中 EN
登录 注册账户

Academic Intelligence · Curated Daily

探索全球前沿学术脉络

AcademicHub 汇聚顶级期刊与预印本平台的实时文献。定制您的专属科研雷达，利用大语言模型自动生成交叉领域文献分析简报。

登录 注册账户
01.
medRxiv (Medicine) 2026-06-17

Brain age gap correlates with DTI-derived microstructural abnormalities in multiple sclerosis.

作者:
Lea↗Al-Ledani↗Bardell↗Maltby↗Ramadan↗R. A↗Lechner-Scott↗

Background: Brain age gap (BAG) is increased in multiple sclerosis (MS), but whether it reflects microstructural pathology beyond conventional atrophy remains unclear. Objective: To test whether BAG is elevated in MS and correlates with conventional and diffusion tensor imaging (DTI) abnormalities relative to healthy controls. Methods: A case-control study of 43 people with MS and 18 healthy controls was performed. BAG was estimated from T1-weighted MRI using brainageR. Controls were used as MRI reference distributions. MRI values were expressed as deviation z-scores and correlated with BAG within MS. Conventional MRI and DTI domains were analysed using age/sex-adjusted partial correlations with domain-wise Benjamini-Hochberg FDR correction, where appropriate. Results: BAG was higher in MS than controls (4.79 vs -2.58 years; p

阅读与讨论 → 访问原文 →
02.
arXiv (CS.CL) 2026-06-12

SENTINEL: Failure-Driven Reinforcement Learning for Training Tool-Using Language Model Agents

作者:
Ziyi Wang↗Yuxuan Lu↗Yimeng Zhang↗Qun Liu↗Chen Luo↗Jiri Gesi↗Hanqing Lu↗Yisi Sang↗Manling Li↗Jing Huang↗Dakuo Wang↗

Language model agents are increasingly effective in solving realistic tasks through multi-turn tool use. However, training reliable tool-using agents remains challenging in practice. While reinforcement learning provides an on-policy paradigm for improving agents from their own environment interactions, its effectiveness depends heavily on the training task distribution. When tasks are fixed before training, the task distribution can become increasingly mismatched with the policy's evolving capabilities, causing many rollouts to be spent on uninformative tasks. We propose SENTINEL, a failure-driven reinforcement learning framework that turns the Solver's rollout failures into targeted training tasks. SENTINEL follows a Controller–Proposer–Solver loop: the Controller analyzes failed trajectories and summarizes recurring error patterns, the Proposer generates executable tasks that stress these weaknesses, and the Solver is trained on the targeted tasks. On Tau2-Bench Retail with Qwen3-4B-Thinking-2507, SENTINEL improves Pass\^{}1 from 66.4 to 74.9 and outperforms RL on general synthetic tasks across Pass\^{}k metrics. These results demonstrate that model failures provide an effective and scalable source of targeted training signal for improving tool-using language model agents.

阅读与讨论 → 访问原文 →
03.
arXiv (CS.CL) 2026-06-12

Detect, Remask, Repair: Diffusion Editing for Faithful Summarization of Evolving Contexts

作者:
Hao Zou↗Zachary Horvitz↗Chandhru Karthick↗Zhou Yu↗Kathleen McKeown↗

Summaries of real-world events can become outdated as contexts evolve and new information arrives. A common response is to generate a new summary from the updated context, but full regeneration discards the previous draft, can obscure what changed, and may be unnecessary when only a few claims are unsupported. We study localized faithfulness repair: updating outdated spans in an existing summary while preserving supported content. We propose DETECT-REMASK-REPAIR, a diffusion-based framework that identifies, remasks, and repairs outdated regions with masked diffusion language models. To evaluate evolving-context summarization, we introduce StreamSum, a benchmark of synthetic event timelines. Experiments on DialogSum and StreamSum show that localized diffusion repair provides a controllable alternative to full rewriting: faithfulness-steered repair improves early drafts, one-step repair reduces repair cost to under half a second, with the framework enabling faithfulness-speed-preservation tradeoffs across datasets. We also find that the framework can provide a post-hoc correction step that improves faithfulness for autoregressive systems.

阅读与讨论 → 访问原文 →
04.
arXiv (CS.LG) 2026-06-12

Using Seismic Statistical Features and VQ-VAE to Improve Spatiotemporal Seismicity Predictability

作者:
Wei Quan↗Denise Gorse↗

arXiv:2606.10069v2 Announce Type: replace Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package. We here extend this previous work in two key ways, focusing on data from Japan as a large dataset is necessary in order to allow for the training of a deep learning (autoencoder) model. First, we move from whole-region prediction (considering, for each candidate event, the likelihood of an event M $\geq$ 5.0 anywhere in the region in the next 15 days) to localised predictions in which both the region of feature computation and the region of prediction are restricted to a circle of radius 24 km around the candidate event, and we show that performance remains excellent, similar to our previous whole-region study for the same area. Second, we here couple this proven set of SSFs, based on one-dimensional (catalogue) data, with a novel feature based on two-dimensional seismic maps, obtained by training a VQ-VAE model to reproduce such maps as output and identifying a measure of its error in doing so with a localised build-up of crustal stress. We show that while localised prediction based on SSFs can be effective alone, with test AUC values as high as those obtained in the case of Japan in our previous whole-region study, the inclusion of the new natively-spatial VQ-VAE-derived feature, top-ranked by SHAP analysis, can enhance performance and additionally appears to near-wholly replace the traditionally-computed $b$-value in terms of feature usage.

阅读与讨论 → 访问原文 →
05.
arXiv (CS.LG) 2026-06-11

From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features

作者:
Juliette Murris↗Bernadette Stolz↗Karsten Borgwardt↗

arXiv:2606.11911v1 Announce Type: cross Abstract: Persistence diagrams are common representations in topological data analysis, but they do not naturally live in a vector space, and the statistical tools developed for comparing them have largely evolved separately from those used for downstream prediction. We introduce STRAND (Survival Topological Representation ANalysis of Diagrams), which treats (collections of) PDs as survival data: each topological feature with persistence value $p = d - b$ is a fully observed time-to-event, and the persistence survival function $S(t) = \mathbb{P}(p > t)$ is the central object for comparing diagrams. From this single representation we derive (i) a non-parametric two-sample test with calibrated Type I error and high power from a small number of diagrams; (ii) interpretable effect sizes; and (iii) a 1-Wasserstein-stable feature vector for downstream machine learning. We validate calibration and power on synthetic manifolds with controlled topology, demonstrate competitive vectorisation across 14 graph and 3D point cloud benchmarks, and apply the method to study functional brain connectivity in fMRI/neuroscience data. To our knowledge, STRAND is the first method to provide hypothesis testing and vectorisation for persistence diagrams from a single coherent and interpretable representation.

阅读与讨论 → 访问原文 →
06.
arXiv (CS.LG) 2026-06-11

Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems

作者:
Warren Fernando↗Nikos Komninos↗

arXiv:2606.11471v1 Announce Type: cross Abstract: The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels. The proliferation of email communication is apparent in both professional and personal contexts, thereby creating numerous vulnerabilities for malicious actors to exploit. Spam emails, a form of unsolicited correspondence often bearing malicious intent towards recipients, have been an ongoing challenge for email users since the inception of email technology, and this problem has been exacerbated by the growth of the digital landscape. Email spam filters are integral components of email clients, engineered to identify potentially harmful messages and alert users to their malicious content. Phishing, frequently the initial phase of malware-based attacks, is evolving rapidly, with malware becoming increasingly sophisticated over time. A widely adopted approach for detecting malicious activity within malware and spam domains is the application of machine learning. Our aim is to assess the impact of the evolution within the spam email domain on these machine learning-based detection systems and to explore strategies for mitigating associated performance degradation.

阅读与讨论 → 访问原文 →
07.
medRxiv (Medicine) 2026-06-16

Cardiac positronium lifetime in human PET: a reproducible right-left ventricular contrast that is not explained by blood oxygenation

作者:
Zermeno↗E. D↗

Background. Ortho-positronium (o-Ps) lifetime, now measurable in vivo on long-axial-field-of-view (LAFOV) PET/CT, has been proposed as a biomarker of tissue oxygenation and hypoxia. Because o-Ps lifetime is dominated by tissue free-volume structure while the oxygen- specific contribution is small, whether an in-vivo lifetime contrast reflects oxygenation rather than anatomy is an open, identifiability-limited question. Aim. To test the oxygenation hypothesis directly using the heart's natural arterial/venous oxygenation contrast, with a built-in anatomical control. Methods. We re-analysed a public [82Rb]Cl human cardiac LAFOV PET/CT dataset (5.30 x 10^8 evaluated three-photon events). Per-compartment o-Ps lifetimes were extracted with a background-plus-two-component exponentially-modified-Gaussian (EMG) model. The list-mode to image mapping and right/left ventricle (RV/LV) identity were established lifetime-free (the mapping reproduces the provider's reconstructed image at block-correlation 0.998 and wins a joint multi-organ alignment panel). We applied a confound battery: registration stress test, blood-core vs wall, lung-air and wall-myocardium partial-volume, tissue density; and a structure/position-matched control (pulmonary artery, deoxygenated, vs aorta, oxygenated). An isotope-matched 82Rb uniform-quartz reference bounded the instrument's positional behaviour. All results were produced by two independent analysis pipelines. Results. RV o-Ps lifetime exceeded LV by delta tau = +0.304 ns (RV 1.700 +/- 0.172, LV 1.396 +/- 0.130 ns; about 1.4 sigma), in the oxygen-expected direction; the contrast was stable across +/-16 mm registration perturbation (sign preserved in 100% of 342 shifts) and resided in the blood core, not the wall. However, the matched-vessel control was null: pulmonary artery minus aorta = -0.011 +/- 0.344 ns. Lung-air and wall-myocardium partial-volume were disfavoured, and the effect fell within the isotope-matched 82Rb instrumental positional envelope (about 0.1-0.35 ns over 40 mm in uniform material). Conclusion. On this single subject, the cardiac o-Ps lifetime contrast does not provide a clean readout of blood oxygenation: an oxygenation effect of the observed (about 0.3 ns) magnitude is ruled out by the matched control, while a small physiological effect cannot be excluded. We provide a reusable confound-control battery for evaluating future in-vivo o-Ps oxygenation claims. Multi-subject replication with anatomy decoupled from oxygenation is required.

阅读与讨论 → 访问原文 →
08.
arXiv (CS.CL) 2026-06-18

As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language

作者:
Jasmine Owers↗Edwin Simpson↗Martha Lewis↗

Figurative language and negation are two areas that challenge current language models, however, both are widely used throughout written and spoken language. Large language models (LLMs) are also widely used in everyday contexts where they cannot necessarily be tuned for a specific dataset. It is therefore essential to understand the ability of LLMs to correctly interpret text that includes both negation and figurative language. To investigate this, we develop a set of new annotations to an existing dataset of figurative language, and test a range of language models on the dataset. We find that the combination of negation and figurativeness can present a particular challenge, and that performance overall and across different negation types is particularly dependent on the prompt style used.

阅读与讨论 → 访问原文 →
09.
arXiv (CS.CV) 2026-06-17

Learning QoE from Packet-Level Measurements in Encrypted Video Conferencing Traffic

作者:
Michael Sidorov↗Ofer Hadar↗

The quality of the user experience has become one of the most important aspects in todays world, as it directly influences individuals willingness to continue using or abandon a product or service. In this context, video conferencing applications (VCAs), which experienced widespread adoption following the COVID-19 pandemic, must deliver excellent performance to remain competitive in an increasingly crowded market. Although content providers (CPs) such as Zoom, WhatsApp, Telegram, and Google Meet can assess conversation quality by comparing transmitted and received data. The widespread use of end-to-end encryption in VCAs makes quality-of-experience (QoE) evaluation by internet service providers (ISPs) far more challenging. Since ISPs do not have access to the encrypted content, they must rely on passive measurements of unencrypted traffic characteristics on the data path. In this work, we present a simple yet effective QoE prediction framework based on an almost stock convolutional neural network (CNN) architecture that uses only the packet sizes extracted from the communication between two participants in a video conferencing (VC) call to predict two QoE metrics: BRISQUE and MOS. The proposed framework is simple, easy to implement, and does not require high-end computational resources, yet it provides superior prediction performance, as shown in our experiments on two custom datasets collected from WhatsApp and Zoom, which achieve substantial improvements over previous models for the QoE prediction task.

阅读与讨论 → 访问原文 →
11.
arXiv (CS.AI) 2026-06-18

Machine Unlearning for the XGBoost Model with Network Intrusion Datasets

作者:
Diana Magalh\~aes↗Eva Maia↗Jo\~ao Vitorino↗Isabel Pra\c{c}a↗

arXiv:2606.19220v1 Announce Type: cross Abstract: Machine Unlearning (MU) has emerged as an important technique for removing specific data points from trained models without requiring full retraining. However, most existing MU research focuses on deep learning and image data, leaving a gap in the domain of network intrusion detection, which relies heavily on tabular data. This work introduces XGBoost-Forget, an unlearning approach for the XGBoost model, to address this gap. The approach is evaluated on two tabular Network Intrusion (NI) datasets, IoT-23 and GeNIS, using multiple metrics to assess model performance, unlearning efficiency, and forgetting quality. The results show that XGBoost-Forget maintains predictive performance close to the original model while providing significantly faster unlearning, demonstrating its potential for MU in tabular NI settings.

阅读与讨论 → 访问原文 →
12.
arXiv (CS.LG) 2026-06-12

DeepJEB++: Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation

作者:
Soyoung Yoo↗Leekyo Jeong↗Jinsu Ra↗Dongeon Lee↗Sunwoong Yang↗Hyogu Jeong↗Namwoo Kang↗

arXiv:2606.12994v1 Announce Type: new Abstract: Data-driven engineering design is constrained by the lack of large-scale 3D datasets that pair geometry with physics-based performance labels. In particular, existing 3D data augmentation techniques have limitations in preserving subtle and diverse geometric variations, and it remains difficult to automate the subsequent simulation-labeling process, where boundary conditions vary depending on the generated geometry. We present DeepJEB++, a foundation-model-driven data-augmentation framework that expands a small seed set of jet engine brackets into a large, simulation-labeled 3D dataset under constrained resources. Our key idea is to augment in the data-rich 2D latent space, then transfer to 3D. In Stage 1, we fine-tune a pretrained 2D latent diffusion model on multi-view renders and synthesize novel views by latent interpolation, retaining manufacturable designs through a vision-language-model (VLM) quality filter. In Stage 2, the validated images are lifted to 3D meshes by a domain-adapted generative foundation model. In Stage 3, an automated pipeline recognizes the load and bolt interfaces on each mesh and assigns finite-element labels – mass, stress, and displacement – without manual intervention. We assess augmentation quality along three intrinsic axes: manufacturability, label fidelity against the SimJEB ground truth, and distributional consistency. Starting from fewer than 400 seed designs, DeepJEB++ yields 15,360 simulation-labeled 3D brackets – a 40x expansion – using a single GPU per stage. The dataset will be made publicly available to support reproducible engineering-AI research.

阅读与讨论 → 访问原文 →
13.
arXiv (CS.AI) 2026-06-19

Data Standards for Humanoid Robotics: The Missing Infrastructure for Physical AI

作者:
Shaoshan Liu↗Xiugong Qin↗Xuan Wu↗Xuan Xia↗Ning Ding↗Jialu Liu↗Jie Tang↗

arXiv:2606.19769v1 Announce Type: cross Abstract: The scalability of humanoid robots will depend not only on models and hardware, but also on whether physical experience can accumulate across robots, tasks, organizations, and time. Drawing on the authors' work in developing ISO/WD 26264-1, Humanoid robot datasets – Part 1: General requirements, within ISO/TC 299/WG 16, this article argues that data standards are becoming foundational infrastructure for Physical AI. We develop three insights. First, humanoid robot data is embodied interaction data, not a collection of isolated digital samples; a useful dataset must preserve the relationship among robot body, action, task, scene, execution trace, and outcome. Second, its value depends on physical coherence: multimodal streams are reusable only when timing, coordinate frames, calibration, kinematics, units, and synchronization assumptions remain inspectable. Third, the main bottleneck is not only data scarcity, but non-cumulative data caused by high collection costs, data silos, and inconsistent evaluation. We argue that humanoid robot data standards address these bottlenecks by making embodied experience interpretable, shareable, traceable, and reusable. A general standard should provide horizontal infrastructure for lifecycle management, metadata, provenance, quality, versioning, and traceability, while capability-specific parts should define domain grammar for manipulation, locomotion, human-robot interaction, cognition, and future humanoid capabilities. As AI moves from screens into bodies, data standards must evolve from organizing digital information to structuring physical interaction.

阅读与讨论 → 访问原文 →
14.
arXiv (CS.CV) 2026-06-12

SPARC: Reliable Spatial Annotations from Robot Demonstrations at Scale

作者:
Nils Blank↗Paul Mattes↗Maximilian Xiling Li↗Jakub Suliga↗Thomas Roth↗Moritz Reuss↗Pankhuri Vanjani↗Rudolf Lioutikov↗

This work introduces Spatial Annotations from Robot Demonstrations with Reliability Calibration (SPARC), a risk-aware framework that automatically labels robot demonstrations with structured spatial annotations and assigns each annotation a reliability score. Structured spatial annotations, such as bounding boxes, object trajectories, and manipulation phase labels, benefit a broad range of robotics applications from training grounded robot policies and embodied foundation models to motion planning and hierarchical task composition. Existing automated pipelines generate such annotations at scale but provide no reliable quality signal: detector confidence is poorly calibrated for annotation correctness, forcing a choice between accepting noisy labels or discarding useful samples. In contrast to existing automated pipelines, SPARC leverages the spatio-temporal structure inherent to robot tasks to generate a reliability signal, reducing noisy labels and retaining more useful samples. We further introduce Interaction-Aware Bench (IA-Bench), a benchmark that measures model accuracy in grounding the locations of interacted objects in robot demonstrations. On 1.7k human-annotated demonstrations spanning diverse embodiments and scenarios, SPARC significantly outperforms detection-only baselines in localization accuracy while retaining three times more samples at high-precision operating points. Our experiments demonstrate that models finetuned on our annotations achieve state-of-the-art results on object-grounding and pointing benchmarks among similarly sized models, while remaining competitive on broader spatial-reasoning suites without manually verified or annotated training data. Furthermore, policies trained on SPARC-generated annotations outperform baselines in cluttered, visually ambiguous real-world scenes. Code, data, and models are available at intuitive-robots.github.io/sparc-labeling.

阅读与讨论 → 访问原文 →
15.
arXiv (CS.AI) 2026-06-12

Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligenc

作者:
Munkhdegerekh Batzorig↗Purevbaatar Ganbold↗Kyungbin Park↗Pilkong Jeong↗Kangbin Yim↗

arXiv:2605.03847v2 Announce Type: replace Abstract: Distributed collaborative intelligence (DCI), encompassing edge-to-edge architectures, federated learning, transfer learning, and swarm systems, creates environments in which emergent risk is structurally unavoidable: locally correct decisions by individual agents compose into globally unacceptable behavioral trajectories under uncertainty. Existing approaches such as constrained optimization, safe reinforcement learning, and runtime assurance evaluate acceptability at the level of individual actions rather than across behavioral trajectories, and none addresses the multi-participant, uncertainty-laden nature of DCI deployments. This paper introduces mechanical conscience (MC), a novel concept and simplified mathematical framework that operationalizes trajectory-level normative regulation for both single-agent and distributed intelligent systems. Mechanical conscience is defined as a supervisory filter that minimally corrects a baseline policy's actions to reduce cumulative deviation from a normatively admissible region, while accounting for epistemic uncertainty. We introduce associated constructs, conscience score, mechanical guilt, and resonant dependability, that provide an interpretable vocabulary and computable governance signals for this emerging field. Core theoretical properties are established: admissibility equivalence, existence of optimal regulation, and monotonic deviation reduction. Illustrative results demonstrate that MC-regulated agents maintain trajectory-level normative acceptability where conventional controllers drift outside admissible bounds, and that the framework naturally extends to suppress interaction-induced emergent risk in multi-agent DCI settings.

阅读与讨论 → 访问原文 →
16.
arXiv (CS.AI) 2026-06-11

Market Design for AI: Beyond the Copyright Binary

作者:
Yan Dai↗Maryam Farboodi↗Negin Golrezaei↗Sepehr Shahshahani↗

arXiv:2606.12260v1 Announce Type: cross Abstract: How can we design a market of human-generated content for use in training AI models that both enables technological progress and preserves individual incentives for high-quality content creation? Existing approaches take polar positions: a "free-for-all" model based on fair use and a "strong intellectual property rights" model. We show that both fail: Free-for-all does not compensate creators, and – by modeling as a static Stackelberg game – strong intellectual property rights also underpower creative incentives. We find this especially true for more innovative creators, a phenomenon we term the "originality penalty." Extending this insight to a dynamic model, we find another market failure undermining AI model performance, even for an initially good model: Such a model induces greater reliance by humans on AI-assisted creation, resulting in homogenized content feeding back into training, which degrades the model performance – a "curse of precision." We further propose a market design with a data intermediary internalizing cross-creator externalities and subsidizing innovative contributions, thereby restoring efficiency.

阅读与讨论 → 访问原文 →
17.
PLOS Medicine 2026-05-15

Spatial transcriptomic-metabolic features of tumor foci and tumor capsule in microvascular invasion with hepatocellular carcinoma: A spatial multi-omics study

作者:
Zhi-Hui Luo↗

by Zhi-Hui Luo, Na Wang, Jingwei Zhao, Fei Long, Si Wu, Wei Zhong, Wei-Ming Chen, Bicheng Wang, Kun Wang, Yufeng Yuan, Jingjiao Zhou, Chunhui Yuan, Fubing Wang Background Microvascular invasion (MVI) is closely related to the recurrence and metastasis of hepatocellular carcinoma (HCC), but the underlying cellular mechanism remains largely elusive. This study aims to elucidate the regional cellular discrepancy between MVI-positive (MVI+) and MVI-negative (MVI−) HCC by integrating Spatial transcriptomics (ST) and spatial metabolomics (SM). Methods and findings ST and SM were performed on six tissue samples from four patients (including 2 MVI+, 2 MVI−, and 2 paratumor tissues), with the integration of 79 public single-cell RNA sequencing datasets of HCC. Patient identity was used as a covariate in the linear equation for regional differentially expressed gene analysis with the ST data. Clinical validation was conducted through multiplex immunofluorescence staining in 79 patients, together with external validation in the cancer genome atlas (TCGA)-liver hepatocellular carcinoma (LIHC) cohort (n = 299) and an independent microarray dataset (n = 62). For cell-type-specific metabolic profiling, spatial transcriptomic-metabolic registration was performed. The functional roles of key metabolites were further validated in vitro using inflammatory cancer-associated fibroblasts (iCAFs) derived from hepatic stellate cells (HSCs) and primary CAFs through co-culture models and various functional assays assessing cell proliferation, migration, and invasion. In the tumor lesion, a malignant STMN1+HMGN2+GPC3+ cell subtype enriched in MVI+ HCC was identified, which exhibited enhanced proliferative activity and was associated with poor prognosis. This finding was further confirmed in a local cohort of 79 patients, where multiplex immunofluorescence staining for the three genes (STMN1, HMGN2, and GPC3) showed significantly higher expression in the MVI+ group than in the MVI− group (p = 0.046). Integrated SM analysis further revealed that this cell population underwent metabolic reprogramming characterized by suppressed glycerolipid metabolism. In the tumor capsule, iCAFs-related genes were downregulated in MVI+ cases, and iCAFs were located distally from the tumor boundary. Spatial metabolite mapping showed a strong correlation between taurine and iCAFs, and functional assays demonstrated that taurine promotes HCC proliferation and migration by suppressing iCAF activity. One limitation of this study is the small sample size of spatial omics data, which hinders a more complete molecular functional analysis of the STMN1+HMGN2+GPC3+ cell subtype and iCAFs in MVI+ HCC. Larger-scale ST cohorts are required to further validate and expand the findings of this study. Conclusions This integrative spatial atlas proposes a hypothesis that there exists a highly proliferative and metabolically reprogrammed malignant cell subtype in the tumor lesion of MVI+ HCC, and that taurine in the tumor capsule modulates iCAF activity to influence tumor progression. The exploratory results provide mechanistic insights into MVI-related HCC progression and offer potential avenues for targeted therapeutic intervention of MVI+ HCC.

阅读与讨论 → 访问原文 →
18.
medRxiv (Medicine) 2026-06-15

Dysplasia-Stratified Management of Barrett's Esophagus: An Incidence-Based U.S. Cost-Effectiveness Analysis

作者:
Kowada↗

Background and Aims Barrett's esophagus (BE) is the principal precursor of esophageal adenocarcinoma (EAC), whose incidence has risen sharply in Western countries since the 1960s. Effective, dysplasia stratified surveillance strategies are needed to prevent progression. This study evaluated the cost effectiveness of dysplasia stratified surveillance intervals and endoscopic eradication therapy (EET) across the BE spectrum. Methods We developed an incidence-based Markov state transition model of BE progression calibrated to U.S. epidemiologic data from a healthcare sector perspective over a lifetime horizon. Four hypothetical cohorts of 50-year-old individuals with short segment BE (SSBE), nondysplastic BE (NDBE), low grade dysplasia (LGD), or high-grade dysplasia (HGD) were evaluated. Strategies included no surveillance; surveillance at 1-, 2-, 3-, 4-, 5-, or 10-year intervals; standard or AI assisted endoscopy; non endoscopic screening (sponge, breath, miRNA tests); and EET for LGD and HGD. Outcomes included costs, quality adjusted life years (QALYs), incremental cost effectiveness ratios (ICERs), net monetary benefits (NMBs), EAC cases, and EAC-related deaths. Sensitivity analyses used a willingness to pay threshold of US$100,000 per QALY. Results No surveillance was the most cost-effective strategy for SSBE and NDBE. For LGD, upfront EET was more cost effective than all surveillance strategies, with results sensitive to EAC incidence and recurrence. For HGD, EET was cost saving and yielded the greatest QALYs, with findings robust in 99.9% of simulations. EET prevented 12,614 and 44,295 EAC related deaths per 100,000 individuals with LGD and HGD, respectively. Conclusion Dysplasia-stratified management is essential for optimizing surveillance and treatment strategies in BE. Any degree of dysplasia should receive EET followed by targeted post-treatment monitoring, establishing EET as the central therapeutic pathway for dysplastic BE.

阅读与讨论 → 访问原文 →
19.
medRxiv (Medicine) 2026-06-17

Accounting for Human Movement to Improve Exposure-Health Models

作者:
Tahir↗Smart↗Cai↗Ng↗Vande Hey↗Lucas↗T. C↗

Background. Current exposure-health models rely on averaged, residential-based environmental exposures, failing to account for human movement. This aggregation can lead to exposure misclassification and biased exposure-response estimates, potentially distorting our understanding of the true health effects of environmental conditions. We developed exposure disaggregation regression models that explicitly account for human movement when linking environmental exposures to health outcomes. Methods. By weighting pixel-level exposures according to distance from home as a simple proxy for human movement, our model linked disaggregated environmental exposures to individual-level health outcomes. Weights were either fixed a priori or derived from a latent distance-decay power parameter learned from the data. We additionally evaluated model performance under a nonlinear exposure-response relationship. Model performance was assessed across multiple sample sizes (N = 1,114; 50,000; and 100,000). A simulation study examined parameter recovery using bias, empirical standard error (EmpSE), and credible interval coverage. As a case study, Demographic and Health Surveys (DHS) data from Albania were used to link acute respiratory infection (ARI) outcomes among children under five to pixel-level NDVI within a 3 km buffer around DHS cluster centroids, and the proposed models were applied to these data. Results. Across all models (fixed-weight, learned-weight, and restricted cubic spline models), parameter recovery improved with increasing sample size. At N = 1,114, estimates were biased and imprecise, with incorrect effect direction for exposure-response parameters (e.g., learned-weight {beta}1 bias = - 0.79; EmpSE = 2.61; coverage = 0.88). In contrast, the models accurately recovered parameters at larger sample sizes, including the latent distance-decay parameter (bias = - 0.02; EmpSE = 0.15; coverage = 0.95 at N = 100,000), demonstrating their ability to reliably learn movement-based exposure weights when sufficient data were available. Conclusion. Instead of relying on arbitrarily-sized buffers, this statistical framework provides a novel method for studying environmental exposure-health relationships whilst accounting for human movement. With sufficiently large sample sizes, it can accurately estimate the influence of disaggregated environmental exposures on individual-level health and help address exposure misclassification arising from residential-only metrics. This methodological framework remains scalable, interpretable, and adaptable to other exposures and outcomes, offering a foundation for future work that integrates richer mobility-informed exposure-health research.

阅读与讨论 → 访问原文 →
20.
medRxiv (Medicine) 2026-06-16

Upper airway disease in primary ciliary dyskinesia: Clinical management and factors influencing decision-making, a multicentre analysis

作者:
Gkatzou↗Campos↗Karavasiloglou↗Fernandez-Rodriguez↗Alexandru↗Anagiotos↗Armengot↗Aslan↗A. T↗Bon↗I. C. M↗Boon↗…

Background Upper airway disease is common in primary ciliary dyskinesia (PCD), but management evidence is limited. We aimed to describe management practices and identify factors influencing management decisions. Methods Using data from the Ear-Nose-Throat (ENT) Prospective International Cohort of patients with PCD (EPIC-PCD) and an ENT-specialist survey across participating centres, we described management practices recorded at routine follow-up. We assessed clinical factors associated with practices via mixed-effects logistic regression models. In a subgroup of patients, we assessed factors associated with initiation or discontinuation of practices. Results We included 579 patients: median age 15 years, 46% female. Nasal rinsing (54%) and nasal corticosteroids (22%) were most frequently prescribed. Among 466 patients with available data, 47 had grommets (10%) and 42 hearing aids (9%). Nasal corticosteroids and rinsing were more frequently prescribed in patients with polyps (odds ratio [OR] 3.74, 95% confidence interval [CI] 1.80-7.76; OR 3.39, 95% CI 1.37-8.37) or turbinate hypertrophy (OR 1.89, 95% CI 1.03-3.47; OR 2.89, 95% CI 1.55-5.38), and upper airway nebulisation in patients with frequent nasal symptoms (OR 2.86, 95% CI 1.11-7.39). Management practices differed between centres, as seen also by the specialists survey responses. In 177 patients with multiple visits, initiation of nasal rinsing was associated with frequent nasal symptoms (OR 3.18, 95% CI 1.24-8.18) and turbinate hypertrophy (OR 3.21, 95% CI 1.20-8.59). Conclusion Upper airway disease management in PCD varies and is partly guided by symptom burden and clinical findings. This variation across centres highlights the need for care standardisation and PCD-specific management guidelines.

阅读与讨论 → 访问原文 →
21.
arXiv (CS.AI) 2026-06-16

Mask-Proof: An LLM-based Automated Data Curation Pipeline on Mathematical Proofs

作者:
Jierui Zhang↗Siyuan Tan↗Xinhang Li↗Longzhuangzhi Lin↗Dailin Li↗Chengfeng Gu↗Xinping Li↗Yaxian Hao↗Shengjia Liang↗Yuxiang Ren↗Wenhao Liu↗

arXiv:2606.15258v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly capable of mathematical problem solving and can even assist with research-level proofs, yet we still lack a scalable and reproducible way to measure step-level reasoning in long proofs across diverse sources. This evaluation gap limits trustworthy AI assistance in proof-certified scientific progress. Existing evaluations often emphasize final answers or rely on costly expert grading, while end-to-end proof generation remains open-ended and hard to verify automatically. We introduce Mask-Proof, a pipeline that turns real proofs into automatically checkable masked-step tasks. It masks key formula steps, provides the necessary surrounding context, and evaluates model reconstructions with an LLM-based equivalence judge using repeated votes for stability. The resulting Mask-ProofBench contains 292 curated problems across diverse research areas. Experiments with 17 models show that reasoning-enhanced models outperform standard models by 12% to 27%. Our evaluator achieves 96.8% agreement with expert annotators, enabling faithful, reproducible, and comparable measurement of step-level mathematical reasoning. Benchmark, annotations, and code are available at https://github.com/weating/Mask-Proof.

阅读与讨论 → 访问原文 →
22.
arXiv (CS.CV) 2026-06-18

InTrain: Intrinsic Trainability for Zero-Cost Neural Architecture Search

作者:
Qinqin Zhou↗Fuhai Chen↗Jipeng Wu↗Zhiwei Chen↗Zhikai Hu↗Weiwei Cai↗

Training-free neural architecture search promises efficient discovery of high-performance networks without costly training. However, existing zero-cost proxies rely on fragmented heuristics that fail to capture the fundamental question: what makes an architecture trainable? This paper introduces Intrinsic Trainability (InTrain), a unified theoretical proxy that formalizes trainability as an architectural invariant emerging from two synergistic components: geometric capacity and optimization resilience. We operationalize intrinsic trainability through analysis of neural information processing. Geometric capacity is quantified via the participation ratio of activation covariance eigenspectrum, capturing the effective dimensionality of representation manifolds. Optimization resilience is measured through cumulative gradient health, assessing the robustness of backpropagation across network depth. InTrain synthesizes these dimensions through a scale-invariant multiplicative coupling, which we hypothesize is essential for capturing their synergistic, non-additive relationship. Extensive experiments on standard NAS benchmarks and search spaces demonstrate that InTrain achieves ranking correlations on par with state-of-the-art ensemble-based proxies and outperforms other single-metric methods.

阅读与讨论 → 访问原文 →
23.
arXiv (CS.AI) 2026-06-16

Gaming-Resistant Insurance Contracts for Autonomous AI Agents: Strategy-Proof Toll Mechanism Design

作者:
Hao-Hsuan Chen↗

arXiv:2606.16326v1 Announce Type: cross Abstract: Paper A defines a time-consistent actuarial runtime that prices each side-effect-bearing action against a contractually fixed safe default and gates execution against a reserve budget. It treats the operator as passive. This paper makes the operator strategic. We characterise a five-attack space for autonomous AI-agent insurance contracts and prove when the actuarial runtime is gaming-resistant. Two attack surfaces – post-toll safe-default selection and within-boundary action splitting – are closed by Paper A's minimal-authority and no-splitting clauses. The remaining three require new contract clauses. First, common-control aggregation prevents cross-boundary re-routing from reducing toll below the boundary potential applied to total exposure. Second, interface failures such as invalid JSON are contract-relevant events, not safety wins: treating them as zero-toll safe defaults can reward unreliable models, while escalation fees reverse the incentive. We validate this interface-compliance theorem on committed cross-model traces from the companion empirical paper. Third, a model-identity menu with a componentwise-minimum penalty schedule makes truthful reporting of the deployed model weakly dominant. We then compose these clauses with Paper A's runtime guarantees to obtain joint incentive compatibility over the five-attack space. Finally, a two-parameter premium family discharges operator individual rationality and weak budget balance at the truthful equilibrium. The result is an incentive-compatibility layer for actuarial control of autonomous-agent side effects.

阅读与讨论 → 访问原文 →
24.
arXiv (math.PR) 2026-06-11

Mean-field limits for stochastic particle systems on dense graphs

作者:
Angeliki Koutsimpela↗Elena Magnanini↗

arXiv:2606.11369v1 Announce Type: new Abstract: We study stochastic interacting particle systems whose interaction structure is described by dense weighted directed graphs converging to a graphon. In the thermodynamic limit, we prove a law of large numbers for the empirical measure process and derive a deterministic nonlinear master equation describing the macroscopic evolution. The limiting equation retains the heterogeneous interaction structure of the microscopic system through the limiting graphon, allowing for spatially non-homogeneous behaviors such as localized or community-type interactions.

阅读与讨论 → 访问原文 →
25.
arXiv (CS.AI) 2026-06-12

HD-Prot: A Protein Language Model for Joint Sequence-Structure Modeling with Continuous Structure Tokens

作者:
Yi Zhou↗Haohao Qu↗Yunqing Liu↗Shanru Lin↗Le Song↗Wenqi Fan↗

arXiv:2512.15133v3 Announce Type: replace-cross Abstract: Proteins inherently possess a consistent sequence-structure duality. The abundance of protein sequence data, which can be readily represented as discrete tokens, has driven fruitful developments in protein language models (pLMs). A key remaining challenge, however, is how to effectively integrate continuous structural knowledge into pLMs. Current methods often discretize protein structures to accommodate the language modeling framework, which inevitably results in the loss of fine-grained information and limits the performance potential of multimodal pLMs. In this paper, we argue that such concerns can be circumvented: a sequence-based pLM can be extended to incorporate the structure modality through continuous tokens, i.e., high-fidelity protein structure latents that avoid vector quantization. Specifically, we propose a hybrid diffusion protein language model, HD-Prot, which embeds a continuous-valued diffusion head atop a discrete pLM, enabling seamless operation with both discrete and continuous tokens for joint sequence-structure modeling. It captures inter-token dependencies across modalities through a unified absorbing diffusion process, and estimates per-token distributions via categorical prediction for sequences and continuous diffusion for structures. Extensive results demonstrate that HD-Prot achieves competitive performance in unconditional sequence-structure co-generation, motif-scaffolding, protein structure prediction, and inverse folding tasks. Furthermore, our method can perform on par with state-of-the-art multimodal pLMs, despite being developed under limited computational resources (i.e., less than one-tenth the budget for modality extension fine-tuning). It highlights the viability of simultaneously estimating categorical and continuous distributions within a unified language model architecture, offering a promising alternative direction for multimodal pLMs.

阅读与讨论 → 访问原文 →