OpenRaiser/Intern-Atlas huggingface dataset:
Policy-Value Guided MDP-MCTS Framework for Cyber Kill-Chain Inference
Venue: arXiv cs.CR 2025
Year: 2025

Threat analysts routinely rely on natural-language reports that describe attacker actions without enumerating the full kill chain or the dependencies between phases, making automated reconstruction of ATT&CK consistent intrusion paths a difficult open problem. We propose a reasoning framework that infers complete seven-phase kill chains by coupling phase-conditioned semantic priors from Transformer models with a symbolic Markov Decision Process and an AlphaZero-style Monte Carlo Tree Search guided by a Policy-Value Network. The framework enforces semantic relevance, phase cohesion, and transition plausibility through a multi-objective reward function while allowing search to explore alternative interpretations of the CTI narrative. Applied to three real intrusions FIN6, APT24, and UNC1549 the approach yields kill chains that surpass Transformer baselines in semantic fidelity and operational coherence, and frequently align with expert-selected TTPs. Our results demonstrate that combining contextual embeddings with search-based decision-making offers a practical path toward automated, interpretable kill-chain reconstruction for cyber defense.
Explainable Action Form Assessment by Exploiting Multimodal Chain-of-Thoughts Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

Evaluating whether human action is standard or not and providing reasonable feedback to improve action standardization is very crucial but challenging in real-world scenarios. However, current video understanding methods are mainly concerned with what and where the action is, which is unable to meet the requirements. Meanwhile, most of the existing datasets lack the labels indicating the degree of action standardization, and the action quality assessment datasets lack explainability and detailed feedback. Therefore, we define a new Human Action Form Assessment (AFA) task, and introduce a new diverse dataset CoT-AFA, which contains a large scale of fitness and martial arts videos with multi-level annotations for comprehensive video analysis. We enrich the CoT-AFA dataset with a novel Chain-of-Thought explanation paradigm. Instead of offering isolated feedback, our explanations provide a complete reasoning process--from identifying an action step to analyzing its outcome and proposing a concrete solution. Furthermore, we propose a framework named Explainable Fitness Assessor, which can not only judge an action but also explain why and provide a solution. This framework employs two parallel processing streams and a dynamic gating mechanism to fuse visual and semantic information, thereby boosting its analytical capabilities. The experimental results demonstrate that our method has achieved improvements in explanation generation (e.g., +16.0% in CIDEr), action classification (+2.7% in accuracy) and quality assessment (+2.1% in accuracy), revealing great potential of CoT-AFA for future studies. Our dataset and source code is available at https://github.com/MICLAB-BUPT/EFA.
Cross-modal ultra-scale learning with tri-modalities of renal biopsy images for glomerular multi-disease auxiliary diagnosis
Venue: arXiv cs.CV 2025
Year: 2025

Constructing a multi-modal automatic classification model based on three types of renal biopsy images can assist pathologists in glomerular multi-disease identification. However, the substantial scale difference between transmission electron microscopy (TEM) image features at the nanoscale and optical microscopy (OM) or immunofluorescence microscopy (IM) images at the microscale poses a challenge for existing multi-modal and multi-scale models in achieving effective feature fusion and improving classification accuracy. To address this issue, we propose a cross-modal ultra-scale learning network (CMUS-Net) for the auxiliary diagnosis of multiple glomerular diseases. CMUS-Net utilizes multiple ultrastructural information to bridge the scale difference between nanometer and micrometer images. Specifically, we introduce a sparse multi-instance learning module to aggregate features from TEM images. Furthermore, we design a cross-modal scale attention module to facilitate feature interaction, enhancing pathological semantic information. Finally, multiple loss functions are combined, allowing the model to weigh the importance among different modalities and achieve precise classification of glomerular diseases. Our method follows the conventional process of renal biopsy pathology diagnosis and, for the first time, performs automatic classification of multiple glomerular diseases including IgA nephropathy (IgAN), membranous nephropathy (MN), and lupus nephritis (LN) based on images from three modalities and two scales. On an in-house dataset, CMUS-Net achieves an ACC of 95.37+/-2.41%, an AUC of 99.05+/-0.53%, and an F1-score of 95.32+/-2.41%. Extensive experiments demonstrate that CMUS-Net outperforms other well-known multi-modal or multi-scale methods and show its generalization capability in staging MN. Code is available at https://github.com/SMU-GL-Group/MultiModal_lkx/tree/main.
BEAT2AASIST model with layer fusion for ESDD 2026 Challenge
Venue: arXiv cs.SD 2025
Year: 2025

Recent advances in audio generation have increased the risk of realistic environmental sound manipulation, motivating the ESDD 2026 Challenge as the first large-scale benchmark for Environmental Sound Deepfake Detection (ESDD). We propose BEAT2AASIST which extends BEATs-AASIST by splitting BEATs-derived representations along frequency or channel dimension and processing them with dual AASIST branches. To enrich feature representations, we incorporate top-k transformer layer fusion using concatenation, CNN-gated, and SE-gated strategies. In addition, vocoder-based data augmentation is applied to improve robustness against unseen spoofing methods. Experimental results on the official test sets demonstrate that the proposed approach achieves competitive performance across the challenge tracks.
From NLG Evaluation to Modern Student Assessment in the Era of ChatGPT: The Great Misalignment Problem and Pedagogical Multi-Factor Assessment (P-MFA)
Venue: arXiv cs.CL 2025
Year: 2025

This paper explores the growing epistemic parallel between NLG evaluation and grading of students in a Finnish University. We argue that both domains are experiencing a Great Misalignment Problem. As students increasingly use tools like ChatGPT to produce sophisticated outputs, traditional assessment methods that focus on final products rather than learning processes have lost their validity. To address this, we introduce the Pedagogical Multi-Factor Assessment (P-MFA) model, a process-based, multi-evidence framework inspired by the logic of multi-factor authentication.
Sparse Principal Component Analysis with Energy Profile Dependent Sample Complexity
Venue: arXiv cs.IT 2025
Year: 2025

We study sparse principal component analysis in the high-dimensional, sample-limited regime, aiming to recover a leading component supported on a few coordinates. Despite extensive progress, most methods and analyses are tailored to the flat-spike case, offering little guidance when spike energy is unevenly distributed across the support. Motivated by this, we propose Spectral Energy Pursuit (SEP), an effective iterative scheme that repeatedly screens and reselects coordinates, with a sample complexity that adapts to the energy profile. We develop our framework around a structure function \(s(p)\) that quantifies how spike energy accumulates over its top \(p\) entries. We establish that SEP succeeds with a sample size of order \(\max_{1\le p\le k} p\,s^2(p)\,\log n\), which matches the classical \(k^2\log n\) sample complexity for flat spikes and improves toward the \(k\log n\) regime as the profile becomes more concentrated. As a lightweight post-processing, a single truncated power iteration is proven to enable the final estimator to attain a uniform statistical error bound. Empirical simulations across flat, power-law, and exponential signals validate that SEP adapts to profile structure without tuning and outperforms existing algorithms.
Label-consistent clustering for evolving data
Venue: arXiv cs.DS 2025
Year: 2025

Data analysis often involves an iterative process, where solutions must be continuously refined in response to new data. Typically, as new data becomes available, an existing solution must be updated to incorporate the latest information. In addition to seeking a high-quality solution for the task at hand, it is also crucial to ensure consistency by minimizing drastic changes from previous solutions. Applying this approach across many iterations, ensures that the solution evolves gradually and smoothly. In this paper, we study the above problem in the context of clustering, specifically focusing on the $k$-center problem. More precisely, we study the following problem: Given a set of points $X$, parameters $k$ and $b$, and a prior clustering solution $H$ for $X$, our goal is to compute a new solution $C$ for $X$, consisting of $k$ centers, which minimizes the clustering cost while introducing at most $b$ changes from $H$. We refer to this problem as label-consistent $k$-center, and we propose two constant-factor approximation algorithms for it. We complement our theoretical findings with an experimental evaluation demonstrating the effectiveness of our methods on real-world datasets.
Gaming the Arena: AI Model Evaluation and the Viral Capture of Attention
Venue: arXiv cs.CY 2025
Year: 2025

Innovation in artificial intelligence (AI) has always been dependent on technological infrastructures, from code repositories to computing hardware. Yet industry -- rather than universities -- has become increasingly influential in shaping AI innovation. As generative forms of AI powered by large language models (LLMs) have driven the breakout of AI into the wider world, the AI community has sought to develop new methods for independently evaluating the performance of AI models. How best, in other words, to compare the performance of AI models against other AI models -- and how best to account for new models launched on nearly a daily basis? Building on recent work in media studies, STS, and computer science on benchmarking and the practices of AI evaluation, I examine the rise of so-called 'arenas' in which AI models are evaluated with reference to gladiatorial-style 'battles'. Through a technography of a leading user-driven AI model evaluation platform, LMArena, I consider five themes central to the emerging 'arena-ization' of AI innovation. Accordingly, I argue that the arena-ization is being powered by a 'viral' desire to capture attention both in, and outside of, the AI community, critical to the scaling and commercialization of AI products. In the discussion, I reflect on the implications of 'arena gaming', a phenomenon through which model developers hope to capture attention.
Heterogeneous Model Alignment in Digital Twin
Venue: arXiv cs.SE 2025
Year: 2025

Digital twin (DT) technology integrates heterogeneous data and models, along with semantic technologies to create multi-layered digital representation of physical systems. DTs enable monitoring, simulation, prediction, and optimization to enhance decision making and operational efficiency. A key challenge in multi-layered, model-driven DTs is aligning heterogeneous models across abstraction layers, which can lead to semantic mismatches, inconsistencies, and synchronization issues. Existing methods, relying on static mappings and manual updates, are often inflexible, error-prone, and risk compromising data integrity. To address these limitations, we present a heterogeneous model alignment approach for multi-layered, model-driven DTs. The framework incorporates a flexibility mechanism that allows metamodels to adapt and interconnect seamlessly while maintaining semantic coherence across abstraction layers. It integrates: (i) adaptive conformance mechanisms that link metamodels with evolving models and (ii) a large language model (LLM) validated alignment process that grounds metamodels in domain knowledge, ensuring structural fidelity and conceptual consistency throughout the DT lifecycle. This approach automates semantic correspondences discovery, minimizes manual mapping, and enhances scalability across diverse model types. We illustrate the approach using air quality use case and validate its performance using different test cases from Ontology Alignment Evaluation Initiative (OAEI) tracks.
Automated Motion Artifact Check for MRI (AutoMAC-MRI): An Interpretable Framework for Motion Artifact Detection and Severity Assessment
Venue: arXiv cs.CV 2025
Year: 2025

Motion artifacts degrade MRI image quality and increase patient recalls. Existing automated quality assessment methods are largely limited to binary decisions and provide little interpretability. We introduce AutoMAC-MRI, an explainable framework for grading motion artifacts across heterogeneous MR contrasts and orientations. The approach uses supervised contrastive learning to learn a discriminative representation of motion severity. Within this feature space, we compute grade-specific affinity scores that quantify an image's proximity to each motion grade, thereby making grade assignments transparent and interpretable. We evaluate AutoMAC-MRI on more than 5000 expert-annotated brain MRI slices spanning multiple contrasts and views. Experiments assessing affinity scores against expert labels show that the scores align well with expert judgment, supporting their use as an interpretable measure of motion severity. By coupling accurate grade detection with per-grade affinity scoring, AutoMAC-MRI enables inline MRI quality control, with the potential to reduce unnecessary rescans and improve workflow efficiency.
Joint Activity Detection and Channel Estimation For Fluid Antenna System Exploiting Geographical and Angular Information
Venue: arXiv cs.IT 2025
Year: 2025

The fluid antenna system (FAS) refers to a family of reconfigurable antenna technologies that provide substantial spatial gains within a compact, predefined small space, thereby offering extensive degrees of freedom in the physical layer for future communication networks. The acquisition of channel state information (CSI) is critical, as it determines the placement of ports/antennas, which directly impacts FAS-based optimization. Although various channel estimation methods have been developed, significant flaws persist. For instance, the performance of greedy-based algorithms is heavily influenced by signal assumptions, and current model-free methods are infeasible due to prohibitively high computational complexity issue. Consequently, there is a pressing need for a well-balanced solution that exhibits flexibility, feasibility, and low complexity to support massive connectivity in FAS. In this work, we propose methods based on approximate message passing (AMP) integrated with adaptive expectation maximization (EM). The EM-AMP framework uniquely enables efficient large matrix computations with adaptive learning capabilities, independent of prior knowledge of the model or parameters within potential distributions, making it a robust candidate for FAS networks. We introduce two variants of the EM-AMP framework that leverage geographical and angular features in a FAS network. These proposed algorithms demonstrate improved estimation precision, fast convergence, and low computational complexity in large activity regions. Additionally, we analytically elucidate the reasons behind the inherent performance floor of greedy-based methods and highlight the critical role of angular information in algorithm design. Extensive numerical results validate the promising efficacy of the proposed algorithm designs and the derived analytical findings.
Revisiting Task-Oriented Dataset Search in the Era of Large Language Models: Challenges, Benchmark, and Solution
Venue: arXiv cs.DB 2025
Year: 2025

The search for suitable datasets is the critical "first step" in data-driven research, but it remains a great challenge. Researchers often need to search for datasets based on high-level task descriptions. However, existing search systems struggle with this task due to ambiguous user intent, task-to-dataset mapping and benchmark gaps, and entity ambiguity. To address these challenges, we introduce KATS, a novel end-to-end system for task-oriented dataset search from unstructured scientific literature. KATS consists of two key components, i.e., offline knowledge base construction and online query processing. The sophisticated offline pipeline automatically constructs a high-quality, dynamically updatable task-dataset knowledge graph by employing a collaborative multi-agent framework for information extraction, thereby filling the task-to-dataset mapping gap. To further address the challenge of entity ambiguity, a unique semantic-based mechanism is used for task entity linking and dataset entity resolution. For online retrieval, KATS utilizes a specialized hybrid query engine that combines vector search with graph-based ranking to generate highly relevant results. Additionally, we introduce CS-TDS, a tailored benchmark suite for evaluating task-oriented dataset search systems, addressing the critical gap in standardized evaluation. Experiments on our benchmark suite show that KATS significantly outperforms state-of-the-art retrieval-augmented generation frameworks in both effectiveness and efficiency, providing a robust blueprint for the next generation of dataset discovery systems.
SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis
Venue: arXiv cs.CV 2025
Year: 2025

We propose a novel dataset that has been specifically designed for 3D semantic segmentation of bridges and the domain gap analysis caused by varying sensors. This addresses a critical need in the field of infrastructure inspection and maintenance, which is essential for modern society. The dataset comprises high-resolution 3D scans of a diverse range of bridge structures from various countries, with detailed semantic labels provided for each. Our initial objective is to facilitate accurate and automated segmentation of bridge components, thereby advancing the structural health monitoring practice. To evaluate the effectiveness of existing 3D deep learning models on this novel dataset, we conduct a comprehensive analysis of three distinct state-of-the-art architectures. Furthermore, we present data acquired through diverse sensors to quantify the domain gap resulting from sensor variations. Our findings indicate that all architectures demonstrate robust performance on the specified task. However, the domain gap can potentially lead to a decline in the performance of up to 11.4% mIoU.
SCOPE: Prompt Evolution for Enhancing Agent Effectiveness
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts. However, a critical bottleneck remains: while agents have access to this context, their static prompts lack the mechanisms to manage it effectively, leading to recurring Corrective and Enhancement failures. To address this capability gap, we introduce \textbf{SCOPE} (Self-evolving Context Optimization via Prompt Evolution). SCOPE frames context management as an \textit{online optimization} problem, synthesizing guidelines from execution traces to automatically evolve the agent's prompt. We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles). Furthermore, we introduce Perspective-Driven Exploration to maximize strategy coverage, increasing the likelihood that the agent has the correct strategy for any given task. Experiments on the HLE benchmark show that SCOPE improves task success rates from 14.23\% to 38.64\% without human intervention. We make our code publicly available at https://github.com/JarvisPei/SCOPE.
MiVLA: Towards Generalizable Vision-Language-Action Model with Human-Robot Mutual Imitation Pre-training
Venue: arXiv cs.RO 2025
Year: 2025

While leveraging abundant human videos and simulated robot data poses a scalable solution to the scarcity of real-world robot data, the generalization capability of existing vision-language-action models (VLAs) remains limited by mismatches in camera views, visual appearance, and embodiment morphologies. To overcome this limitation, we propose MiVLA, a generalizable VLA empowered by human-robot mutual imitation pre-training, which leverages inherent behavioral similarity between human hands and robotic arms to build a foundation of strong behavioral priors for both human actions and robotic control. Specifically, our method utilizes kinematic rules with left/right hand coordinate systems for bidirectional alignment between human and robot action spaces. Given human or simulated robot demonstrations, MiVLA is trained to forecast behavior trajectories for one embodiment, and imitate behaviors for another one unseen in the demonstration. Based on this mutual imitation, it integrates the behavioral fidelity of real-world human data with the manipulative diversity of simulated robot data into a unified model, thereby enhancing the generalization capability for downstream tasks. Extensive experiments conducted on both simulation and real-world platforms with three robots (ARX, PiPer and LocoMan), demonstrate that MiVLA achieves strong improved generalization capability, outperforming state-of-the-art VLAs (e.g., $\boldsymbolπ_{0}$, $\boldsymbolπ_{0.5}$ and H-RDT) by 25% in simulation, and 14% in real-world robot control tasks.
Statistics of Min-max Normalized Eigenvalues in Random Matrices
Venue: arXiv cs.LG 2025
Year: 2025

Random matrix theory has played an important role in various areas of pure mathematics, mathematical physics, and machine learning. From a practical perspective of data science, input data are usually normalized prior to processing. Thus, this study investigates the statistical properties of min-max normalized eigenvalues in random matrices. Previously, the effective distribution for such normalized eigenvalues has been proposed. In this study, we apply it to evaluate a scaling law of the cumulative distribution. Furthermore, we derive the residual error that arises during matrix factorization of random matrices. We conducted numerical experiments to verify these theoretical predictions.
FM-EAC: Feature Model-based Enhanced Actor-Critic for Multi-Task Control in Dynamic Environments
Venue: arXiv cs.LG 2025
Year: 2025

Model-based reinforcement learning (MBRL) and model-free reinforcement learning (MFRL) evolve along distinct paths but converge in the design of Dyna-Q [1]. However, modern RL methods still struggle with effective transferability across tasks and scenarios. Motivated by this limitation, we propose a generalized algorithm, Feature Model-Based Enhanced Actor-Critic (FM-EAC), that integrates planning, acting, and learning for multi-task control in dynamic environments. FM-EAC combines the strengths of MBRL and MFRL and improves generalizability through the use of novel feature-based models and an enhanced actor-critic framework. Simulations in both urban and agricultural applications demonstrate that FM-EAC consistently outperforms many state-of-the-art MBRL and MFRL methods. More importantly, different sub-networks can be customized within FM-EAC according to user-specific requirements.