions (e.g., insulin boluses), and \textit{switching control} for longer-acting treatments and regime shifts. The core of our method is a constrained Markov decision process augmented with physiological state features, enabling safe policy learning under clinical and resource constraints. The framework incorporates biologically realistic factors, including insulin decay, leading to policies that better reflect real-world therapeutic behaviour. While not intended for clinical deployment, this work establishes a foundation for future safe and temporally-aware RL in healthcare. We provide theoretical guarantees of convergence and demonstrate empirical improvements in a stylised T1DM control task, reducing blood glucose level violations from 22.4\% (state-of-the-art) to as low as 10.8\%.
Sotopia-RL: Reward Design for Social Intelligence
Venue: arXiv cs.CL 2025
Year: 2025

Social intelligence has become a critical capability for large language models (LLMs), enabling them to engage effectively in real-world social tasks such as collaboration and negotiation. Reinforcement learning (RL) is a natural fit for training socially intelligent agents because it allows models to learn sophisticated strategies directly through social interactions without requiring human annotations. However, there are two unique parts about social intelligence tasks: (1) the quality of individual utterances in social interactions is not strictly related to final success; (2) social interactions require multi-dimensional rubrics for success. Therefore, we argue that it is necessary to design rewards for building utterance-level multi-dimensional reward models to facilitate RL training for social intelligence tasks. To address these challenges, we propose Sotopia-RL, a novel framework that refines coarse episode-level feedback into utterance-level, multi-dimensional rewards. Utterance-level credit assignment attributes outcomes to individual utterances, while multi-dimensional rewards capture the full richness of social interactions and reduce reward hacking. Experiments in Sotopia, an open-ended social learning environment, demonstrate that Sotopia-RL achieves state-of-the-art social goal completion scores (7.17 on Sotopia-hard and 8.31 on Sotopia-full), significantly outperforming existing approaches. Ablation studies confirm the necessity of both utterance-level credit assignment and multi-dimensional reward design for RL training.