arXiv subjects

Dong Yang

Publications and source records attributed to Dong Yang.

At least 37 records · Page 2Linked to original sources

arXiv2026

Improved Evidence Extraction and Metrics for Document Inconsistency Detection with LLMs

Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. However, research on LLM-based approaches to document inconsistency detection is relatively limited. We address this gap by investigating evidence extraction capabilties of LLMs for document inconsistency detection. To this end, we introduce new comprehensive evidence-extraction metrics and a redact-and-retry framework with constrained filtering that substantially improves evidence extraction performance over other prompting methods. We support our approach with strong experimental results and release a new semi-synthetic dataset for evaluating evidence extraction.

cs.CL↗

arXiv2026

Surg$Σ$: A Spectrum of Large-Scale Multimodal Data and Foundation Models for Surgical Intelligence

Surgical intelligence has the potential to improve the safety and consistency of surgical care, yet most existing surgical AI frameworks remain task-specific and struggle to generalize across procedures and institutions. Although multimodal foundation models, particularly multimodal large language models, have demonstrated strong cross-task capabilities across various medical domains, their advancement in surgery remains constrained by the lack of large-scale, systematically curated multimodal data. To address this challenge, we introduce Surg$Σ$, a spectrum of large-scale multimodal data and foundation models for surgical intelligence. At the core of this framework lies Surg$Σ$-DB, a large-scale multimodal data foundation designed to support diverse surgical tasks. Surg$Σ$-DB consolidates heterogeneous surgical data sources (including open-source datasets, curated in-house clinical collections and web-source data) into a unified schema, aiming to improve label consistency and data standardization across heterogeneous datasets. Surg$Σ$-DB spans 6 clinical specialties and diverse surgical types, providing rich image- and video-level annotations across 18 practical surgical tasks covering understanding, reasoning, planning, and generation, at an unprecedented scale (over 5.98M conversations). Beyond conventional multimodal conversations, Surg$Σ$-DB incorporates hierarchical reasoning annotations, providing richer semantic cues to support deeper contextual understanding in complex surgical scenarios. We further provide empirical evidence through recently developed surgical foundation models built upon Surg$Σ$-DB, illustrating the practical benefits of large-scale multimodal annotations, unified semantic design, and structured reasoning annotations for improving cross-task generalization and interpretability.

cs.AI↗

arXiv2026

FEASTS and MHONGOOSE: HI Column Density Distribution at $z=0$ for $N_\mathrm{HI}>10^{17.8}\, \mathrm{cm}^{-2}$

We present the first $z=0$ HI column density distribution function, $f(N_\mathrm{HI})$, extending down to $\log (N_\mathrm{HI}/\mathrm{cm}^{-2})=17.8$. This was derived from high-sensitivity 21-cm emission-line imaging at $\sim$1 kpc resolution. At high-column-densities (19.8$< \log (N_\mathrm{HI}/\mathrm{cm}^{-2}) <$21.3), our results align with earlier $z=0$ studies but benefit from 100 times greater sensitivity. Comparisons with $z\sim3$ quasar absorption-line studies reveal that $f(N_\mathrm{HI})$ at $z=0$ is systematically lower by 0.1-0.4 dex for $19.2< \log (N_\mathrm{HI}/\mathrm{cm}^{-2}) <21$. However, the distributions become comparable at $17.8< \log (N_\mathrm{HI}/\mathrm{cm}^{-2}) <19.2$, suggesting weak evolution in this regime. Extrapolating the length incidence ($\mathrm{d}N/\mathrm{d}X$) for $\log (N_\mathrm{HI}/\mathrm{cm}^{-2}) >17.5$ implies a covering fraction ($f_\mathrm{cov}$) of $\sim0.7$ within 1-kpc-scale HI-detected pixels at $z=0$. Notably, for $17.8< \log (N_\mathrm{HI}/\mathrm{cm}^{-2}) <20$, impact parameters at a given $N_\mathrm{HI}$ are significantly lower than previous $z\sim0$ absorption-line results and TNG50 simulation predictions. This discrepancy indicates challenges in identifying galaxy counterparts for absorbers and in recovering low-column-density HI within cosmological simulations. Finally, we derive a covering fraction of 0.006 for $\log (N_\mathrm{HI}/\mathrm{cm}^{-2}) >17.8$ gas within the virial radius around Milky-Way-like galaxies. These findings provide new constraints on the baryonic flows and gaseous dynamics governing galaxy evolution.

astro-ph.GA↗

arXiv2026

EMBERS I: Low redshift post-starburst galaxies are frequently depleted in molecular gas relative to star forming progenitors

The cold gas content of post-starburst galaxies (PSBs) provides important insight into the mechanisms that drive rapid quenching, but a multiphase assessment of both the atomic and molecular gas in PSBs does not yet exist. We introduce the Ensemble of Multiphase Baryons Evolving in Rapidly-quenching Systems, or EMBERS, a homogeneously selected, nearly mass- and redshift-complete survey of the global atomic (HI) and molecular gas (H2) in PSBs, observed with the Five Hundred-metre Aperture Spherical Telescope (FAST) and the Institut de radioastronomie millimetrique (IRAM) 30m telescope. We present new CO(1-0) observations for 52 PSBs with the IRAM 30m, which, combined with 9 archival observations, gives a total H2 sample of 61, of which 58/61 have ancillary HI measurements. We detect CO(1-0) in 34/61 galaxies, corresponding to molecular gas fractions (fH2 = MH2/M*) ranging from two to 250 per cent. By comparing with a stellar-mass matched star-forming (SF) control sample from xCOLD GASS, we find that PSBs on average are 0.3-0.6 dex depleted in H2. However, considering both HI and H2, individual PSBs host diverse gas reservoirs ranging from gas-rich in both phases, elevated in one phase, or gas-poor, the latter of which is common at lower stellar mass. The existence of gas-normal and gas-depleted PSBs in both phases suggests that some PSBs may rejuvenate their star formation, but the rapid shutdown of star formation in others is likely terminal. Despite this diversity, the majority of EMBERS PSBs are gas-poor compared to SF controls, with the typical PSB hosting gas reservoirs intermediate to those found in star-forming and quenched galaxies.

astro-ph.GA↗

arXiv2026

LUMEN: Longitudinal Multi-Modal Radiology Model for Prognosis and Diagnosis

Large vision-language models (VLMs) have evolved from general-purpose applications to specialized use cases such as in the clinical domain, demonstrating potential for decision support in radiology. One promising application is assisting radiologists in decision-making by the analysis of radiology imaging data such as chest X-rays (CXR) via a visual and natural language question-answering (VQA) interface. When longitudinal imaging is available, radiologists analyze temporal changes, which are essential for accurate diagnosis and prognosis. The manual longitudinal analysis is a time-consuming process, motivating the development of a training framework that can provide prognostic capabilities. We introduce a novel training framework LUMEN, that is optimized for longitudinal CXR interpretation, leveraging multi-image and multi-task instruction fine-tuning to enhance prognostic and diagnostic performance. We conduct experiments on the publicly available MIMIC-CXR and its associated Medical-Diff-VQA datasets. We further formulate and construct a novel instruction-following dataset incorporating longitudinal studies, enabling the development of a prognostic VQA task. Our method demonstrates significant improvements over baseline models in diagnostic VQA tasks, and more importantly, shows promising potential for prognostic capabilities. These results underscore the value of well-designed, instruction-tuned VLMs in enabling more accurate and clinically meaningful radiological interpretation of longitudinal radiological imaging data.

cs.CV↗

arXiv2026

A Particle-in-Cell Simulation Framework for Thomson Scattering Analysis in Inertial Confinement Fusion

In inertial confinement fusion (ICF), Thomson scattering (TS) is a widely used diagnostic technique for probing plasma conditions. We present a first-principles numerical approach to obtaining scattered light signals of ion acoustic features with high resolution in angle and frequency space using particle-in-cell simulations under typical ICF conditions. Our method demonstrates good agreement with existing theories for thermal collective TS. In the super-thermal collective regime, the results align with theory when the driven plasma modes are well-matched in wave vectors to the probe and collecting beams. Moreover, we also find that TS signals can remain significant even under imperfect wave-vector matching-a result that contradicts the conventional expectation that the TS spectrum strictly follows the plasma density spectrum. We attribute this discrepancy to a beating wave mechanism arising from the interaction between the probe beam and driven plasma density modulations. Our work thus provides a practical framework for interpreting TS signals from driven ion modes, a common yet complex feature in ICF plasmas.

physics.plasm-ph↗

arXiv2026

FM-RME: Foundation Model Empowered Radio Map Estimation

Traditional radio map estimation (RME) techniques fail to capture multi-dimensional and dynamic characteristics of complex spectrum environments. Recent data-driven methods achieve accurate RME in spatial domain, but ignore physical prior knowledge of radio propagation, limiting data efficiency especially in multi-dimensional scenarios. To overcome such limitations, we propose a new foundation model, characterized by self-supervised pre-training on diverse data for zero-shot generalization, enabling multi-dimensional radio map estimation (FM-RME). Specifically, FM-RME builds an effective synergy of two core components: a geometry-aware feature extraction module that encodes physical propagation symmetries, i.e., translation and rotation invariance, as inductive bias, and an attention-based neural network that learns long-range correlations across the spatial-temporal-spectral domains. A masked self-supervised multi-dimensional pre-training strategy is further developed to learn generalizable spectrum representations across diverse wireless environments. Once pre-trained, FM-RME supports zero-shot inference for multi-dimensional RME, including spatial, temporal, and spectral estimation, without scenario-specific retraining. Simulation results verify that FM-RME exhibits desired learning performance across diverse datasets and zero-shot generalization capabilities beyond existing RME methods.

eess.SP↗

arXiv2026

Improved LLM Agents for Financial Document Question Answering

Large language models (LLMs) have shown impressive capabilities on numerous natural language processing tasks. However, LLMs still struggle with numerical question answering for financial documents that include tabular and textual data. Recent works have showed the effectiveness of critic agents (i.e., self-correction) for this task given oracle labels. Building upon this framework, this paper examines the effectiveness of the traditional critic agent when oracle labels are not available, and show, through experiments, that this critic agent's performance deteriorates in this scenario. With this in mind, we present an improved critic agent, along with the calculator agent which outperforms the previous state-of-the-art approach (program-of-thought) and is safer. Furthermore, we investigate how our agents interact with each other, and how this interaction affects their performance.

cs.CL↗

arXiv2025

MAISI: Medical AI for Synthetic Imaging

Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet, MAISI can process organ segmentation, including 127 anatomical structures, as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic, anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data.

eess.IV↗

arXiv2025

The Fractured Metropolis: Optimization Cutoffs, Uneven Congestion, and the Spatial Politics of Globalization

The divergence in globalization strategies between the US (retrenchment and polarization) and China (expansion) presents a puzzle that traditional distributional theories fail to fully explain. This paper offers a novel framework by conceptualizing the globalized economy as a "Congestible Club Good," leading to a "Fractured Metropolis." We argue that globalization flows ($M$) are constrained by domestic Institutional Capacity ($K$), which is heterogeneous and historically contingent. We introduce the concept of the "Optimization Cutoff": globalization incentivized the US to bypass costly domestic upgrades in favor of global expansion, leading to the long-term neglect of Public Capacity ($K_{Public}$). This historical path created a deep polarization. "Congested Incumbents," reliant on the stagnant $K_{Public}$, experience globalization as chaos ($MC>MB$), while "Insulated Elites" use Private Capacity ($K_{Private}$) to bypass bottlenecks ($MB>MC$). This divergence paralyzes the consensus needed to restore $K_{Public}$, creating a "Capacity Trap" where protectionism becomes the politically rational, yet economically suboptimal, equilibrium. Empirically, we construct an Institutional Congestion Index using textual analysis (2000-2024), revealing an exponential surge in disorder-related keywords (from 272 hits to 1,333). We triangulate this perception with the material failure of $K_{Public}$, such as the 3.7 million case backlog in US immigration courts. Our findings suggest the crisis of globalization is fundamentally a crisis of uneven institutional capacity and the resulting political paralysis.

econ.GN↗

arXiv2025

"Don't Fall Behind": A Unified Framework of Dynastic Survival, Two-Stage Belief Error, and the Modern Involution Trap

We set out to solve a dual puzzle regarding reproductive strategies: The "Ancient vs. Modern" Puzzle (why pre-modern elites adopted a "Survival" strategy while modern elites adopt an "Anxiety" strategy) and the "Class Divide" Puzzle (why modern involution manifests as a U-shaped fertility pattern). We develop a unified computational framework (DP + Monte Carlo) that introduces Cognitive Heterogeneity across classes. Our Hybrid Model (M-H) posits that the poor act as "Rational Survivors" (M1 utility, Reality parameters), while the middle/rich act as "Biased Strivers" (M4b utility, Belief parameters). Our simulations yield three core findings. First, we confirm that the "Survival" strategy is objectively rational whenever risk exceeds a low threshold ($σ> 0.45$). Given that real-world risk is massive ($σ_{Real} \approx 4.9$), the modern "Quality" strategy is objectively fragile. Second, the trap for the Middle/Rich ($B \ge 200$) is driven by a "Two-Stage Belief Error": they are first "baited" by a Causal Error (underestimating risk) to enter the status game, and then "trapped" by a Marginal Error (underestimating returns) which triggers a stop in fertility. Third, the U-shape is driven by the cognitive divide. The Poor escape the trap by retaining a "Rational Survival" strategy in the face of real high risk. Conversely, the Aspirational Middle Class ($HC \approx 12, B \ge 200$) is uniquely trapped by their Biased Beliefs. Their high competence raises their dynastic reference point ($R$) to a level where, under perceived low returns, restricting fertility to $N=1$ becomes the only rational choice within their biased belief system.

econ.TH↗

arXiv2025

The Distribution of Atomic Hydrogen in the Host Galaxies of FRBs

We probe the atomic hydrogen (HI) emission from the host galaxies of fast radio bursts (FRBs) to investigate the emerging trend of disturbance and asymmetry in the population. Quadrupling the sample size, we detect 16 out of 17 new hosts in HI, with the single non-detection arising in a galaxy known to be transitioning towards quiescence. With respect to typical local Universe galaxies, FRB hosts are generally massive in HI ($M_{HI}>10^9 M_\odot$), which aligns with previous studies reporting that FRB hosts also tend to have high stellar masses and are star-forming. However, they span a broad range of other HI derived properties. Using visual inspection alongside various asymmetry metrics, we identify six unambiguously settled host galaxies, demonstrating for the first time that a disturbed HI morphology is not a universal feature of FRB host galaxies. However, we find another six that show clear signs of disturbance, one borderline case, and three which require deeper or more targeted observations to reach a conclusion; this brings the confirmed ratio of disturbed-to-settled FRB hosts to 11:6. Given that roughly a 1:1 ratio is expected for random background galaxies of similar type, our observed ratio yields a p-value of 0.222. Therefore, we conclude that contrary to earlier indications, there is no statistically significant excess of HI disturbance in this sample of FRB host galaxies with respect to the general galaxy population, and hence we find no evidence for a fundamental connection between FRB progenitor formation and merger-induced star formation activity.

astro-ph.GA↗

arXiv2025

MAISI-v2: Accelerated 3D High-Resolution Medical Image Synthesis with Rectified Flow and Region-specific Contrastive Loss

Medical image synthesis is an important topic for both clinical and research applications. Recently, diffusion models have become a leading approach in this area. Despite their strengths, many existing methods struggle with (1) limited generalizability that only work for specific body regions or voxel spacings, (2) slow inference, which is a common issue for diffusion models, and (3) weak alignment with input conditions, which is a critical issue for medical imaging. MAISI, a previously proposed framework, addresses generalizability issues but still suffers from slow inference and limited condition consistency. In this work, we present MAISI-v2, the first accelerated 3D medical image synthesis framework that integrates rectified flow to enable fast and high quality generation. To further enhance condition fidelity, we introduce a novel region-specific contrastive loss to enhance the sensitivity to region of interest. Our experiments show that MAISI-v2 can achieve SOTA image quality with $33 \times$ acceleration for latent diffusion model. We also conducted a downstream segmentation experiment to show that the synthetic images can be used for data augmentation. We release our code, training details, model weights, and a GUI demo to facilitate reproducibility and promote further development within the community.

cs.CV↗

arXiv2025

Reasoning Visual Language Model for Chest X-Ray Analysis

Vision-language models (VLMs) have shown strong promise for medical image analysis, but most remain opaque, offering predictions without the transparent, stepwise reasoning clinicians rely on. We present a framework that brings chain-of-thought (CoT) reasoning to chest X-ray interpretation. Inspired by reasoning-first training paradigms, our approach is designed to learn how experts reason, not just what they conclude, by aligning intermediate steps with observable image evidence and radiology workflow. Beyond accuracy, the explicit reasoning traces support clinical auditability: they reveal why a conclusion was reached, which alternatives were considered, and where uncertainty remains, enabling quality assurance, error analysis, and safer human-AI collaboration. Our model couples high-fidelity visual encoding with a two-stage training recipe: a reasoning-style supervised fine-tuning (SFT) followed by reinforcement learning (RL) that uses verifiable rewards over a list of X-ray abnormalities. The model outputs reasoning that mirrors radiologists systematic thought process, uncertainty, and differential diagnosis. In out-of-distribution evaluation, the approach achieves competitive multi-label classification while improving interpretability. In a reader study with expert radiologists, full reasoning traces increased confidence, supported error auditing, and reduced time to finalize reports. We release code and the model NV-Reason-CXR-3B to support community progress toward trustworthy, explainable AI in chest radiography and other medical imaging tasks where reasoning quality is as critical as prediction quality.

cs.CV↗

arXiv2025

Auto3DSeg for Brain Tumor Segmentation from 3D MRI in BraTS 2023 Challenge

In this work, we describe our solution to the BraTS 2023 cluster of challenges using Auto3DSeg from MONAI. We participated in all 5 segmentation challenges, and achieved the 1st place results in three of them: Brain Metastasis, Brain Meningioma, BraTS-Africa challenges, and the 2nd place results in the remaining two: Adult and Pediatic Glioma challenges.

cs.CV↗

arXiv2025

Towards the Automatic Segmentation, Modeling and Meshing of the Aortic Vessel Tree from Multicenter Acquisitions: An Overview of the SEG.A. 2023 Segmentation of the Aorta Challenge

The automated analysis of the aortic vessel tree (AVT) from computed tomography angiography (CTA) holds immense clinical potential, but its development has been impeded by a lack of shared, high-quality data. We launched the SEG.A. challenge to catalyze progress in this field by introducing a large, publicly available, multi-institutional dataset for AVT segmentation. The challenge benchmarked automated algorithms on a hidden test set, with subsequent optional tasks in surface meshing for computational simulations. Our findings reveal a clear convergence on deep learning methodologies, with 3D U-Net architectures dominating the top submissions. A key result was that an ensemble of the highest-ranking algorithms significantly outperformed individual models, highlighting the benefits of model fusion. Performance was strongly linked to algorithmic design, particularly the use of customized post-processing steps, and the characteristics of the training data. This initiative not only establishes a new performance benchmark but also provides a lasting resource to drive future innovation toward robust, clinically translatable tools.

cs.CV↗

arXiv2025

DeepOmni: Towards Seamless and Smart Speech Interaction with Adaptive Modality-Specific MoE

Native multimodal large language models (MLLMs) restructure a single large language model (LLM) into a spoken language model (SLM) capable of both speech and text generation. Compared to modular and aligned MLLMs, native MLLMs preserve richer paralinguistic features such as emotion and prosody, and generate speech responses directly within the backbone LLM rather than using a separate speech decoder. This integration also results in lower response latency and smoother interaction. However, native MLLMs suffer from catastrophic forgetting and performance degradation because the available paired speech-text data is insufficient to support the pretraining of MLLMs compared to the vast amount of text data required to pretrain text LLMs. To address this issue, we propose DeepTalk, a framework for adaptive modality expert learning based on a Mixture of Experts (MoE) architecture. DeepTalk first adaptively distinguishes modality experts according to their modality load within the LLM. Each modality expert then undergoes specialized single-modality training, followed by joint multimodal collaborative training. As a result, DeepTalk incurs only a 5.5% performance drop compared to the original LLM, which is significantly lower than the average performance drop of over 20% typically seen in native MLLMs (such as GLM-4-Voice), and is on par with modular MLLMs. Meanwhile, the end-to-end dialogue latency remains within 0.5 seconds, ensuring a seamless and intelligent speech interaction experience. Code and models are released at https://github.com/talkking/DeepTalk.

cs.CL↗

arXiv2025

OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM

Advancing machine intelligence requires developing the ability to perceive across multiple modalities, much as humans sense the world. We introduce OmniVinci, an initiative to build a strong, open-source, omni-modal LLM. We carefully study the design choices across model architecture and data curation. For model architecture, we present three key innovations: (i) OmniAlignNet for strengthening alignment between vision and audio embeddings in a shared omni-modal latent space; (ii) Temporal Embedding Grouping for capturing relative temporal alignment between vision and audio signals; and (iii) Constrained Rotary Time Embedding for encoding absolute temporal information in omni-modal embeddings. We introduce a curation and synthesis pipeline that generates 24M single-modal and omni-modal conversations. We find that modalities reinforce one another in both perception and reasoning. Our model, OmniVinci, outperforms Qwen2.5-Omni with +19.05 on DailyOmni (cross-modal understanding), +1.7 on MMAR (audio), and +3.9 on Video-MME (vision), while using just 0.2T training tokens - a 6 times reduction compared to Qwen2.5-Omni's 1.2T. We finally demonstrate omni-modal advantages in downstream applications spanning robotics, medical AI, and smart factory.

cs.CV↗