OpenRaiser/Intern-Atlas huggingface dataset:
Vision-Language Model Dialog Games for Self-Improvement
Venue: arXiv cs.LG 2025
Year: 2025

The increasing demand for high-quality, diverse training data poses a significant bottleneck in advancing vision-language models (VLMs). This paper presents VLM Dialog Games, a novel and scalable self-improvement framework for VLMs. Our approach leverages self-play between two agents engaged in a goal-oriented play centered around image identification. By filtering for successful game interactions, we automatically curate a high-quality dataset of interleaved images and text. We demonstrate that fine-tuning on this synthetic data leads to performance gains on downstream tasks and generalises across datasets. Moreover, as the improvements in the model lead to better game play, this procedure can be applied iteratively. This work paves the way for self-improving VLMs, with potential applications in various real-world scenarios especially when the high-quality multimodal data is scarce.
Unveiling Privacy and Security Gaps in Female Health Apps
Venue: arXiv cs.HC 2025
Year: 2025

Female Health Applications (FHA), a growing segment of FemTech, aim to provide affordable and accessible healthcare solutions for women globally. These applications gather and monitor health and reproductive data from millions of users. With ongoing debates on women's reproductive rights and privacy, it's crucial to assess how these apps protect users' privacy. In this paper, we undertake a security and data protection assessment of 45 popular FHAs. Our investigation uncovers harmful permissions, extensive collection of sensitive personal and medical data, and the presence of numerous third-party tracking libraries. Furthermore, our examination of their privacy policies reveals deviations from fundamental data privacy principles. These findings highlight a significant lack of privacy and security measures for FemTech apps, especially as women's reproductive rights face growing political challenges. The results and recommendations provide valuable insights for users, app developers, and policymakers, paving the way for better privacy and security in Female Health Applications.
Cache is King: Smart Page Eviction with eBPF
Venue: arXiv cs.OS 2025
Year: 2025

The page cache is a central part of an OS. It reduces repeated accesses to storage by deciding which pages to retain in memory. As a result, the page cache has a significant impact on the performance of many applications. However, its one-size-fits-all eviction policy performs poorly in many workloads. While the systems community has experimented with a plethora of new and adaptive eviction policies in non-OS settings (e.g., key-value stores, CDNs), it is very difficult to implement such policies in the page cache, due to the complexity of modifying kernel code. To address these shortcomings, we design a novel eBPF-based framework for the Linux page cache, called $\texttt{cachebpf}$, that allows developers to customize the page cache without modifying the kernel. $\texttt{cachebpf}$ enables applications to customize the page cache policy for their specific needs, while also ensuring that different applications' policies do not interfere with each other and preserving the page cache's ability to share memory across different processes. We demonstrate the flexibility of $\texttt{cachebpf}$'s interface by using it to implement several eviction policies. Our evaluation shows that it is indeed beneficial for applications to customize the page cache to match their workloads' unique properties, and that they can achieve up to 70% higher throughput and 58% lower tail latency.
Cross-modality Force and Language Embeddings for Natural Human-Robot Communication
Venue: arXiv cs.RO 2025
Year: 2025

A method for cross-modality embedding of force profile and words is presented for synergistic coordination of verbal and haptic communication. When two people carry a large, heavy object together, they coordinate through verbal communication about the intended movements and physical forces applied to the object. This natural integration of verbal and physical cues enables effective coordination. Similarly, human-robot interaction could achieve this level of coordination by integrating verbal and haptic communication modalities. This paper presents a framework for embedding words and force profiles in a unified manner, so that the two communication modalities can be integrated and coordinated in a way that is effective and synergistic. Here, it will be shown that, although language and physical force profiles are deemed completely different, the two can be embedded in a unified latent space and proximity between the two can be quantified. In this latent space, a force profile and words can a) supplement each other, b) integrate the individual effects, and c) substitute in an exchangeable manner. First, the need for cross-modality embedding is addressed, and the basic architecture and key building block technologies are presented. Methods for data collection and implementation challenges will be addressed, followed by experimental results and discussions.
Where Do Passengers Gaze? Impact of Passengers' Personality Traits on Their Gaze Pattern Toward Pedestrians During APMV-Pedestrian Interactions with Diverse eHMIs
Venue: arXiv cs.HC 2025
Year: 2025

Autonomous Personal Mobility Vehicles (APMVs) are designed to address the ``last-mile'' transportation challenge for everyone. When an APMV encounters a pedestrian, it uses an external Human-Machine Interface (eHMI) to negotiate road rights. Through this interaction, passengers also engage with the process. This study examines passengers' gaze behavior toward pedestrians during such interactions, focusing on whether different eHMI designs influence gaze patterns based on passengers' personality traits. The results indicated that when using a visual-based eHMI, passengers often struggled to perceive the communication content. Consequently, passengers with higher Neuroticism scores, who were more sensitive to communication details, might seek cues from pedestrians' reactions. In addition, a multimodal eHMI (visual and voice) using neutral voice did not significantly affect the gaze behavior of passengers toward pedestrians, regardless of personality traits. In contrast, a multimodal eHMI using affective voice encouraged passengers with high Openness to Experience scores to focus on pedestrians' heads. In summary, this study revealed how different eHMI designs influence passengers' gaze behavior and highlighted the effects of personality traits on their gaze patterns toward pedestrians, providing new insights for personalized eHMI designs.
METAMON: Finding Inconsistencies between Program Documentation and Behavior using Metamorphic LLM Queries
Venue: arXiv cs.SE 2025
Year: 2025

Code documentation can, if written precisely, help developers better understand the code they accompany. However, unlike code, code documentation cannot be automatically verified via execution, potentially leading to inconsistencies between documentation and the actual behavior. While such inconsistencies can be harmful for the developer's understanding of the code, checking and finding them remains a costly task due to the involvement of human engineers. This paper proposes METAMON, which uses an existing search-based test generation technique to capture the current program behavior in the form of test cases, and subsequently uses LLM-based code reasoning to identify the generated regression test oracles that are not consistent with the program specifications in the documentation. METAMON is supported in this task by metamorphic testing and self-consistency. An empirical evaluation against 9,482 pairs of code documentation and code snippets, generated using five open-source projects from Defects4J v2.0.1, shows that METAMON can classify the code-and-documentation inconsistencies with a precision of 0.72 and a recall of 0.48.
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
Venue: arXiv cs.LG 2025
Year: 2025

Recent advances in large language models (LLMs) have led to models that tackle diverse molecular tasks, such as chemical reaction prediction and molecular property prediction. Large-scale molecular instruction-tuning datasets have enabled sequence-only (e.g., SMILES or SELFIES) generalist molecular LLMs, and researchers are now exploring multimodal approaches that incorporate molecular structural information for further gains. However, a genuinely multimodal, generalist LLM that covers a broad spectrum of molecular tasks has yet to be fully investigated. We observe that naive next token prediction training ignores graph-structural information, limiting an LLM's ability to exploit molecular graphs. To address this, we propose (i) Molecular structure Preference Optimization (MolPO), which facilitates graph usage by optimizing preferences between pairs of correct and perturbed molecular structures, and (ii) an advanced graph encoder with a tailored pre-training strategy to improve the effect of graph utilization by MolPO. Building on these contributions, we introduce Mol-LLM, the first multimodal generalist model that (a) handles a broad spectrum of molecular tasks among molecular LLMs, (b) explicitly leverages molecular-structure information, and (c) takes advantage of extensive instruction tuning. Mol-LLM attains state-of-the-art or comparable results across the most comprehensive molecular-LLM benchmark-even on out-of-distribution datasets for reaction and property prediction, where it surpasses prior generalist molecular LLMs by a large margin.
TranSQL+: Serving Large Language Models with SQL on Low-Resource Hardware
Venue: arXiv cs.DB 2025
Year: 2025

Deploying Large Language Models (LLMs) on resource-constrained devices remains challenging due to limited memory, lack of GPUs, and the complexity of existing runtimes. In this paper, we introduce TranSQL+, a template-based code generator that translates LLM computation graphs into pure SQL queries for execution in relational databases. Without relying on external libraries, TranSQL+, leverages mature database features, such as vectorized execution and out-of-core processing, for efficient inference. We further propose a row-to-column (ROW2COL) optimization that improves join efficiency in matrix operations. Evaluated on Llama3-8B and DeepSeekMoE models, TranSQL+ achieves up to 20x lower prefill latency and 4x higher decoding speed compared to DeepSpeed Inference and Llama.cpp in low-memory and CPU-only configurations. Our results highlight relational databases as a practical environment for LLMs on low-resource hardware.
RS-YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images
Venue: arXiv cs.CV 2025
Year: 2025

Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model is named RS-YOLOX. To strengthen the feature learning ability of the network, we used Efficient Channel Attention (ECA) in the backbone network of YOLOX and combined the Adaptively Spatial Feature Fusion (ASFF) with the neck network of YOLOX. To balance the numbers of positive and negative samples in training, we used the Varifocal Loss function. Finally, to obtain a high-performance remote sensing object detector, we combined the trained model with an open-source framework called Slicing Aided Hyper Inference (SAHI). This work evaluated models on three aerial remote sensing datasets (DOTA-v1.5, TGRS-HRRSD, and RSOD). Our comparative experiments demonstrate that our model has the highest accuracy in detecting objects in remote sensing image datasets.
5G-AKA-HPQC: Hybrid Post-Quantum Cryptography Protocol for Quantum-Resilient 5G Primary Authentication with Forward Secrecy
Venue: arXiv cs.CR 2025
Year: 2025

5G enables digital innovation by integrating diverse services, making security especially primary authentication crucial. Two standardized protocols, 5G AKA and EAP AKA', handle authentication for 3GPP and non 3GPP devices. However, 5G AKA has vulnerabilities, including linkability attacks. Additionally, quantum computing poses threats, requiring quantum resistant cryptography. While post-quantum cryptography (PQC) is being standardized, its real world robustness remains unproven. Conventional cryptographic schemes offer reliability due to decades of practical use. To bridge this gap, IETF is standardizing hybrid PQC (HPQC), combining classical and quantum resistant methods. Ensuring forward secrecy and quantum resilience in 5G-AKA is critical. To address these issues, we propose 5G AKA HPQC, a protocol maintaining compatibility with existing standards while enhancing security by integrating keys derived from Elliptic Curve Integrated Encryption Scheme (ECIES) and PQC Key Encapsulation Mechanism (KEM). We validate its security using SVO Logic and ProVerif, confirming its robustness. Performance evaluations assess computational and communication overheads, demonstrating a balance between security and efficiency. This research provides key insights into quantum-safe authentication, contributing to future standardization of secure mobile authentication protocols.
PH-VAE: A Polynomial Hierarchical Variational Autoencoder Towards Disentangled Representation Learning
Venue: arXiv cs.LG 2025
Year: 2025

The variational autoencoder (VAE) is a simple and efficient generative artificial intelligence method for modeling complex probability distributions of various types of data, such as images and texts. However, it suffers some main shortcomings, such as lack of interpretability in the latent variables, difficulties in tuning hyperparameters while training, producing blurry, unrealistic downstream outputs or loss of information due to how it calculates loss functions and recovers data distributions, overfitting, and origin gravity effect for small data sets, among other issues. These and other limitations have caused unsatisfactory generation effects for the data with complex distributions. In this work, we proposed and developed a polynomial hierarchical variational autoencoder (PH-VAE), in which we used a polynomial hierarchical date format to generate or to reconstruct the data distributions. In doing so, we also proposed a novel Polynomial Divergence in the loss function to replace or generalize the Kullback-Leibler (KL) divergence, which results in systematic and drastic improvements in both accuracy and reproducibility of the re-constructed distribution function as well as the quality of re-constructed data images while keeping the dataset size the same but capturing fine resolution of the data. Moreover, we showed that the proposed PH-VAE has some form of disentangled representation learning ability.
A Systematic Approach for Assessing Large Language Models' Test Case Generation Capability
Venue: arXiv cs.SE 2025
Year: 2025

Software testing ensures the quality and reliability of software products, but manual test case creation is labor-intensive. With the rise of large language models (LLMs), there is growing interest in unit test creation with LLMs. However, effective assessment of LLM-generated test cases is limited by the lack of standardized benchmarks that comprehensively cover diverse programming scenarios. To address the assessment of LLM's test case generation ability and lacking dataset for evaluation, we propose the Generated Benchmark from Control-Flow Structure and Variable Usage Composition (GBCV) approach, which systematically generates programs used for evaluating LLMs' test generation capabilities. By leveraging basic control-flow structures and variable usage, GBCV provides a flexible framework to create a spectrum of programs ranging from simple to complex. Because GPT-4o and GPT-3-Turbo are publicly accessible models, to present real-world regular user's use case, we use GBCV to assess LLM performance on them. Our findings indicate that GPT-4o performs better on complex program structures, while all models effectively detect boundary values in simple conditions but face challenges with arithmetic computations. This study highlights the strengths and limitations of LLMs in test generation, provides a benchmark framework, and suggests directions for future improvement.
Differentially-Private Multi-Tier Federated Learning: A Formal Analysis and Evaluation
Venue: arXiv cs.NI 2025
Year: 2025

While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters. Differential privacy (DP) is often employed to address such issues. However, the impact of DP on FL in multi-tier networks -- where hierarchical aggregations couple noise injection decisions at different tiers, and trust models are heterogeneous across subnetworks -- is not well understood. To fill this gap, we develop \underline{M}ulti-Tier \underline{F}ederated Learning with \underline{M}ulti-Tier \underline{D}ifferential \underline{P}rivacy ({\tt M$^2$FDP}), a DP-enhanced FL methodology for jointly optimizing privacy and performance over such networks. One of the key principles of {\tt M$^2$FDP} is to adapt DP noise injection across the established edge/fog computing hierarchy (e.g., edge devices, intermediate nodes, and other tiers up to cloud servers) according to the trust models in different subnetworks. We conduct a comprehensive analysis of the convergence behavior of {\tt M$^2$FDP} under non-convex problem settings, revealing conditions on parameter tuning under which the training process converges sublinearly to a finite stationarity gap that depends on the network hierarchy, trust model, and target privacy level. We show how these relationships can be employed to develop an adaptive control algorithm for {\tt M$^2$FDP} that tunes properties of local model training to minimize energy, latency, and the stationarity gap while meeting desired convergence and privacy criterion. Subsequent numerical evaluations demonstrate that {\tt M$^2$FDP} obtains substantial improvements in these metrics over baselines for different privacy budgets and system configurations.
Variations on the Expectation due to Changes in the Probability Measure
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, closed-form expressions are presented for the variation of the expectation of a given function due to changes in the probability measure used for the expectation. They unveil interesting connections with Gibbs probability measures, mutual information, and lautum information.
SPARC: Subspace-Aware Prompt Adaptation for Robust Continual Learning in LLMs
Venue: arXiv cs.LG 2025
Year: 2025

We propose SPARC, a lightweight continual learning framework for large language models (LLMs) that enables efficient task adaptation through prompt tuning in a lower-dimensional space. By leveraging principal component analysis (PCA), we identify a compact subspace of the training data. Optimizing prompts in this lower-dimensional space enhances training efficiency, as it focuses updates on the most relevant features while reducing computational overhead. Furthermore, since the model's internal structure remains unaltered, the extensive knowledge gained from pretraining is fully preserved, ensuring that previously learned information is not compromised during adaptation. Our method achieves high knowledge retention in both task-incremental and domain-incremental continual learning setups while fine-tuning only 0.04% of the model's parameters. Additionally, by integrating LoRA, we enhance adaptability to computational constraints, allowing for a tradeoff between accuracy and training cost. Experiments on the SuperGLUE benchmark demonstrate that our PCA-based prompt tuning combined with LoRA maintains full knowledge retention while improving accuracy, utilizing only 1% of the model's parameters. These results establish our approach as a scalable and resource-efficient solution for continual learning in LLMs.
DANDI: Diffusion as Normative Distribution for Deep Neural Network Input
Venue: arXiv cs.SE 2025
Year: 2025

Surprise Adequacy (SA) has been widely studied as a test adequacy metric that can effectively guide software engineers towards inputs that are more likely to reveal unexpected behaviour of Deep Neural Networks (DNNs). Intuitively, SA is an out-of-distribution metric that quantifies the dissimilarity between the given input and the training data: if a new input is very different from those seen during training, the DNN is more likely to behave unexpectedly against the input. While SA has been widely adopted as a test prioritization method, its major weakness is the fact that the computation of the metric requires access to the training dataset, which is often not allowed in real-world use cases. We present DANDI, a technique that generates a surrogate input distribution using Stable Diffusion to compute SA values without requiring the original training data. An empirical evaluation of DANDI applied to image classifiers for CIFAR10 and ImageNet-1K shows that SA values computed against synthetic data are highly correlated with the values computed against the training data, with Spearman Rank correlation value of 0.852 for ImageNet-1K and 0.881 for CIFAR-10. Further, we show that SA value computed by DANDI achieves can prioritize inputs as effectively as those computed using the training data, when testing DNN models mutated by DeepMutation. We believe that DANDI can significantly improve the usability of SA for practical DNN testing.
Adaptive Budget Optimization for Multichannel Advertising Using Combinatorial Bandits
Venue: arXiv cs.LG 2025
Year: 2025

Effective budget allocation is crucial for optimizing the performance of digital advertising campaigns. However, the development of practical budget allocation algorithms remain limited, primarily due to the lack of public datasets and comprehensive simulation environments capable of verifying the intricacies of real-world advertising. While multi-armed bandit (MAB) algorithms have been extensively studied, their efficacy diminishes in non-stationary environments where quick adaptation to changing market dynamics is essential. In this paper, we advance the field of budget allocation in digital advertising by introducing three key contributions. First, we develop a simulation environment designed to mimic multichannel advertising campaigns over extended time horizons, incorporating logged real-world data. Second, we propose an enhanced combinatorial bandit budget allocation strategy that leverages a saturating mean function and a targeted exploration mechanism with change-point detection. This approach dynamically adapts to changing market conditions, improving allocation efficiency by filtering target regions based on domain knowledge. Finally, we present both theoretical analysis and empirical results, demonstrating that our method consistently outperforms baseline strategies, achieving higher rewards and lower regret across multiple real-world campaigns.