OpenRaiser/Intern-Atlas huggingface dataset:
BaThe: Defense against the Jailbreak Attack in Multimodal Large Language Models by Treating Harmful Instruction as Backdoor Trigger
Venue: arXiv cs.CR 2025
Year: 2025

Multimodal Large Language Models (MLLMs) have showcased impressive performance in a variety of multimodal tasks. On the other hand, the integration of additional image modality may allow the malicious users to inject harmful content inside the images for jailbreaking. Unlike text-based LLMs, where adversaries need to select discrete tokens to conceal their malicious intent using specific algorithms, the continuous nature of image signals provides a direct opportunity for adversaries to inject harmful intentions. In this work, we propose $\textbf{BaThe}$ ($\textbf{Ba}$ckdoor $\textbf{T}$rigger S$\textbf{h}$i$\textbf{e}$ld), a simple yet effective jailbreak defense mechanism. Our work is motivated by recent research on jailbreak backdoor attack and virtual prompt backdoor attack in generative language models. Jailbreak backdoor attack uses harmful instructions combined with manually crafted strings as triggers to make the backdoored model generate prohibited responses. We assume that harmful instructions can function as triggers, and if we alternatively set rejection responses as the triggered response, the backdoored model then can defend against jailbreak attacks. We achieve this by utilizing virtual rejection prompt, similar to the virtual prompt backdoor attack. We embed the virtual rejection prompt into the soft text embeddings, which we call ``wedge''. Our comprehensive experiments demonstrate that BaThe effectively mitigates various types of jailbreak attacks and is adaptable to defend against unseen attacks, with minimal impact on MLLMs' performance.
V2X-VLM: End-to-End V2X Cooperative Autonomous Driving Through Large Vision-Language Models
Venue: arXiv cs.RO 2025
Year: 2025

Vehicle-to-everything (V2X) cooperation has emerged as a promising paradigm to overcome the perception limitations of classical autonomous driving by leveraging information from both ego-vehicle and infrastructure sensors. However, effectively fusing heterogeneous visual and semantic information while ensuring robust trajectory planning remains a significant challenge. This paper introduces V2X-VLM, a novel end-to-end (E2E) cooperative autonomous driving framework based on vision-language models (VLMs). V2X-VLM integrates multiperspective camera views from vehicles and infrastructure with text-based scene descriptions to enable a more comprehensive understanding of driving environments. Specifically, we propose a contrastive learning-based mechanism to reinforce the alignment of heterogeneous visual and textual characteristics, which enhances the semantic understanding of complex driving scenarios, and employ a knowledge distillation strategy to stabilize training. Experiments on a large real-world dataset demonstrate that V2X-VLM achieves state-of-the-art trajectory planning accuracy, significantly reducing L2 error and collision rate compared to existing cooperative autonomous driving baselines. Ablation studies validate the contributions of each component. Moreover, the evaluation of robustness and efficiency highlights the practicality of V2X-VLM for real-world deployment to enhance overall autonomous driving safety and decision-making.
Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
Venue: arXiv cs.LG 2025
Year: 2025

Bayesian Physics Informed Neural Networks (BPINN) have attracted considerable attention for inferring the system states and physical parameters of differential equations according to noisy observations. However, in practice, Hamiltonian Monte Carlo (HMC) used to estimate the internal parameters of the solver for BPINN often encounters these troubles including poor performance and awful convergence for a given step size used to adjust the momentum of those parameters. To address the convergence of HMC for the BPINN method and extend its application scope to multi-scale partial differential equations (PDE), we develop a robust multi-scale BPINN (dubbed MBPINN) method by integrating multi-scale deep neural networks (MscaleDNN) and the BPINN framework. In this newly proposed MBPINN method, we reframe HMC with Stochastic Gradient Descent (SGD) to ensure the most ``likely'' estimation is always provided, and we configure its solver as a Fourier feature mapping-induced MscaleDNN. This novel method offers several key advantages: (1) it is more robust than HMC, (2) it incurs less computational cost than HMC, and (3) it is more flexible for complex problems. We demonstrate the applicability and performance of the proposed method through some general Poisson and multi-scale elliptic problems in one and two-dimensional Euclidean spaces. Our findings indicate that the proposed method can avoid HMC failures and provide valid results. Additionally, our method is capable of handling complex elliptic PDE and producing comparable results for general elliptic PDE under the case of lower signal-to-noise rate. These findings suggest that our proposed approach has great potential for physics-informed machine learning for parameter estimation and solution recovery in the case of ill-posed problems.
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
Venue: arXiv cs.LG 2025
Year: 2025

Differentially Private Federated Learning (DPFL) strengthens privacy protection by perturbing model gradients with noise, though at the cost of reduced accuracy. Although prior empirical studies indicate that initializing from pre-trained rather than random parameters can alleviate noise disturbance, the problem of optimally fine-tuning pre-trained models in DPFL remains unaddressed. In this paper, we propose Pretrain-DPFL, a framework that systematically evaluates three most representative fine-tuning strategies: full-tuning (FT), head-tuning (HT), and unified-tuning(UT) combining HT followed by FT. Through convergence analysis under smooth non-convex loss, we establish theoretical conditions for identifying the optimal fine-tuning strategy in Pretrain-DPFL, thereby maximizing the benefits of pre-trained models in mitigating noise disturbance. Extensive experiments across multiple datasets demonstrate Pretrain-DPFL's superiority, achieving $25.22\%$ higher accuracy than scratch training and outperforming the second-best baseline by $8.19\%$, significantly improving the privacy-utility trade-off in DPFL.
Directed Exploration in Reinforcement Learning from Linear Temporal Logic
Venue: arXiv cs.LG 2025
Year: 2025

Linear temporal logic (LTL) is a powerful language for task specification in reinforcement learning, as it allows describing objectives beyond the expressivity of conventional discounted return formulations. Nonetheless, recent works have shown that LTL formulas can be translated into a variable rewarding and discounting scheme, whose optimization produces a policy maximizing a lower bound on the probability of formula satisfaction. However, the synthesized reward signal remains fundamentally sparse, making exploration challenging. We aim to overcome this limitation, which can prevent current algorithms from scaling beyond low-dimensional, short-horizon problems. We show how better exploration can be achieved by further leveraging the LTL specification and casting its corresponding Limit Deterministic Büchi Automaton (LDBA) as a Markov reward process, thus enabling a form of high-level value estimation. By taking a Bayesian perspective over LDBA dynamics and proposing a suitable prior distribution, we show that the values estimated through this procedure can be treated as a shaping potential and mapped to informative intrinsic rewards. Empirically, we demonstrate applications of our method from tabular settings to high-dimensional continuous systems, which have so far represented a significant challenge for LTL-based reinforcement learning algorithms.
Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have achieved great success in various reasoning tasks. In this work, we focus on the graph reasoning ability of LLMs. Although theoretical studies proved that LLMs are capable of handling graph reasoning tasks, empirical evaluations reveal numerous failures. To deepen our understanding on this discrepancy, we revisit the ability of LLMs on three fundamental graph tasks: graph description translation, graph connectivity, and the shortest-path problem. Our findings suggest that LLMs can fail to understand graph structures through text descriptions and exhibit varying performance for all these three fundamental tasks. Meanwhile, we perform a real-world investigation on knowledge graphs and make consistent observations with our findings. The codes and datasets are available.
Infinite Scrolling, Finite Satisfaction: Exploring User Behavior and Satisfaction on Social Media in Bangladesh
Venue: arXiv cs.HC 2025
Year: 2025

Social media platforms continue to change our digital relationships nowadays. Therefore, recognizing the complex consequences of infinite scrolling is essential. This paper explores two distinct angles of social media engagement: mindless scrolling and mindful scrolling. This extensive study dives into numerous aspects of social media user behavior and satisfaction via the perspective of multiple surveys. We investigate the psychological exploit of infinite scrolling design to keep users engaged, illuminating its effect on users' emotional well-being. Furthermore, we explore its diverse effects on various groups, such as teenagers, professional people, and pregnant women, to better understand how digital activity differs throughout life phases. Furthermore, our study reveals the psychological consequences of being exposed to unfavorable news material. In the context of nutritional objectives, we examine the problems users confront as well as the significance of scrolling in dietary achievement. By taking into account the demographic effect, we can determine how factors like age, gender, and socioeconomic position affect user behavior. This study presents a comprehensive knowledge of the complicated connection of infinite scrolling with user satisfaction and psychological well-being through a variety of surveys, opening the door for well-informed conversations on online engagement.
Ranking Generated Answers: On the Agreement of Retrieval Models with Humans on Consumer Health Questions
Venue: arXiv cs.IR 2025
Year: 2025

Evaluating the output of generative large language models (LLMs) is challenging and difficult to scale. Many evaluations of LLMs focus on tasks such as single-choice question-answering or text classification. These tasks are not suitable for assessing open-ended question-answering capabilities, which are critical in domains where expertise is required. One such domain is health, where misleading or incorrect answers can have a negative impact on a user's well-being. Using human experts to evaluate the quality of LLM answers is generally considered the gold standard, but expert annotation is costly and slow. We present a method for evaluating LLM answers that uses ranking models trained on annotated document collections as a substitute for explicit relevance judgements and apply it to the CLEF 2021 eHealth dataset. In a user study, our method correlates with the preferences of a human expert (Kendall's $τ=0.64$). It is also consistent with previous findings in that the quality of generated answers improves with the size of the model and more sophisticated prompting strategies.
Machine Learning with Physics Knowledge for Prediction: A Survey
Venue: arXiv cs.LG 2025
Year: 2025

This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning.
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits
Venue: arXiv cs.LG 2025
Year: 2025

The restless multi-armed bandit (RMAB) framework is a popular model with applications across a wide variety of fields. However, its solution is hindered by the exponentially growing state space (with respect to the number of arms) and the combinatorial action space, making traditional reinforcement learning methods infeasible for large-scale instances. In this paper, we propose GINO-Q, a three-timescale stochastic approximation algorithm designed to learn an asymptotically optimal index policy for RMABs. GINO-Q mitigates the curse of dimensionality by decomposing the RMAB into a series of subproblems, each with the same dimension as a single arm, ensuring that complexity increases linearly with the number of arms. Unlike recently developed Whittle-index-based algorithms, GINO-Q does not require RMABs to be indexable, enhancing its flexibility and applicability. Our experimental results demonstrate that GINO-Q consistently learns near-optimal policies, even for non-indexable RMABs where Whittle-index-based algorithms perform poorly, and it converges significantly faster than existing baselines.
Multi-layer diffusion model of photovoltaic installations
Venue: arXiv cs.CE 2025
Year: 2025

Nowadays, harmful effects of climate change are becoming increasingly apparent. A vital issue that must be addressed is the generation of energy from non-renewable and often polluting sources. For this reason, the development of renewable energy sources is of great importance. Unfortunately, too rapid spread of renewables can disrupt stability of the power system and lead to energy blackouts. One should not simply support it, without ensuring sustainability and understanding of the diffusion process. In this research, we propose a new agent-based model of diffusion of photovoltaic panels. It is an extension of the q-voter model that utilizes a multi-layer network structure. The novelty is that both opinion dynamics and diffusion of innovation are studied simultaneously on a multidimensional structure. The model is analyzed using Monte Carlo simulations and the mean-field approximation. The impact of parameters and specifications on the basic properties of the model is discussed. Firstly, we show that for a certain range of parameters, innovation always succeeds, regardless of the initial conditions. Secondly, that the mean-field approximation gives qualitatively the same results as computer simulations, even though it does not utilize knowledge of the network structure.
P3P: Pseudo-3D Pre-training for Scaling 3D Voxel-based Masked Autoencoders
Venue: arXiv cs.CV 2025
Year: 2025

3D pre-training is crucial to 3D perception tasks. Nevertheless, limited by the difficulties in collecting clean and complete 3D data, 3D pre-training has persistently faced data scaling challenges. In this work, we introduce a novel self-supervised pre-training framework that incorporates millions of images into 3D pre-training corpora by leveraging a large depth estimation model. New pre-training corpora encounter new challenges in representation ability and embedding efficiency of models. Previous pre-training methods rely on farthest point sampling and k-nearest neighbors to embed a fixed number of 3D tokens. However, these approaches prove inadequate when it comes to embedding millions of samples that feature a diverse range of point numbers, spanning from 1,000 to 100,000. In contrast, we propose a tokenizer with linear-time complexity, which enables the efficient embedding of a flexible number of tokens. Accordingly, a new 3D reconstruction target is proposed to cooperate with our 3D tokenizer. Our method achieves state-of-the-art performance in 3D classification, few-shot learning, and 3D segmentation. Code is available at https://github.com/XuechaoChen/P3P-MAE.
Gödel Incompleteness Theorem for PAC Learnable Theory from the view of complexity measurement
Venue: arXiv cs.LO 2025
Year: 2025

Different from the view that information is objective reality, this paper adopts the idea that all information needs to be compiled by the interpreter before it can be observed. From the traditional complexity definition, this paper defines the complexity under "the interpreter", which means that heuristically finding the best interpreter is equivalent to using PAC to find the most suitable interpreter. Then we generalize the observation process to the formal system with functors, in which we give concrete proof of the generalized Gödel incompleteness theorem which indicates that there are some objects that are PAC-learnable, but the best interpreter is not found among the alternative interpreters. A strong enough machine algorithm cannot be interpretable in the face of any object. There are always objects that make a strong enough machine learning algorithm uninterpretable, which puts an upper bound on the generalization ability of strong AI.
Order-Preserving Dimension Reduction for Multimodal Semantic Embedding
Venue: arXiv cs.LG 2025
Year: 2025

Searching for the $k$-nearest neighbors (KNN) in multimodal data retrieval is computationally expensive, particularly due to the inherent difficulty in comparing similarity measures across different modalities. Recent advances in multimodal machine learning address this issue by mapping data into a shared embedding space; however, the high dimensionality of these embeddings (hundreds to thousands of dimensions) presents a challenge for time-sensitive vision applications. This work proposes Order-Preserving Dimension Reduction (OPDR), aiming to reduce the dimensionality of embeddings while preserving the ranking of KNN in the lower-dimensional space. One notable component of OPDR is a new measure function to quantify KNN quality as a global metric, based on which we derive a closed-form map between target dimensionality and key contextual parameters. We have integrated OPDR with multiple state-of-the-art dimension-reduction techniques, distance functions, and embedding models; experiments on a variety of multimodal datasets demonstrate that OPDR effectively retains recall high accuracy while significantly reducing computational costs.
Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy plane
Venue: arXiv cs.CV 2025
Year: 2025

The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Behance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time.
Understanding Generative AI Content with Embedding Models
Venue: arXiv cs.LG 2025
Year: 2025

Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representations based on domain expertise, deep neural networks (DNNs) now offer a radically different approach. DNNs implicitly engineer features by transforming their input data into hidden feature vectors called embeddings. For embedding vectors produced by foundation models -- which are trained to be useful across many contexts -- we demonstrate that simple and well-studied dimensionality-reduction techniques such as Principal Component Analysis uncover inherent heterogeneity in input data concordant with human-understandable explanations. Of the many applications for this framework, we find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI).
Private Means and the Curious Incident of the Free Lunch
Venue: arXiv cs.CR 2025
Year: 2025

We show that the most well-known and fundamental building blocks of DP implementations -- sum, mean, count (and many other linear queries) -- can be released with substantially reduced noise for the same privacy guarantee. We achieve this by projecting individual data with worst-case sensitivity $R$ onto a simplex where all data now has a constant norm $R$. In this simplex, additional ``free'' queries can be run that are already covered by the privacy-loss of the original budgeted query, and which algebraically give additional estimates of counts or sums.
How Well Do Large Language Models Serve as End-to-End Secure Code Agents for Python?
Venue: arXiv cs.SE 2025
Year: 2025

The rapid advancement of large language models (LLMs) such as GPT-4 has revolutionized the landscape of software engineering, positioning these models at the core of modern development practices. As we anticipate these models to evolve into the primary and trustworthy tools used in software development, ensuring the security of the code they produce becomes paramount. How well can LLMs serve as end-to-end secure code producers? This paper presents a systematic investigation into LLMs' inherent potential to generate code with fewer vulnerabilities. Specifically, We studied GPT-3.5 and GPT-4's capability to identify and repair vulnerabilities in the code generated by four popular LLMs including themselves (GPT-3.5, GPT-4, Code Llama, and CodeGeeX2). By manually or automatically reviewing 4,900 pieces of code, our study reveals that: (1) large language models lack awareness of scenario-relevant security risks, which leads to the generation of over 75% vulnerable code on the SecurityEval benchmark; (2) LLMs such as GPT-3.5 and GPT-4 are unable to precisely identify vulnerabilities in the code they generated; (3) GPT-3.5 and GPT-4 can achieve 33.2%~59.6% success rates in repairing the insecure code produced by the 4 LLMs, but they both perform poorly when repairing self-produced code, indicating self-repair "blind spots". To address the limitation of a single round of repair, we developed a lightweight tool that prompts LLMs to construct safer source code through an iterative repair procedure based on the insights gained from our study. Experiments show that assisted by semantic analysis engines, our tool significantly improves the success rates of repair to 65.9%~85.5%.