arXiv subjects

Bowen Chen

Publications and source records attributed to Bowen Chen.

At least 55 records · Page 3Linked to original sources

arXiv2025

RSRefSeg 2: Decoupling Referring Remote Sensing Image Segmentation with Foundation Models

Referring Remote Sensing Image Segmentation provides a flexible and fine-grained framework for remote sensing scene analysis via vision-language collaborative interpretation. Current approaches predominantly utilize a three-stage pipeline encompassing dual-modal encoding, cross-modal interaction, and pixel decoding. These methods demonstrate significant limitations in managing complex semantic relationships and achieving precise cross-modal alignment, largely due to their coupled processing mechanism that conflates target localization with boundary delineation. This architectural coupling amplifies error propagation under semantic ambiguity while restricting model generalizability and interpretability. To address these issues, we propose RSRefSeg 2, a decoupling paradigm that reformulates the conventional workflow into a collaborative dual-stage framework: coarse localization followed by fine segmentation. RSRefSeg 2 integrates CLIP's cross-modal alignment strength with SAM's segmentation generalizability through strategic foundation model collaboration. Specifically, CLIP is employed as the dual-modal encoder to activate target features within its pre-aligned semantic space and generate localization prompts. To mitigate CLIP's misactivation challenges in multi-entity scenarios described by referring texts, a cascaded second-order prompter is devised, which enhances precision through implicit reasoning via decomposition of text embeddings into complementary semantic subspaces. These optimized semantic prompts subsequently direct the SAM to generate pixel-level refined masks, thereby completing the semantic transmission pipeline. Extensive experiments (RefSegRS, RRSIS-D, and RISBench) demonstrate that RSRefSeg 2 surpasses contemporary methods in segmentation accuracy (+~3% gIoU) and complex semantic interpretation. Code is available at: https://github.com/KyanChen/RSRefSeg2.

cs.CV↗

arXiv2025

Mirror in the Model: Ad Banner Image Generation via Reflective Multi-LLM and Multi-modal Agents

Recent generative models such as GPT-4o have shown strong capabilities in producing high-quality images with accurate text rendering. However, commercial design tasks like advertising banners demand more than visual fidelity -- they require structured layouts, precise typography, consistent branding, and more. In this paper, we introduce MIMO (Mirror In-the-Model), an agentic refinement framework for automatic ad banner generation. MIMO combines a hierarchical multi-modal agent system (MIMO-Core) with a coordination loop (MIMO-Loop) that explores multiple stylistic directions and iteratively improves design quality. Requiring only a simple natural language based prompt and logo image as input, MIMO automatically detects and corrects multiple types of errors during generation. Experiments show that MIMO significantly outperforms existing diffusion and LLM-based baselines in real-world banner design scenarios.

cs.CV↗

arXiv2025

OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent

Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. While LLM-based methods offer automated keyword generation, they face three major limitations: reliance on large-scale query-keyword pair data, lack of online multi-objective performance monitoring and optimization, and weak quality control in keyword selection. These issues hinder the agentic use of LLMs in fully automating keyword decisions by monitoring and reasoning over key performance indicators such as impressions, clicks, conversions, and CTA effectiveness. To overcome these challenges, we propose OMS, a keyword generation framework that is On-the-fly (requires no training data, monitors online performance, and adapts accordingly), Multi-objective (employs agentic reasoning to optimize keywords based on multiple performance metrics), and Self-reflective (agentically evaluates keyword quality). Experiments on benchmarks and real-world ad campaigns show that OMS outperforms existing methods; ablation and human evaluations confirm the effectiveness of each component and the quality of generated keywords.

cs.AI↗

arXiv2025

Unveiling the multiband metallic nature of the normal state in nickelate La3Ni2O7

The discovery of unconventional superconductivity around 80 K in perovskite nickelates under high pressure has furnished a new platform to explore high-temperature unconventional superconductivity in addition to cuprates. Understanding the normal state of nickelate superconductors is crucial to uncovering the origin of this unconventional superconductivity and gaining further insight into its underlying mechanism. In this study, we systemically studied the transport properties of La3Ni2O7 by tuning the pressure under high magnetic fields. Magnetoresistance (MR) consistently exhibits a quasi-quadratic dependence on the magnetic field across all measured pressures and temperatures. Increased pressure enhances the metallicity of the system and leads to a monotonic increase in MR, which follows the extended Kohler's rule. These results suggest that the normal state of La3Ni2O7 to be a multiband metallic nature.

cond-mat.supr-con↗

arXiv2025

XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation

Achieving fine-grained control over subject identity and semantic attributes (pose, style, lighting) in text-to-image generation, particularly for multiple subjects, often undermines the editability and coherence of Diffusion Transformers (DiTs). Many approaches introduce artifacts or suffer from attribute entanglement. To overcome these challenges, we propose a novel multi-subject controlled generation model XVerse. By transforming reference images into offsets for token-specific text-stream modulation, XVerse allows for precise and independent control for specific subject without disrupting image latents or features. Consequently, XVerse offers high-fidelity, editable multi-subject image synthesis with robust control over individual subject characteristics and semantic attributes. This advancement significantly improves personalized and complex scene generation capabilities.

cs.CV↗

arXiv2025

Improving Compiler Bug Isolation by Leveraging Large Language Models

Compilers play a foundational role in building reliable software systems, and bugs within them can lead to catastrophic consequences. The compilation process typically involves hundreds of files, making traditional automated bug isolation techniques inapplicable due to scalability or effectiveness issues. Current mainstream compiler bug localization techniques have limitations in test program mutation and resource consumption. Inspired by the recent advances of pre-trained Large Language Models (LLMs), we propose an innovative approach named AutoCBI, which (1) uses LLMs to summarize compiler file functions and (2) employs specialized prompts to guide LLM in reordering suspicious file rankings. This approach leverages four types of information: the failing test program, source file function summaries, lists of suspicious files identified through analyzing test coverage, as well as compilation configurations with related output messages, resulting in a refined ranking of suspicious files. Our evaluation of AutoCBI against state-of-the-art approaches (DiWi, RecBi and FuseFL) on 120 real-world bugs from the widely-used GCC and LLVM compilers demonstrates its effectiveness. Specifically, AutoCBI isolates 66.67%/69.23%, 300%/340%, and 100%/57.14% more bugs than RecBi, DiWi, and FuseFL, respectively, in the Top-1 ranked results for GCC/LLVM. Additionally, the ablation study underscores the significance of each component in our approach.

cs.SE↗

arXiv2025

Uncertainty-Aware Metabolic Stability Prediction with Dual-View Contrastive Learning

Accurate prediction of molecular metabolic stability (MS) is critical for drug research and development but remains challenging due to the complex interplay of molecular interactions. Despite recent advances in graph neural networks (GNNs) for MS prediction, current approaches face two critical limitations: (1) incomplete molecular modeling due to atom-centric message-passing mechanisms that disregard bond-level topological features, and (2) prediction frameworks that lack reliable uncertainty quantification. To address these challenges, we propose TrustworthyMS, a novel contrastive learning framework designed for uncertainty-aware metabolic stability prediction. First, a molecular graph topology remapping mechanism synchronizes atom-bond interactions through edge-induced feature propagation, capturing both localized electronic effects and global conformational constraints. Second, contrastive topology-bond alignment enforces consistency between molecular topology views and bond patterns via feature alignment, enhancing representation robustness. Third, uncertainty modeling through Beta-Binomial uncertainty quantification enables simultaneous prediction and confidence calibration under epistemic uncertainty. Through extensive experiments, our results demonstrate that TrustworthyMS outperforms current state-of-the-art methods in terms of predictive performance.

cs.LG↗

arXiv2025

HDRSDR-VQA: A Subjective Video Quality Dataset for HDR and SDR Comparative Evaluation

We introduce HDRSDR-VQA, a large-scale video quality assessment dataset designed to facilitate comparative analysis between High Dynamic Range (HDR) and Standard Dynamic Range (SDR) content under realistic viewing conditions. The dataset comprises 960 videos generated from 54 diverse source sequences, each presented in both HDR and SDR formats across nine distortion levels. To obtain reliable perceptual quality scores, we conducted a comprehensive subjective study involving 145 participants and six consumer-grade HDR-capable televisions. A total of over 22,000 pairwise comparisons were collected and scaled into Just-Objectionable-Difference (JOD) scores. Unlike prior datasets that focus on a single dynamic range format or use limited evaluation protocols, HDRSDR-VQA enables direct content-level comparison between HDR and SDR versions, supporting detailed investigations into when and why one format is preferred over the other. The open-sourced part of the dataset is publicly available to support further research in video quality assessment, content-adaptive streaming, and perceptual model development.

cs.CV↗

arXiv2025

Hybrid and scalable photonic circuit cavity quantum electrodynamics

Similar to superconducting circuit quantum electrodynamics (cQED), the development of a photonic analog--specifically, photonic circuit cQED--has become a major focus in integrated quantum photonics. Current solid-state cQED devices, however, face scalability challenges due to the difficulty in simultaneously spectral tuning of cavity modes and quantum emitters while ensuring in-plane optical modes confinement for efficient on-chip light routing. Here, we overcome these limitations by proposing and demonstrating a hybrid solid-state cQED platform integrated on a chip. Our device integrates semiconducting quantum dots (QDs) with a thin-film lithium niobate (TFLN) microring resonator. Leveraging TFLN's ferroelectric and electro-optic (EO) properties, we implement local spectral tuning of both waveguide-coupled QDs and cavity modes. This approach achieves a broad spectral tuning range of up to 4.82 nm for individual QDs, enabling deterministic on-chip single-photon emission with a Purcell factor of 3.52. When combined with EO cavity tuning, we realize a spectrally tunable hybrid photonic circuit cQED device, sustaining near-constant Purcell factors of 1.89 over a 0.30 nm spectral range. This achievement enables scalable on-chip cavity-enhanced single-photon sources while preserving optical properties and maintaining compatibility with advanced photonic architectures, marking a significant step toward practical implementation of large-scale chip-based quantum networks.

quant-ph↗

arXiv2025

Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution (SR) aims to reconstruct high-resolution remote sensing images from low-resolution inputs, thereby addressing limitations imposed by sensors and imaging conditions. However, the inherent characteristics of remote sensing images, including diverse ground object types and complex details, pose significant challenges to achieving high-quality reconstruction. Existing methods typically employ a uniform structure to process various types of ground objects without distinction, making it difficult to adapt to the complex characteristics of remote sensing images. To address this issue, we introduce a Mixture of Experts (MoE) model and design a set of heterogeneous experts. These experts are organized into multiple expert groups, where experts within each group are homogeneous while being heterogeneous across groups. This design ensures that specialized activation parameters can be employed to handle the diverse and intricate details of ground objects effectively. To better accommodate the heterogeneous experts, we propose a multi-level feature aggregation strategy to guide the routing process. Additionally, we develop a dual-routing mechanism to adaptively select the optimal expert for each pixel. Experiments conducted on the UCMerced and AID datasets demonstrate that our proposed method achieves superior SR reconstruction accuracy compared to state-of-the-art methods. The code will be available at https://github.com/Mr-Bamboo/MFG-HMoE.

eess.IV↗

arXiv2025

Large-scale quantum-dot-lithium-niobate hybrid integrated photonic circuits enabling on-chip quantum networking

Hybrid integrated quantum photonics combines solid-state artificial atoms with reconfigurable photonic circuits, enabling scalable chip-based quantum networks. Self-assembled quantum dots (QDs) are ideal for this goal due to their ability to generate highly indistinguishable single photons with exceptional brightness. Integrating QDs into low-loss photonic circuits can facilitate complex quantum networks by enabling entanglement transfer via two-photon interference. However, challenges such as limited scalability, spectral inhomogeneity, and quantum interference between independent sources remain. We present a hybrid photonic architecture that integrates QD-containing waveguides with low-loss lithium niobate (LN) circuits, incorporating 20 deterministic single-photon sources (SPSs). Using the piezoelectric properties of thin-film lithium niobate (TFLN), we achieve on-chip local spectral tuning of QD emissions by up to 7.7 meV,three orders of magnitude greater than the transform-limited linewidth. This approach enables on-chip quantum interference with a visibility of 0.73 between two spatially separated QD SPSs connected by 0.48 mm long waveguides, establishing a functional quantum network.The large-scale integration of spectrally tunable QD-based SPSs into low-loss LN circuits, combined with fast electro-optical switching, paves the way for compact, lightweight, and scalable photonic quantum networks.

quant-ph↗

arXiv2025

Text-Derived Relational Graph-Enhanced Network for Skeleton-Based Action Segmentation

Skeleton-based Temporal Action Segmentation (STAS) aims to segment and recognize various actions from long, untrimmed sequences of human skeletal movements. Current STAS methods typically employ spatio-temporal modeling to establish dependencies among joints as well as frames, and utilize one-hot encoding with cross-entropy loss for frame-wise classification supervision. However, these methods overlook the intrinsic correlations among joints and actions within skeletal features, leading to a limited understanding of human movements. To address this, we propose a Text-Derived Relational Graph-Enhanced Network (TRG-Net) that leverages prior graphs generated by Large Language Models (LLM) to enhance both modeling and supervision. For modeling, the Dynamic Spatio-Temporal Fusion Modeling (DSFM) method incorporates Text-Derived Joint Graphs (TJG) with channel- and frame-level dynamic adaptation to effectively model spatial relations, while integrating spatio-temporal core features during temporal modeling. For supervision, the Absolute-Relative Inter-Class Supervision (ARIS) method employs contrastive learning between action features and text embeddings to regularize the absolute class distributions, and utilizes Text-Derived Action Graphs (TAG) to capture the relative inter-class relationships among action features. Additionally, we propose a Spatial-Aware Enhancement Processing (SAEP) method, which incorporates random joint occlusion and axial rotation to enhance spatial generalization. Performance evaluations on four public datasets demonstrate that TRG-Net achieves state-of-the-art results.

cs.CV↗

arXiv2025

TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records

Large language models (LLMs) have emerged as promising tools for assisting in medical tasks, yet processing Electronic Health Records (EHRs) presents unique challenges due to their longitudinal nature. While LLMs' capabilities to perform medical tasks continue to improve, their ability to reason over temporal dependencies across multiple patient visits and time frames remains unexplored. We introduce TIMER (Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records), a framework that incorporate instruction-response pairs grounding to different parts of a patient's record as a critical dimension in both instruction evaluation and tuning for longitudinal clinical records. We develop TIMER-Bench, the first time-aware benchmark that evaluates temporal reasoning capabilities over longitudinal EHRs, as well as TIMER-Instruct, an instruction-tuning methodology for LLMs to learn reasoning over time. We demonstrate that models fine-tuned with TIMER-Instruct improve performance by 7.3% on human-generated benchmarks and 9.2% on TIMER-Bench, indicating that temporal instruction-tuning improves model performance for reasoning over EHR.

cs.AI↗

arXiv2025

AI-driven 3D Spatial Transcriptomics

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology-transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://vortex-demo.github.io/

cs.CV↗

arXiv2025

Stable Soliton Microcomb Generation in X-cut Lithium Tantalate via Thermal-Assisted Photorefractive Suppression

Chip-based soliton frequency microcombs combine compact size, broad bandwidth, and high coherence, presenting a promising solution for integrated optical telecommunications, precision sensing, and spectroscopy. Recent progress in ferroelectric thin films, particularly thin-film Lithium niobate (LN) and thin-film Lithium tantalate (LT), has significantly advanced electro-optic (EO) modulation and soliton microcombs generation, leveraging their strong third-order nonlinearity and high Pockels coefficients. However, achieving soliton frequency combs in X-cut ferroelectric materials remains challenging due to the competing effects of thermo-optic and photorefractive phenomena. These issues hinder the simultaneous realization of soliton generation and high-speed EO modulation. Here, following the thermal-regulated carrier behaviour and auxiliary-laser-assisted approach, we propose a convenient mechanism to suppress both photorefractive and thermal dragging effect at once, and implement a facile method for soliton formation and its long-term stabilization in integrated X-cut LT microresonators for the first time. The resulting mode-locked states exhibit robust stability against perturbations, enabling new pathways for fully integrated photonic circuits that combine Kerr nonlinearity with high-speed EO functionality.

physics.optics↗

arXiv2025

Magic-Boost: Boost 3D Generation with Multi-View Conditioned Diffusion

Benefiting from the rapid development of 2D diffusion models, 3D content generation has witnessed significant progress. One promising solution is to finetune the pre-trained 2D diffusion models to produce multi-view images and then reconstruct them into 3D assets via feed-forward sparse-view reconstruction models. However, limited by the 3D inconsistency in the generated multi-view images and the low reconstruction resolution of the feed-forward reconstruction models, the generated 3d assets are still limited to incorrect geometries and blurry textures. To address this problem, we present a multi-view based refine method, named Magic-Boost, to further refine the generation results. In detail, we first propose a novel multi-view conditioned diffusion model which extracts 3d prior from the synthesized multi-view images to synthesize high-fidelity novel view images and then introduce a novel iterative-update strategy to adopt it to provide precise guidance to refine the coarse generated results through a fast optimization process. Conditioned on the strong 3d priors extracted from the synthesized multi-view images, Magic-Boost is capable of providing precise optimization guidance that well aligns with the coarse generated 3D assets, enriching the local detail in both geometry and texture within a short time ($\sim15$min). Extensive experiments show Magic-Boost greatly enhances the coarse generated inputs, generates high-quality 3D assets with rich geometric and textural details. (Project Page: https://magic-research.github.io/magic-boost/)

cs.CV↗

arXiv2024

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs

This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and strong Japanese LLMs, and as of this writing, more than 1,500 participants from academia and industry are working together for this purpose. This paper presents the background of the establishment of LLM-jp, summaries of its activities, and technical reports on the LLMs developed by LLM-jp. For the latest activities, visit https://llm-jp.nii.ac.jp/en/.

cs.CL↗

arXiv2024

A Statistical and Multi-Perspective Revisiting of the Membership Inference Attack in Large Language Models

The lack of data transparency in Large Language Models (LLMs) has highlighted the importance of Membership Inference Attack (MIA), which differentiates trained (member) and untrained (non-member) data. Though it shows success in previous studies, recent research reported a near-random performance in different settings, highlighting a significant performance inconsistency. We assume that a single setting doesn't represent the distribution of the vast corpora, causing members and non-members with different distributions to be sampled and causing inconsistency. In this study, instead of a single setting, we statistically revisit MIA methods from various settings with thousands of experiments for each MIA method, along with study in text feature, embedding, threshold decision, and decoding dynamics of members and non-members. We found that (1) MIA performance improves with model size and varies with domains, while most methods do not statistically outperform baselines, (2) Though MIA performance is generally low, a notable amount of differentiable member and non-member outliers exists and vary across MIA methods, (3) Deciding a threshold to separate members and non-members is an overlooked challenge, (4) Text dissimilarity and long text benefit MIA performance, (5) Differentiable or not is reflected in the LLM embedding, (6) Member and non-members show different decoding dynamics.

cs.CL↗