OpenRaiser/Intern-Atlas huggingface dataset:
MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment while monitoring disease progression through serial MRI and clinical follow-up. Accurate prostate segmentation is an important preliminary step for automating this process, enabling automated detection and diagnosis of PCa. However, existing deep-learning segmentation models are often trained on single-time-point and expertly annotated datasets, making them unsuitable for longitudinal AS analysis, where multiple time points and a scarcity of expert labels hinder their effective fine-tuning. To address these challenges, we propose MambaX-Net, a novel semi-supervised, dual-scan 3D segmentation architecture that computes the segmentation for time point t by leveraging the MRI and the corresponding segmentation mask from the previous time point. We introduce two new components: (i) a Mamba-enhanced Cross-Attention Module, which integrates the Mamba block into cross attention to efficiently capture temporal evolution and long-range spatial dependencies, and (ii) a Shape Extractor Module that encodes the previous segmentation mask into a latent anatomical representation for refined zone delination. Moreover, we introduce a semi-supervised self-training strategy that leverages pseudo-labels generated from a pre-trained nnU-Net, enabling effective learning without expert annotations. MambaX-Net was evaluated on a longitudinal AS dataset, and results showed that it significantly outperforms state-of-the-art U-Net and Transformer-based models, achieving superior prostate zone segmentation even when trained on limited and noisy data.
Dynamic Switched Quantum Key Distribution Network with PUF-based authentication
Venue: arXiv cs.CR 2025
Year: 2025

We demonstrate a centrally controlled dynamic switched-QKD network, with integrated PUF-based dynamic authentication for each QKD link. The performance of the dynamic switched-QKD network with real-time PUF-based authentication is analyzed.
Formally Exploring Time-Series Anomaly Detection Evaluation Metrics
Venue: arXiv cs.LG 2025
Year: 2025

Undetected anomalies in time series can trigger catastrophic failures in safety-critical systems, such as chemical plant explosions or power grid outages. Although many detection methods have been proposed, their performance remains unclear because current metrics capture only narrow aspects of the task and often yield misleading results. We address this issue by introducing verifiable properties that formalize essential requirements for evaluating time-series anomaly detection. These properties enable a theoretical framework that supports principled evaluations and reliable comparisons. Analyzing 37 widely used metrics, we show that most satisfy only a few properties, and none satisfy all, explaining persistent inconsistencies in prior results. To close this gap, we propose LARM, a flexible metric that provably satisfies all properties, and extend it to ALARM, an advanced variant meeting stricter requirements.
WP-CrackNet: A Collaborative Adversarial Learning Framework for End-to-End Weakly-Supervised Road Crack Detection
Venue: arXiv cs.CV 2025
Year: 2025

Road crack detection is essential for intelligent infrastructure maintenance in smart cities. To reduce reliance on costly pixel-level annotations, we propose WP-CrackNet, an end-to-end weakly-supervised method that trains with only image-level labels for pixel-wise crack detection. WP-CrackNet integrates three components: a classifier generating class activation maps (CAMs), a reconstructor measuring feature inferability, and a detector producing pixel-wise road crack detection results. During training, the classifier and reconstructor alternate in adversarial learning to encourage crack CAMs to cover complete crack regions, while the detector learns from pseudo labels derived from post-processed crack CAMs. This mutual feedback among the three components improves learning stability and detection accuracy. To further boost detection performance, we design a path-aware attention module (PAAM) that fuses high-level semantics from the classifier with low-level structural cues from the reconstructor by modeling spatial and channel-wise dependencies. Additionally, a center-enhanced CAM consistency module (CECCM) is proposed to refine crack CAMs using center Gaussian weighting and consistency constraints, enabling better pseudo-label generation. We create three image-level datasets and extensive experiments show that WP-CrackNet achieves comparable results to supervised methods and outperforms existing weakly-supervised methods, significantly advancing scalable road inspection. The source code package and datasets are available at https://mias.group/WP-CrackNet/.
Non-interference analysis of bounded labeled Petri nets
Venue: arXiv cs.FL 2025
Year: 2025

This paper focuses on a fundamental problem on information security of bounded labeled Petri nets: non-interference analysis. As in hierarchical control, we assume that a system is observed by users at different levels, namely high-level users and low-level users. The output events produced by the firing of transitions are also partitioned into high-level output events and low-level output events. In general, high-level users can observe the occurrence of all the output events, while low-level users can only observe the occurrence of low-level output events. A system is said to be non-interferent if low-level users cannot infer the firing of transitions labeled with high-level output events by looking at low-level outputs. In this paper, we study a particular non-interference property, namely strong non-deterministic non-interference (SNNI), using a special automaton called SNNI Verifier, and propose a necessary and sufficient condition for SNNI.
Learned Inertial Odometry for Cycling Based on Mixture of Experts Algorithm
Venue: arXiv cs.RO 2025
Year: 2025

With the rapid growth of bike sharing and the increasing diversity of cycling applications, accurate bicycle localization has become essential. traditional GNSS-based methods suffer from multipath effects, while existing inertial navigation approaches rely on precise modeling and show limited robustness. Tight Learned Inertial Odometry (TLIO) achieves low position drift by combining raw IMU data with predicted displacements by neural networks, but its high computational cost restricts deployment on mobile devices. To overcome this, we extend TLIO to bicycle localization and introduce an improved Mixture-of Experts (MoE) model that reduces both training and inference costs. Experiments show that, compared to the state-of-the-art LLIO framework, our method achieves comparable accuracy while reducing parameters by 64.7% and computational cost by 81.8%.
Intelligent Communication Mixture-of-Experts Boosted-Medical Image Segmentation Foundation Model
Venue: arXiv cs.CV 2025
Year: 2025

Foundation models for medical image segmentation have achieved remarkable performance. Adaptive fine-tuning of natural image segmentation foundation models is crucial for medical image segmentation tasks. However, some limitations exist in existing fine-tuning methods: 1) insufficient representation of high-level features and 2) the fine-tuning process disrupts the structural integrity of pretrained weights. Inspired by these critical problems, we propose an intelligent communication mixture-of-experts boosted-medical image segmentation foundation model, named IC-MoE, with twofold ideas: 1) We construct basic experts, semantic experts, and adaptive experts. Moreover, we implement a pixel probability adaptive voting strategy, which enables expert selection and fusion through label consistency and load balancing. This approach preliminarily enhances the representation capability of high-level features while preserving the structural integrity of pretrained weights. 2) We propose a semantic-guided contrastive learning method to address the issue of weak supervision in contrastive learning. This method further enhances the representation capability of high-level features while preserving the structural integrity of pretrained weights. Extensive experiments across three public medical image segmentation datasets demonstrate that the IC-MoE outperforms other SOTA models. Consequently, the proposed IC-MoE effectively supplements foundational medical image segmentation models with high-level features and pretrained structural integrity. We also validate the superior generalizability of the IC-MoE across diverse medical image segmentation scenarios.
Mensen aanwijzen maar niet bij naam noemen: behavioural targeting, persoonsgegevens, en de nieuwe Privacyverordening
Venue: arXiv cs.CY 2025
Year: 2025

Information about millions of people is collected for behavioural targeting, a type of marketing that involves tracking people's online behaviour for targeted advertising. It is hotly debated whether data protection law applies to behavioural targeting. Many behavioural targeting companies say that, as long as they do not tie names to data they hold about individuals, they do not process any personal data, and that, therefore, data protection law does not apply to them. European Data Protection Authorities, however, take the view that a company processes personal data if it uses data to single out a person, even if it cannot tie a name to these data. This paper argues that data protection law should indeed apply to behavioural targeting. Companies can often tie a name to nameless data about individuals. Furthermore, behavioural targeting relies on collecting information about individuals, singling out individuals, and targeting ads to individuals. Many privacy risks remain, regardless of whether companies tie a name to the information they hold about a person. A name is merely one of the identifiers that can be tied to data about a person, and it is not even the most practical identifier for behavioural targeting. Seeing data used to single out a person as personal data fits the rationale for data protection law: protecting fairness and privacy.
Train for Truth, Keep the Skills: Binary Retrieval-Augmented Reward Mitigates Hallucinations
Venue: arXiv cs.CL 2025
Year: 2025

Language models often generate factually incorrect information unsupported by their training data, a phenomenon known as extrinsic hallucination. Existing mitigation approaches often degrade performance on open-ended generation and downstream tasks, limiting their practical utility. We propose an online reinforcement learning method using a novel binary retrieval-augmented reward (RAR) to address this tradeoff. Unlike continuous reward schemes, our approach assigns a reward of one only when the model's output is entirely factually correct, and zero otherwise. We evaluate our method on Qwen3 reasoning models across diverse tasks. For open-ended generation, binary RAR achieves a 39.3% reduction in hallucination rates, substantially outperforming both supervised training and continuous-reward RL baselines. In short-form question answering, the model learns calibrated abstention, strategically outputting "I don't know" when faced with insufficient parametric knowledge. This yields 44.4% and 21.7% fewer incorrect answers on PopQA and GPQA, respectively. Crucially, these factuality gains come without performance degradation on instruction following, math, or code, whereas continuous-reward RL, despite improving factuality, induces quality regressions.
Flow-Aware Ellipsoidal Filtration for Persistent Homology of Recurrent Signals
Venue: arXiv cs.CG 2025
Year: 2025

One common use of persistent homology is to explore the shape of point clouds, where points are assumed to be sampled from a geometric object. We propose a novel filtration, called ellipsoidal filtration, which assumes that point clouds are sampled from a dynamic smooth flow. Instead of creating topologies from point clouds at increasing scales using isotropic balls (for example, Vietoris-Rips filtration), ellipsoidal filtration creates ellipsoids around points based on local flow variances, approximating the flow's manifold as the scale increases. We show that constructing ellipsoidal neighbourhoods improves the denoising of recurrent signals and the estimation of recurrence times, especially when the data contain bottlenecks. Choosing ellipsoids according to the maximum persistence of the H1 class provides a data-driven threshold for both denoising and recurrence-time estimation.
Who Needs Crossings?: Noncrossing Linkages are Universal, and Deciding (Global) Rigidity is Hard
Venue: arXiv cs.CG 2025
Year: 2025

We exactly settle the complexity of graph realization, graph rigidity, and graph global rigidity as applied to three types of graphs: "globally noncrossing" graphs, which avoid crossings in all of their configurations; matchstick graphs, with unit-length edges and where only noncrossing configurations are considered; and unrestricted graphs (crossings allowed) with unit edge lengths (or in the global rigidity case, edge lengths in $\{1,2\}$). We show that all nine of these questions are complete for the class $\exists\mathbb{R}$, defined by the Existential Theory of the Reals, or its complement $\forall\mathbb{R}$; in particular, each problem is (co)NP-hard. One of these nine results--that realization of unit-distance graphs is $\exists\mathbb{R}$-complete--was shown previously by Schaefer (2013), but the other eight are new. We strengthen several prior results. Matchstick graph realization was known to be NP-hard (Eades \& Wormald 1990, or Cabello et al.\ 2007), but its membership in NP remained open; we show it is complete for the (possibly) larger class $\exists\mathbb{R}$. Global rigidity of graphs with edge lengths in $\{1,2\}$ was known to be coNP-hard (Saxe 1979); we show it is $\forall\mathbb{R}$-complete. The majority of the paper is devoted to proving an analog of Kempe's Universality Theorem--informally, "there is a linkage to sign your name"--for globally noncrossing linkages. In particular, we show that any polynomial curve $ϕ(x,y)=0$ can be traced by a noncrossing linkage, settling an open problem from 2004. More generally, we show that the regions in the plane that may be traced by a noncrossing linkage are precisely the compact semialgebraic regions (plus the trivial case of the entire plane). Thus, no drawing power is lost by restricting to noncrossing linkages. We prove analogous results for matchstick linkages and unit-distance linkages as well.
This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!
Venue: arXiv cs.DB 2025
Year: 2025

Tuning database management systems (DBMSs) is challenging due to trillions of possible configurations and evolving workloads. Recent advances in tuning have led to breakthroughs in optimizing over the possible configurations. However, due to their design and inability to leverage query-level historical insights, existing automated tuners struggle to adapt and re-optimize the DBMS when the environment changes (e.g., workload drift, schema transfer). This paper presents the Booster framework that assists existing tuners in adapting to environment changes (e.g., drift, cross-schema transfer). Booster structures historical artifacts into query-configuration contexts, prompts large language models (LLMs) to suggest configurations for each query based on relevant contexts, and then composes the query-level suggestions into a holistic configuration with beam search. With multiple OLAP workloads, we evaluate Booster's ability to assist different state-of-the-art tuners (e.g., cost-/machine learning-/LLM-based) in adapting to environment changes. By composing recommendations derived from query-level insights, Booster assists tuners in discovering configurations that are up to 74% better and in up to 4.7x less time than the alternative approach of continuing to tune from historical configurations.
Towards Explainable Skin Cancer Classification: A Dual-Network Attention Model with Lesion Segmentation and Clinical Metadata Fusion
Venue: arXiv cs.CV 2025
Year: 2025

Skin cancer is a life-threatening disease where early detection significantly improves patient outcomes. Automated diagnosis from dermoscopic images is challenging due to high intra-class variability and subtle inter-class differences. Many deep learning models operate as "black boxes," limiting clinical trust. In this work, we propose a dual-encoder attention-based framework that leverages both segmented lesions and clinical metadata to enhance skin lesion classification in terms of both accuracy and interpretability. A novel Deep-UNet architecture with Dual Attention Gates (DAG) and Atrous Spatial Pyramid Pooling (ASPP) is first employed to segment lesions. The classification stage uses two DenseNet201 encoders-one on the original image and another on the segmented lesion whose features are fused via multi-head cross-attention. This dual-input design guides the model to focus on salient pathological regions. In addition, a transformer-based module incorporates patient metadata (age, sex, lesion site) into the prediction. We evaluate our approach on the HAM10000 dataset and the ISIC 2018 and 2019 challenges. The proposed method achieves state-of-the-art segmentation performance and significantly improves classification accuracy and average AUC compared to baseline models. To validate our model's reliability, we use Gradient-weighted Class Activation Mapping (Grad-CAM) to generate heatmaps. These visualizations confirm that our model's predictions are based on the lesion area, unlike models that rely on spurious background features. These results demonstrate that integrating precise lesion segmentation and clinical data with attention-based fusion leads to a more accurate and interpretable skin cancer classification model.
Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains
Venue: arXiv cs.CL 2025
Year: 2025

Finetuning specialized generative evaluators has emerged as a popular paradigm to meet the increasing demand for scalable evaluation during both training and test-time. However, recent work has largely focused on applying new methodology, such as reinforcement learning (RL), to training evaluators, shying away from large-scale, data-driven development. In this work, we focus on data scaling, curating a set of 2.5M samples spanning five unique evaluation tasks (pairwise, step-level, reference-free and reference-based verification, and single rating) and multiple domains focused on reasoning evaluation. With our data, we train Foundational Automatic Reasoning Evaluators (FARE), a family of 8B and 20B (with 3.6B active) parameter evaluators, with a simple iterative rejection-sampling supervised finetuning (SFT) approach. FARE-8B challenges larger specialized RL-trained evaluators and FARE-20B sets the new standard for open-source evaluators, surpassing specialized 70B+ evaluators. Beyond static benchmarks, we evaluate FARE in real-world tasks: As inference-time rerankers, FARE-20B achieves near-oracle performance on MATH. As verifiers in RL training, FARE improves the downstream RL-trained model performance by up to 14.1% vs. string-matching verifiers. When initialized from FARE, a continually-finetuned FARE-Code outperforms gpt-oss-20B by 65% on evaluating test-case quality.
Information Capacity of EEG: Theoretical and Computational Limits of Recoverable Neural Information
Venue: arXiv cs.IT 2025
Year: 2025

Electroencephalography (EEG) is widely used to study human brain dynamics, yet its quantitative information capacity remains unclear. Here, we combine information theory and synthetic forward modeling to estimate the mutual information between latent cortical sources and EEG recordings. Using Gaussian-channel theory and empirical simulations, we find that scalp EEG conveys only tens of bits per sample about low-dimensional neural activity. Information saturates with approximately 64-128 electrodes and scales logarithmically with signal-to-noise ratio (SNR). Linear decoders capture nearly all variance that is linearly recoverable, but the mutual information they recover remains far below the analytic channel capacity, indicating that measurement physics - not algorithmic complexity - is the dominant limitation. These results outline the intrinsic ceiling on how much structure about brain state or thought content can be inferred from EEG.
Modeling Layered Consciousness with Multi-Agent Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

We propose a multi-agent framework for modeling artificial consciousness in large language models (LLMs), grounded in psychoanalytic theory. Our \textbf{Psychodynamic Model} simulates self-awareness, preconsciousness, and unconsciousness through agent interaction, guided by a Personalization Module combining fixed traits and dynamic needs. Using parameter-efficient fine-tuning on emotionally rich dialogues, the system was evaluated across eight personalized conditions. An LLM as a judge approach showed a 71.2\% preference for the fine-tuned model, with improved emotional depth and reduced output variance, demonstrating its potential for adaptive, personalized cognition.
RiskTagger: An LLM-based Agent for Automatic Annotation of Web3 Crypto Money Laundering Behaviors
Venue: arXiv cs.CR 2025
Year: 2025

While the rapid growth of Web3 has driven the development of decentralized finance, user anonymity and cross-chain asset flows make on-chain laundering behaviors more covert and complex. In this context, constructing high-quality anti-money laundering(AML) datasets has become essential for risk-control systems and on-chain forensic analysis, yet current practices still rely heavily on manual efforts with limited efficiency and coverage. In this paper, we introduce RiskTagger, a large-language-model-based agent for the automatic annotation of crypto laundering behaviors in Web3. RiskTagger is designed to replace or complement human annotators by addressing three key challenges: extracting clues from complex unstructured reports, reasoning over multichain transaction paths, and producing auditor-friendly explanations. RiskTagger implements an end-to-end multi-module agent, integrating a key-clue extractor, a multichain fetcher with a laundering-behavior reasoner, and a data explainer, forming a data annotation pipeline. Experiments on the real case Bybit Hack (with the highest stolen asset value) demonstrate that RiskTagger achieves 100% accuracy in clue extraction, 84.1% consistency with expert judgment, and 90% coverage in explanation generation. Overall, RiskTagger automates laundering behavior annotation while improving transparency and scalability in AML research.