OpenRaiser/Intern-Atlas huggingface dataset:
MAMAT: 3D Mamba-Based Atmospheric Turbulence Removal and its Object Detection Capability
Venue: arXiv cs.CV 2025
Year: 2025

Restoration and enhancement are essential for improving the quality of videos captured under atmospheric turbulence conditions, aiding visualization, object detection, classification, and tracking in surveillance systems. In this paper, we introduce a novel Mamba-based method, the 3D Mamba-Based Atmospheric Turbulence Removal (MAMAT), which employs a dual-module strategy to mitigate these distortions. The first module utilizes deformable 3D convolutions for non-rigid registration to minimize spatial shifts, while the second module enhances contrast and detail. Leveraging the advanced capabilities of the 3D Mamba architecture, experimental results demonstrate that MAMAT outperforms state-of-the-art learning-based methods, achieving up to a 3\% improvement in visual quality and a 15\% boost in object detection. It not only enhances visualization but also significantly improves object detection accuracy, bridging the gap between visual restoration and the effectiveness of surveillance applications.
Normalized Matching Transformer
Venue: arXiv cs.CV 2025
Year: 2025

We present a new state of the art approach for sparse keypoint matching between pairs of images. Our method consists of a fully deep learning based approach combining a visual backbone coupled with a SplineCNN graph neural network for feature processing and a normalized transformer decoder for decoding keypoint correspondences together with the Sinkhorn algorithm. Our method is trained using a contrastive and a hyperspherical loss for better feature representations. We additionally use data augmentation during training. This comparatively simple architecture combining extensive normalization and advanced losses outperforms current state of the art approaches on PascalVOC and SPair-71k datasets by $5.1\%$ and $2.2\%$ respectively compared to BBGM, ASAR, COMMON and GMTR while training for at least $1.7x$ fewer epochs.
Energy-Aware LLMs: A step towards sustainable AI for downstream applications
Venue: arXiv cs.PF 2025
Year: 2025

Advanced Large Language Models (LLMs) have revolutionized various fields, including communication networks, sparking an innovation wave that has led to new applications and services, and significantly enhanced solution schemes. Despite all these impressive developments, most LLMs typically require huge computational resources, resulting in terribly high energy consumption. Thus, this research study proposes an end-to-end pipeline that investigates the trade-off between energy efficiency and model performance for an LLM during fault ticket analysis in communication networks. It further evaluates the pipeline performance using two real-world datasets for the tasks of root cause analysis and response feedback in a communication network. Our results show that an appropriate combination of quantization and pruning techniques is able to reduce energy consumption while significantly improving model performance.
A Roadmap Towards Improving Multi-Agent Reinforcement Learning With Causal Discovery And Inference
Venue: arXiv cs.LG 2025
Year: 2025

Causal reasoning is increasingly used in Reinforcement Learning (RL) to improve the learning process in several dimensions: efficacy of learned policies, efficiency of convergence, generalisation capabilities, safety and interpretability of behaviour. However, applications of causal reasoning to Multi-Agent RL (MARL) are still mostly unexplored. In this paper, we take the first step in investigating the opportunities and challenges of applying causal reasoning in MARL. We measure the impact of a simple form of causal augmentation in state-of-the-art MARL scenarios increasingly requiring cooperation, and with state-of-the-art MARL algorithms exploiting various degrees of collaboration between agents. Then, we discuss the positive as well as negative results achieved, giving us the chance to outline the areas where further research may help to successfully transfer causal RL to the multi-agent setting.
CRDT-Based Game State Synchronization in Peer-to-Peer VR
Venue: arXiv cs.DC 2025
Year: 2025

Virtual presence demands ultra-low latency, a factor that centralized architectures, by their nature, cannot minimize. Local peer-to-peer architectures offer a compelling alternative, but also pose unique challenges in terms of network infrastructure. This paper introduces a prototype leveraging Conflict-Free Replicated Data Types (CRDTs) to enable real-time collaboration in a shared virtual environment. Using this prototype, we investigate latency, synchronization, and the challenges of decentralized coordination in dynamic non-Byzantine contexts. We aim to question prevailing assumptions about decentralized architectures and explore the practical potential of P2P in advancing virtual presence. This work challenges the constraints of mediated networks and highlights the potential of decentralized architectures to redefine collaboration and interaction in digital spaces.
NVBleed: Covert and Side-Channel Attacks on NVIDIA Multi-GPU Interconnect
Venue: arXiv cs.CR 2025
Year: 2025

Multi-GPU systems are becoming increasingly important in highperformance computing (HPC) and cloud infrastructure, providing acceleration for data-intensive applications, including machine learning workloads. These systems consist of multiple GPUs interconnected through high-speed networking links such as NVIDIA's NVLink. In this work, we explore whether the interconnect on such systems can offer a novel source of leakage, enabling new forms of covert and side-channel attacks. Specifically, we reverse engineer the operations of NVlink and identify two primary sources of leakage: timing variations due to contention and accessible performance counters that disclose communication patterns. The leakage is visible remotely and even across VM instances in the cloud, enabling potentially dangerous attacks. Building on these observations, we develop two types of covert-channel attacks across two GPUs, achieving a bandwidth of over 70 Kbps with an error rate of 4.78% for the contention channel. We develop two end-to-end crossGPU side-channel attacks: application fingerprinting (including 18 high-performance computing and deep learning applications) and 3D graphics character identification within Blender, a multi-GPU rendering application. These attacks are highly effective, achieving F1 scores of up to 97.78% and 91.56%, respectively. We also discover that leakage surprisingly occurs across Virtual Machines on the Google Cloud Platform (GCP) and demonstrate a side-channel attack on Blender, achieving F1 scores exceeding 88%. We also explore potential defenses such as managing access to counters and reducing the resolution of the clock to mitigate the two sources of leakage.
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
Venue: arXiv cs.LG 2025
Year: 2025

Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic molecule identification. However, existing methods often struggle to capture long-range dependencies efficiently and neglect the spectral information. Recently, selective state space models, particularly Mamba, have demonstrated remarkable advantages in capturing long-range dependencies with linear complexity and a selection mechanism. Motivated by their success across various domains, we propose GLADMamba, a novel framework that adapts the selective state space model into UGLAD field. We design a View-Fused Mamba (VFM) module with a Mamba-Transformer-style architecture to efficiently fuse information from different graph views with a selective state mechanism. We also design a Spectrum-Guided Mamba (SGM) module with a Mamba-Transformer-style architecture to leverage the Rayleigh quotient to guide the embedding refinement process, considering the spectral information for UGLAD. GLADMamba can dynamically focus on anomaly-related information while discarding irrelevant information for anomaly detection. To the best of our knowledge, this is the first work to introduce Mamba and explicit spectral information to UGLAD. Extensive experiments on 12 real-world datasets demonstrate that GLADMamba outperforms existing state-of-the-art methods, achieving superior performance in UGLAD. The code is available at https://github.com/Yali-Fu/GLADMamba.
VSAG: An Optimized Search Framework for Graph-based Approximate Nearest Neighbor Search
Venue: arXiv cs.DB 2025
Year: 2025

Approximate nearest neighbor search (ANNS) is a fundamental problem in vector databases and AI infrastructures. Recent graph-based ANNS algorithms have achieved high search accuracy with practical efficiency. Despite the advancements, these algorithms still face performance bottlenecks in production, due to the random memory access patterns of graph-based search and the high computational overheads of vector distance. In addition, the performance of a graph-based ANNS algorithm is highly sensitive to parameters, while selecting the optimal parameters is cost-prohibitive, e.g., manual tuning requires repeatedly re-building the index. This paper introduces VSAG, an open-source framework that aims to enhance the in production performance of graph-based ANNS algorithms. VSAG has been deployed at scale in the services of Ant Group, and it incorporates three key optimizations: (i) efficient memory access: it reduces L3 cache misses with pre-fetching and cache-friendly vector organization; (ii) automated parameter tuning: it automatically selects performance-optimal parameters without requiring index rebuilding; (iii) efficient distance computation: it leverages modern hardware, scalar quantization, and smartly switches to low-precision representation to dramatically reduce the distance computation costs. We evaluate VSAG on real-world datasets. The experimental results show that VSAG achieves the state-of-the-art performance and provides up to 4x speedup over HNSWlib (an industry-standard library) while ensuring the same accuracy.
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA
Venue: arXiv cs.CL 2025
Year: 2025

To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to provide factual medical knowledge. However, beyond general medical knowledge from open-ended datasets, clinical case-based knowledge is also critical for effective medical reasoning, as it provides context grounded in real-world patient experiences.Motivated by this, we propose Experience Retrieval-Augmentation ExpRAG framework based on Electronic Health Record(EHR), aiming to offer the relevant context from other patients' discharge reports. ExpRAG performs retrieval through a coarse-to-fine process, utilizing an EHR-based report ranker to efficiently identify similar patients, followed by an experience retriever to extract task-relevant content for enhanced medical reasoning.To evaluate ExpRAG, we introduce DischargeQA, a clinical QA dataset with 1,280 discharge-related questions across diagnosis, medication, and instruction tasks. Each problem is generated using EHR data to ensure realistic and challenging scenarios. Experimental results demonstrate that ExpRAG consistently outperforms a text-based ranker, achieving an average relative improvement of 5.2%, highlighting the importance of case-based knowledge for medical reasoning.
An Empirical Study of the Role of Incompleteness and Ambiguity in Interactions with Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Natural language as a medium for human-computer interaction has long been anticipated, has been undergoing a sea-change with the advent of Large Language Models (LLMs) with startling capacities for processing and generating language. Many of us now treat LLMs as modern-day oracles, asking it almost any kind of question. Unlike its Delphic predecessor, consulting an LLM does not have to be a single-turn activity (ask a question, receive an answer, leave); and -- also unlike the Pythia -- it is widely acknowledged that answers from LLMs can be improved with additional context. In this paper, we aim to study when we need multi-turn interactions with LLMs to successfully get a question answered; or conclude that a question is unanswerable. We present a neural symbolic framework that models the interactions between human and LLM agents. Through the proposed framework, we define incompleteness and ambiguity in the questions as properties deducible from the messages exchanged in the interaction, and provide results from benchmark problems, in which the answer-correctness is shown to depend on whether or not questions demonstrate the presence of incompleteness or ambiguity (according to the properties we identify). Our results show multi-turn interactions are usually required for datasets which have a high proportion of incompleteness or ambiguous questions; and that that increasing interaction length has the effect of reducing incompleteness or ambiguity. The results also suggest that our measures of incompleteness and ambiguity can be useful tools for characterising interactions with an LLM on question-answeringproblems
Won: Establishing Best Practices for Korean Financial NLP
Venue: arXiv cs.CL 2025
Year: 2025

In this work, we present the first open leaderboard for evaluating Korean large language models focused on finance. Operated for about eight weeks, the leaderboard evaluated 1,119 submissions on a closed benchmark covering five MCQA categories: finance and accounting, stock price prediction, domestic company analysis, financial markets, and financial agent tasks and one open-ended qa task. Building on insights from these evaluations, we release an open instruction dataset of 80k instances and summarize widely used training strategies observed among top-performing models. Finally, we introduce Won, a fully open and transparent LLM built using these best practices. We hope our contributions help advance the development of better and safer financial LLMs for Korean and other languages.
Interpersonal Trust Among Students in Virtual Learning Environments: A Comprehensive Review
Venue: arXiv cs.CY 2025
Year: 2025

Interpersonal trust is recognized as one of the pillars of collaboration and successful learning among students in virtual learning environments (VLEs). This systematic mapping study investigates attributes, phases, and features that support interpersonal trust among students in VLEs. Analyzing 46 articles, we identified 37 attributes that influence phases of acquiring and losing trust, categorized into four themes: Ability, Integrity, Affinity, and Non-Personal Factors. Attributes such as collaborative and ethical behavior, academic skills, and higher grades are often used to select peers, mainly through recommendation systems and user profiles. To organize our findings, we elaborated two conceptual maps describing the main characteristics of trust definitions and the attributes classification by phases and themes.
Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning
Venue: arXiv cs.CV 2025
Year: 2025

Large Vision-Language Models (LVLMs) typically follow a two-stage training paradigm-pretraining and supervised fine-tuning. Recently, preference optimization, derived from the language domain, has emerged as an effective post-training reinforcement strategy to enhance capabilities of LVLMs. However, constructing high-quality human-annotated preference data and developing robust reward models to mimic these preferences are both costly and challenging. Motivated by this observation, we propose Vision-R1, a novel vision-guided R1-like reinforcement learning algorithm for LVLMs that rewards models with definitive vision feedback. It only leverages curated instruction data, eliminating the need for specialized reward models and handcrafted preference datasets. We incorporate a criterion-driven reward function that further integrates multi-dimensional feedback to evaluate model completions comprehensively based on the vision task logic. Furthermore, we introduce a progressive rule refinement strategy that dynamically adjusts the reward criteria during training, enabling continuous model improvement and mitigating reward hacking. Extensive experiments on both in-distribution and out-of-distribution benchmarks demonstrate that fine-tuning the 7B LVLMs with Vision-R1 achieves consistent performance gains, with even up to 50% improvement and surpassing the state-of-the-art 10x size model.
Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
Venue: arXiv cs.CV 2025
Year: 2025

Retrieval-augmented generation (RAG) has emerged as a pivotal technique in artificial intelligence (AI), particularly in enhancing the capabilities of large language models (LLMs) by enabling access to external, reliable, and up-to-date knowledge sources. In the context of AI-Generated Content (AIGC), RAG has proven invaluable by augmenting model outputs with supplementary, relevant information, thus improving their quality. Recently, the potential of RAG has extended beyond natural language processing, with emerging methods integrating retrieval-augmented strategies into the computer vision (CV) domain. These approaches aim to address the limitations of relying solely on internal model knowledge by incorporating authoritative external knowledge bases, thereby improving both the understanding and generation capabilities of vision models. This survey provides a comprehensive review of the current state of retrieval-augmented techniques in CV, focusing on two main areas: (I) visual understanding and (II) visual generation. In the realm of visual understanding, we systematically review tasks ranging from basic image recognition to complex applications such as medical report generation and multimodal question answering. For visual content generation, we examine the application of RAG in tasks related to image, video, and 3D generation. Furthermore, we explore recent advancements in RAG for embodied AI, with a particular focus on applications in planning, task execution, multimodal perception, interaction, and specialized domains. Given that the integration of retrieval-augmented techniques in CV is still in its early stages, we also highlight the key limitations of current approaches and propose future research directions to drive the development of this promising area.
Formal Verification of Parameterized Systems based on Induction
Venue: arXiv cs.LO 2025
Year: 2025

Parameterized systems play a crucial role in the computer field, and their security is of great significance. Formal verification of parameterized protocols is especially challenging due to its "parameterized" feature, which brings complexity and undecidability. Existing automated parameterized verification methods have limitations, such as facing difficulties in automatically deriving parameterized invariants constrained by mixed Forall and Exists quantifiers, or having challenges in completing the parameterized verification of large and complex protocols. This paper proposes a formal verification framework for parameterized systems based on induction, named wiseParaverifier. It starts from small concretizations of protocols, analyzes inductive counterexamples, and constructs counterexample formulas to guide the entire process of parameterized verification. It also presents a heuristic Generalize method to quickly find auxiliary invariants, a method for promoting complex mixed quantifiers and merging parameterized invariants, and uses symmetric reduction ideas to accelerate the verification process. Experimental results show that wiseParaverifier can successfully complete automatic inductive verification on 7 cache coherence protocols and 10 distributed protocols. It has strong verification capabilities and migration capabilities, and can provide concise and readable verification results, which is helpful for learners to understand protocol behaviors.
Reinforcement Learning-based Self-adaptive Differential Evolution through Automated Landscape Feature Learning
Venue: arXiv cs.NE 2025
Year: 2025

Recently, Meta-Black-Box-Optimization (MetaBBO) methods significantly enhance the performance of traditional black-box optimizers through meta-learning flexible and generalizable meta-level policies that excel in dynamic algorithm configuration (DAC) tasks within the low-level optimization, reducing the expertise required to adapt optimizers for novel optimization tasks. Though promising, existing MetaBBO methods heavily rely on human-crafted feature extraction approach to secure learning effectiveness. To address this issue, this paper introduces a novel MetaBBO method that supports automated feature learning during the meta-learning process, termed as RLDE-AFL, which integrates a learnable feature extraction module into a reinforcement learning-based DE method to learn both the feature encoding and meta-level policy. Specifically, we design an attention-based neural network with mantissa-exponent based embedding to transform the solution populations and corresponding objective values during the low-level optimization into expressive landscape features. We further incorporate a comprehensive algorithm configuration space including diverse DE operators into a reinforcement learning-aided DAC paradigm to unleash the behavior diversity and performance of the proposed RLDE-AFL. Extensive benchmark results show that co-training the proposed feature learning module and DAC policy contributes to the superior optimization performance of RLDE-AFL to several advanced DE methods and recent MetaBBO baselines over both synthetic and realistic BBO scenarios. The source codes of RLDE-AFL are available at https://github.com/GMC-DRL/RLDE-AFL.
Accurate Peak Detection in Multimodal Optimization via Approximated Landscape Learning
Venue: arXiv cs.NE 2025
Year: 2025

Detecting potential optimal peak areas and locating the accurate peaks in these areas are two major challenges in Multimodal Optimization problems (MMOPs). To address them, much efforts have been spent on developing novel searching operators, niching strategies and multi-objective problem transformation pipelines. Though promising, existing approaches more or less overlook the potential usage of landscape knowledge. In this paper, we propose a novel optimization framework tailored for MMOPs, termed as APDMMO, which facilitates peak detection via fully leveraging the landscape knowledge and hence capable of providing strong optimization performance on MMOPs. Specifically, we first design a novel surrogate landscape model which ensembles a group of non-linear activation units to improve the regression accuracy on diverse MMOPs. Then we propose a free-of-trial peak detection method which efficiently locates potential peak areas through back-propagation on the learned surrogate landscape model. Based on the detected peak areas, we employ SEP-CMAES for local search within these areas in parallel to further improve the accuracy of the found optima. Extensive benchmarking results demonstrate that APDMMO outperforms several up-to-date baselines. Further ablation studies verify the effectiveness of the proposed novel designs. The source-code is available at ~\href{}{https://github.com/GMC-DRL/APDMMO}.