OpenRaiser/Intern-Atlas huggingface dataset:
I Know What You Said: Unveiling Hardware Cache Side-Channels in Local Large Language Model Inference
Venue: arXiv cs.CR 2025
Year: 2025

Large Language Models (LLMs) that can be deployed locally have recently gained popularity for privacy-sensitive tasks, with companies such as Meta, Google, and Intel playing significant roles in their development. However, the security of local LLMs through the lens of hardware cache side-channels remains unexplored. In this paper, we unveil novel side-channel vulnerabilities in local LLM inference: token value and token position leakage, which can expose both the victim's input and output text, thereby compromising user privacy. Specifically, we found that adversaries can infer the token values from the cache access patterns of the token embedding operation, and deduce the token positions from the timing of autoregressive decoding phases. To demonstrate the potential of these leaks, we design a novel eavesdropping attack framework targeting both open-source and proprietary LLM inference systems. The attack framework does not directly interact with the victim's LLM and can be executed without privilege. We evaluate the attack on a range of practical local LLM deployments (e.g., Llama, Falcon, and Gemma), and the results show that our attack achieves promising accuracy. The restored output and input text have an average edit distance of 5.2% and 17.3% to the ground truth, respectively. Furthermore, the reconstructed texts achieve average cosine similarity scores of 98.7% (input) and 98.0% (output).
Symbolic Rule Extraction from Attention-Guided Sparse Representations in Vision Transformers
Venue: arXiv cs.CV 2025
Year: 2025

Recent neuro-symbolic approaches have successfully extracted symbolic rule-sets from CNN-based models to enhance interpretability. However, applying similar techniques to Vision Transformers (ViTs) remains challenging due to their lack of modular concept detectors and reliance on global self-attention mechanisms. We propose a framework for symbolic rule extraction from ViTs by introducing a sparse concept layer inspired by Sparse Autoencoders (SAEs). This linear layer operates on attention-weighted patch representations and learns a disentangled, binarized representation in which individual neurons activate for high-level visual concepts. To encourage interpretability, we apply a combination of L1 sparsity, entropy minimization, and supervised contrastive loss. These binarized concept activations are used as input to the FOLD-SE-M algorithm, which generates a rule-set in the form of logic programs. Our method achieves a 5.14% better classification accuracy than the standard ViT while enabling symbolic reasoning. Crucially, the extracted rule-set is not merely post-hoc but acts as a logic-based decision layer that operates directly on the sparse concept representations. The resulting programs are concise and semantically meaningful. This work is the first to extract executable logic programs from ViTs using sparse symbolic representations. It bridges the gap between transformer-based vision models and symbolic logic programming, providing a step forward in interpretable and verifiable neuro-symbolic AI.
Learning Graph Representation of Agent Diffusers
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion-based generative models have significantly advanced text-to-image synthesis, demonstrating impressive text comprehension and zero-shot generalization. These models refine images from random noise based on textual prompts, with initial reliance on text input shifting towards enhanced visual fidelity over time. This transition suggests that static model parameters might not optimally address the distinct phases of generation. We introduce LGR-AD (Learning Graph Representation of Agent Diffusers), a novel multi-agent system designed to improve adaptability in dynamic computer vision tasks. LGR-AD models the generation process as a distributed system of interacting agents, each representing an expert sub-model. These agents dynamically adapt to varying conditions and collaborate through a graph neural network that encodes their relationships and performance metrics. Our approach employs a coordination mechanism based on top-$k$ maximum spanning trees, optimizing the generation process. Each agent's decision-making is guided by a meta-model that minimizes a novel loss function, balancing accuracy and diversity. Theoretical analysis and extensive empirical evaluations show that LGR-AD outperforms traditional diffusion models across various benchmarks, highlighting its potential for scalable and flexible solutions in complex image generation tasks. Code is available at: https://github.com/YousIA/LGR_AD
New Wide Locally Recoverable Codes with Unified Locality
Venue: arXiv cs.DC 2025
Year: 2025

Wide Locally Recoverable Codes (LRCs) have recently been proposed as a solution for achieving high reliability, good performance, and ultra-low storage cost in distributed storage systems. However, existing wide LRCs struggle to balance optimal fault tolerance and high availability during frequent system events. By analyzing the existing LRCs, we reveal three limitations in the LRC construction which lay behind the non-optimal overall performance from multiple perspectives, including non-minimum local recovery cost, non cluster-topology-aware data distribution, and non XOR-based local coding. Thanks to the flexible design space offered by the locality property of wide LRCs, we present UniLRC, which unifies locality considerations in code construction. UniLRC achieves the optimal fault tolerance while overcoming the revealed limitations. We implement UniLRC prototype and conduct comprehensive theoretical and system evaluations, showing significant improvements in reliability and performance over existing wide LRCs deployed in Google and Azure clusters.
"Explain, Don't Just Warn!" -- A Real-Time Framework for Generating Phishing Warnings with Contextual Cues
Venue: arXiv cs.CR 2025
Year: 2025

Anti-phishing tools typically display generic warnings that offer users limited explanation on why a website is considered malicious, which can prevent end-users from developing the mental models needed to recognize phishing cues on their own. This becomes especially problematic when these tools inevitably fail - particularly against evasive threats, and users are found to be ill-equipped to identify and avoid them independently. To address these limitations, we present PhishXplain (PXP), a real-time explainable phishing warning system designed to augment existing detection mechanisms. PXP empowers users by clearly articulating why a site is flagged as malicious, highlighting suspicious elements using a memory-efficient implementation of LLaMA 3.2. It utilizes a structured two-step prompt architecture to identify phishing features, generate contextual explanations, and render annotated screenshots that visually reinforce the warning. Longitudinally implementing PhishXplain over a month on 7,091 live phishing websites, we found that it can generate warnings for 94% of the sites, with a correctness of 96%. We also evaluated PhishXplain through a user study with 150 participants split into two groups: one received conventional, generic warnings, while the other interacted with PXP's explainable alerts. Participants who received the explainable warnings not only demonstrated a significantly better understanding of phishing indicators but also achieved higher accuracy in identifying phishing threats, even without any warning. Moreover, they reported greater satisfaction and trust in the warnings themselves. These improvements were especially pronounced among users with lower initial levels of cybersecurity proficiency and awareness. To encourage the adoption of this framework, we release PhishXplain as a browser extension.
Predictive Digital Twins for Thermal Management Using Machine Learning and Reduced-Order Models
Venue: arXiv cs.LG 2025
Year: 2025

Digital twins enable real-time simulation and prediction in engineering systems. This paper presents a novel framework for predictive digital twins of a headlamp heatsink, integrating physics-based reduced-order models (ROMs) from computational fluid dynamics (CFD) with supervised machine learning. A component-based ROM library, derived via proper orthogonal decomposition (POD), captures thermal dynamics efficiently. Machine learning models, including Decision Trees, k-Nearest Neighbors, Support Vector Regression (SVR), and Neural Networks, predict optimal ROM configurations, enabling rapid digital twin updates. The Neural Network achieves a mean absolute error (MAE) of 54.240, outperforming other models. Quantitative comparisons of predicted and original values demonstrate high accuracy. This scalable, interpretable framework advances thermal management in automotive systems, supporting robust design and predictive maintenance.
DP-TRAE: A Dual-Phase Merging Transferable Reversible Adversarial Example for Image Privacy Protection
Venue: arXiv cs.CR 2025
Year: 2025

In the field of digital security, Reversible Adversarial Examples (RAE) combine adversarial attacks with reversible data hiding techniques to effectively protect sensitive data and prevent unauthorized analysis by malicious Deep Neural Networks (DNNs). However, existing RAE techniques primarily focus on white-box attacks, lacking a comprehensive evaluation of their effectiveness in black-box scenarios. This limitation impedes their broader deployment in complex, dynamic environments. Further more, traditional black-box attacks are often characterized by poor transferability and high query costs, significantly limiting their practical applicability. To address these challenges, we propose the Dual-Phase Merging Transferable Reversible Attack method, which generates highly transferable initial adversarial perturbations in a white-box model and employs a memory augmented black-box strategy to effectively mislead target mod els. Experimental results demonstrate the superiority of our approach, achieving a 99.0% attack success rate and 100% recovery rate in black-box scenarios, highlighting its robustness in privacy protection. Moreover, we successfully implemented a black-box attack on a commercial model, further substantiating the potential of this approach for practical use.
Masked Subspace Clustering Methods
Venue: arXiv cs.LG 2025
Year: 2025

To further utilize the unsupervised features and pairwise information, we propose a general Bilevel Clustering Optimization (BCO) framework to improve the performance of clustering. And then we introduce three special cases on subspace clustering with two different types of masks. At first, we reformulate the original subspace clustering as a Basic Masked Subspace Clustering (BMSC), which reformulate the diagonal constraints to a hard mask. Then, we provide a General Masked Subspace Clustering (GMSC) method to integrate different clustering via a soft mask. Furthermore, based on BCO and GMSC, we induce a learnable soft mask and design a Recursive Masked Subspace Clustering (RMSC) method that can alternately update the affinity matrix and the soft mask. Numerical experiments show that our models obtain significant improvement compared with the baselines on several commonly used datasets, such as MNIST, USPS, ORL, COIL20 and COIL100.
Towards Human-Centric Autonomous Driving: A Fast-Slow Architecture Integrating Large Language Model Guidance with Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous driving has made significant strides through data-driven techniques, achieving robust performance in standardized tasks. However, existing methods frequently overlook user-specific preferences, offering limited scope for interaction and adaptation with users. To address these challenges, we propose a "fast-slow" decision-making framework that integrates a Large Language Model (LLM) for high-level instruction parsing with a Reinforcement Learning (RL) agent for low-level real-time decision. In this dual system, the LLM operates as the "slow" module, translating user directives into structured guidance, while the RL agent functions as the "fast" module, making time-critical maneuvers under stringent latency constraints. By decoupling high-level decision making from rapid control, our framework enables personalized user-centric operation while maintaining robust safety margins. Experimental evaluations across various driving scenarios demonstrate the effectiveness of our method. Compared to baseline algorithms, the proposed architecture not only reduces collision rates but also aligns driving behaviors more closely with user preferences, thereby achieving a human-centric mode. By integrating user guidance at the decision level and refining it with real-time control, our framework bridges the gap between individual passenger needs and the rigor required for safe, reliable driving in complex traffic environments.
FACET: Force-Adaptive Control via Impedance Reference Tracking for Legged Robots
Venue: arXiv cs.RO 2025
Year: 2025

Reinforcement learning (RL) has made significant strides in legged robot control, enabling locomotion across diverse terrains and complex loco-manipulation capabilities. However, the commonly used position or velocity tracking-based objectives are agnostic to forces experienced by the robot, leading to stiff and potentially dangerous behaviors and poor control during forceful interactions. To address this limitation, we present \emph{Force-Adaptive Control via Impedance Reference Tracking} (FACET). Inspired by impedance control, we use RL to train a control policy to imitate a virtual mass-spring-damper system, allowing fine-grained control under external forces by manipulating the virtual spring. In simulation, we demonstrate that our quadruped robot achieves improved robustness to large impulses (up to 200 Ns) and exhibits controllable compliance, achieving an 80% reduction in collision impulse. The policy is deployed to a physical robot to showcase both compliance and the ability to engage with large forces by kinesthetic control and pulling payloads up to 2/3 of its weight. Further extension to a legged loco-manipulator and a humanoid shows the applicability of our method to more complex settings to enable whole-body compliance control. Project Website: https://facet.pages.dev/
A WSPD, Separator and Small Tree Cover for c-packed Graphs
Venue: arXiv cs.CG 2025
Year: 2025

The $c$-packedness property, proposed in 2010, is a geometric property that captures the spatial distribution of a set of edges. Despite the recent interest in $c$-packedness, its utility has so far been limited to Fréchet distance problems. An open problem is whether a wider variety of algorithmic and data structure problems can be solved efficiently under the $c$-packedness assumption, and more specifically, on $c$-packed graphs. In this paper, we prove two fundamental properties of $c$-packed graphs: that there exists a linear-size well-separated pair decomposition under the graph metric, and there exists a constant size balanced separator. We then apply these fundamental properties to obtain a small tree cover for the metric space and distance oracles under the shortest path metric. In particular, we obtain a tree cover of constant size, an exact distance oracle of near-linear size and an approximate distance oracle of linear size.
Incremental Analysis of Legacy Applications Using Knowledge Graphs for Application Modernization
Venue: arXiv cs.SE 2025
Year: 2025

Industries such as banking, telecom, and airlines - o6en have large so6ware systems that are several decades old. Many of these systems are written in old programming languages such as COBOL, PL/1, Assembler, etc. In many cases, the documentation is not updated, and those who developed/designed these systems are no longer around. Understanding these systems for either modernization or even regular maintenance has been a challenge. An extensive application may have natural boundaries based on its code dependencies and architecture. There are also other logical boundaries in an enterprise setting driven by business functions, data domains, etc. Due to these complications, the system architects generally plan their modernization across these logical boundaries in parts, thereby adopting an incremental approach for the modernization journey of the entire system. In this work, we present a so6ware system analysis tool that allows a subject ma=er expert (SME) or system architect to analyze a large so6ware system incrementally. We analyze the source code and other artifacts (such as data schema) to create a knowledge graph using a customizable ontology/schema. Entities and relations in our ontology can be defined for any combination of programming languages and platforms. Using this knowledge graph, the analyst can then define logical boundaries around dependent Entities (e.g. Programs, Transactions, Database Tables etc.). Our tool then presents different views showcasing the dependencies from the newly defined boundary to/from the other logical groups of the system. This exercise is repeated interactively to 1) Identify the Entities and groupings of interest for a modernization task and 2) Understand how a change in one part of the system may affect the other parts. To validate the efficacy of our tool, we provide an initial study of our system on two client applications.
Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence
Venue: arXiv cs.AI 2025
Year: 2025

The ultimate goal of artificial intelligence (AI) is to achieve Artificial General Intelligence (AGI). Embodied Artificial Intelligence (EAI), which involves intelligent systems with physical presence and real-time interaction with the environment, has emerged as a key research direction in pursuit of AGI. While advancements in deep learning, reinforcement learning, large-scale language models, and multimodal technologies have significantly contributed to the progress of EAI, most existing reviews focus on specific technologies or applications. A systematic overview, particularly one that explores the direct connection between EAI and AGI, remains scarce. This paper examines EAI as a foundational approach to AGI, systematically analyzing its four core modules: perception, intelligent decision-making, action, and feedback. We provide a detailed discussion of how each module contributes to the six core principles of AGI. Additionally, we discuss future trends, challenges, and research directions in EAI, emphasizing its potential as a cornerstone for AGI development. Our findings suggest that EAI's integration of dynamic learning and real-world interaction is essential for bridging the gap between narrow AI and AGI.
Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction
Venue: arXiv cs.CV 2025
Year: 2025

Monocular height estimation (MHE) from very-high-resolution (VHR) optical imagery remains challenging due to limited structural cues and the high cost and geographic constraints of conventional elevation data such as airborne LiDAR and multi-view stereo. Although recent MHE and monocular depth estimation (MDE) models show strong performance, their robustness under varied illumination and scene conditions is still limited. We introduce a fully automated correction pipeline that integrates sparse, imperfect global LiDAR measurements from ICESat-2 with deep learning predictions to enhance accuracy and stability. The workflow relies entirely on publicly available models and data and requires only a single georeferenced optical image to produce corrected height maps, enabling low-cost and globally scalable deployment. We also establish the first benchmark for this task, evaluating two random forest based approaches, four parameter efficient fine tuning methods, and full fine tuning. Experiments across six diverse regions at 0.5 m resolution (297 km2), covering the urban cores of Tokyo, Paris, and Sao Paulo as well as suburban and forested areas, show substantial gains. The best method reduces the MHE model's mean absolute error (MAE) by 30.9 percent and improves its F1HE score by 44.2 percent. For the MDE model, MAE improves by 24.1 percent and the F1HE score by 25.1 percent. These results validate the effectiveness of our correction pipeline and demonstrate how sparse global LiDAR can systematically strengthen both MHE and MDE models, enabling scalable and widely accessible 3D height mapping.
The Distracting Effect: Understanding Irrelevant Passages in RAG
Venue: arXiv cs.CL 2025
Year: 2025

A well-known issue with Retrieval Augmented Generation (RAG) is that retrieved passages that are irrelevant to the query sometimes distract the answer-generating LLM, causing it to provide an incorrect response. In this paper, we shed light on this core issue and formulate the distracting effect of a passage w.r.t. a query (and an LLM). We provide a quantifiable measure of the distracting effect of a passage and demonstrate its robustness across LLMs. Our research introduces novel methods for identifying and using hard distracting passages to improve RAG systems. By fine-tuning LLMs with these carefully selected distracting passages, we achieve up to a 7.5% increase in answering accuracy compared to counterparts fine-tuned on conventional RAG datasets. Our contribution is two-fold: first, we move beyond the simple binary classification of irrelevant passages as either completely unrelated vs. distracting, and second, we develop and analyze multiple methods for finding hard distracting passages. To our knowledge, no other research has provided such a comprehensive framework for identifying and utilizing hard distracting passages.
Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) are becoming increasingly popular in the medical domain, bridging the gap between medical images and clinical language. Existing VLMs demonstrate an impressive ability to comprehend medical images and text queries to generate detailed, descriptive diagnostic medical reports. However, hallucination--the tendency to generate descriptions that are inconsistent with the visual content--remains a significant issue in VLMs, with particularly severe implications in the medical field. To facilitate VLM research on gastrointestinal (GI) image analysis and study hallucination, we curate a multimodal image-text GI dataset: Gut-VLM. This dataset is created using a two-stage pipeline: first, descriptive medical reports of Kvasir-v2 images are generated using ChatGPT, which introduces some hallucinated or incorrect texts. In the second stage, medical experts systematically review these reports, and identify and correct potential inaccuracies to ensure high-quality, clinically reliable annotations. Unlike traditional datasets that contain only descriptive texts, our dataset also features tags identifying hallucinated sentences and their corresponding corrections. A common approach to reducing hallucination in VLM is to finetune the model on a small-scale, problem-specific dataset. However, we take a different strategy using our dataset. Instead of finetuning the VLM solely for generating textual reports, we finetune it to detect and correct hallucinations, an approach we call hallucination-aware finetuning. Our results show that this approach is better than simply finetuning for descriptive report generation. Additionally, we conduct an extensive evaluation of state-of-the-art VLMs across several metrics, establishing a benchmark. GitHub Repo: https://github.com/bhattarailab/Hallucination-Aware-VLM.