arXiv subjects

Yuchen Guo

Publications and source records attributed to Yuchen Guo.

At least 37 records · Page 2Linked to original sources

arXiv2026

LumiVideo: An Intelligent Agentic System for Video Color Grading

Video color grading is a critical post-production process that transforms flat, log-encoded raw footage into emotionally resonant cinematic visuals. Existing automated methods act as static, black-box executors that directly output edited pixels, lacking both interpretability and the iterative control required by professionals. We introduce LumiVideo, an agentic system that mimics the cognitive workflow of professional colorists through four stages: Perception, Reasoning, Execution, and Reflection. Given only raw log video, LumiVideo autonomously produces a cinematic base grade by analyzing the scene's physical lighting and semantic content. Its Reasoning engine synergizes an LLM's internalized cinematic knowledge with a Retrieval-Augmented Generation (RAG) framework via a Tree of Thoughts (ToT) search to navigate the non-linear color parameter space. Rather than generating pixels, the system compiles the deduced parameters into industry-standard ASC-CDL configurations and a globally consistent 3D LUT, analytically guaranteeing temporal consistency. An optional Reflection loop then allows creators to refine the result via natural language feedback. We further introduce LumiGrade, the first log-encoded video benchmark for evaluating automated grading. Experiments show that LumiVideo approaches human expert quality in fully automatic mode while enabling precise iterative control when directed.

cs.CV↗

arXiv2026

Fuse4Seg: Image Fusion for Multi-Modal Medical Segmentation via Bi-level Optimization

Multi-modal medical image fusion is traditionally optimized for human visual perception, aiming to maximize generic contrast and structural fidelity. However, when these visually pleasing fused images are deployed in automated clinical workflows, this visual-semantic discrepancy causes task-agnostic feature degradation, inadvertently smoothing out critical, high-frequency tumor boundaries. To bridge this semantic gap, we propose Fuse4Seg, a novel framework that reformulates multi-modal fusion as a cooperative bi-level optimization problem with medical segmentation. Rather than relying on rigid visual metrics, our fusion leader dynamically updates its feature extraction strategy driven directly by semantic gradients backpropagated from the downstream segmentation follower. To guarantee robust physical fidelity alongside semantic utility, we design a frequency-decoupled architecture stringently regularized by a Frequency Decomposition Loss and a Spatial Gradient Loss. This explicit physical anchor prevents anatomical distortion and ensures the lossless preservation of task-critical details. Extensive experiments demonstrate that our task-aware, single-channel fused prior generalizes seamlessly across diverse multi-scale modalities. More impressively, it remarkably surpasses contemporary dual-channel segmentation state-of-the-arts while explicitly providing a readable, "glass-box" physical image to foster clinical visual interpretability and trust.

cs.CV↗

arXiv2026

Quantum criticality in open quantum systems from the purification perspective

Open quantum systems host mixed-state phases that go beyond the symmetry-protected topological and spontaneous symmetry-breaking paradigms established for closed, pure-state systems. Developing a unified and physically transparent classification of such phases remains a central challenge. In this work, we introduce a purification-based framework that systematically characterizes all mixed-state phases in one-dimensional systems with $\mathbb{Z}_2^σ \times \mathbb{Z}_2^τ$ symmetry. By introducing an ancillary $κ$ chain and employing decorated domain-wall constructions, we derive eight purified fixed-point Hamiltonians labeled by topological indices $(μ_{στ},μ_{τκ},μ_{κσ}) \in \{\pm1\}^3$. Tracing out the ancilla recovers the full structure of mixed-state phases, including symmetric, strong-to-weak spontaneous symmetry breaking, average symmetry-protected topological phases, and their nontrivial combinations. Interpolations between the eight fixed points naturally define a three-dimensional phase diagram with a cube geometry. The edges correspond to elementary transitions associated with single topological indices, while the faces host intermediate phases arising from competing domain-wall decorations. Along the edges, we identify a class of critical behavior that connects distinct strong-to-weak symmetry-breaking patterns associated with distinct strong subgroups, highlighting a mechanism unique to mixed-state settings. Large-scale tensor-network simulations reveal a rich phase structure, including pyramid-shaped symmetry-breaking regions and a fully symmetry-broken phase at the cube center. Overall, our purification approach provides a geometrically transparent and physically complete classification of mixed-state phases, unified with a single $\mathbb{Z}_2^σ \times \mathbb{Z}_2^τ \times \mathbb{Z}_2^κ$ model.

quant-ph↗

arXiv2026

A Morphology Catalog of Galaxies in CEERS: Evolution in the Size and Color Gradients of Galaxies Since Cosmic Dawn

We present measurements of morphological parameters from fitting 53,885 galaxies detected to a magnitude limit of F356W$< 28.5$ in the CEERS NIRCam imaging with galfit in six broadband filters: F115W, F150W, F200W, F277W, F356W, and F444W. We provide a public catalog of Sérsic index, effective semi-major axis, axis ratio, integrated magnitude, and position angle for these galaxies in each of the filters. Uncertainties in the measured parameters are estimated from simulated galaxies that have similar noise and background properties as the observed galaxies. We compare our measurements with those in the CANDELS/EGS field measured with HST/WFC3 and find that the sizes agree to within 0.09 dex and the Sérsic indices agree to within 0.13 dex. We further present the evolution in the size-mass relation, and find that the evolution to $z\sim9$ is consistent with previous results derived at lower redshift. Finally, we look at the color gradients of galaxies at $1 2.5$), the color gradients are nearly flat with no dependence on mass, indicating that the stellar populations are more uniform throughout. The structural measurements presented are accurate to $20\%$ or better for most galaxies with F356W $<27.0$ mag and will enable further studies of galaxy morphology to $z\sim10$.

astro-ph.GA↗

arXiv2026

A-MapReduce: Executing Wide Search via Agentic MapReduce

Contemporary large language model (LLM)-based multi-agent systems exhibit systematic advantages in deep research tasks, which emphasize iterative, vertically structured information seeking. However, when confronted with wide search tasks characterized by large-scale, breadth-oriented retrieval, existing agentic frameworks, primarily designed around sequential, vertically structured reasoning, remain stuck in expansive search objectives and inefficient long-horizon execution. To bridge this gap, we propose A-MapReduce, a MapReduce paradigm-inspired multi-agent execution framework that recasts wide search as a horizontally structured retrieval problem. Concretely, A-MapReduce implements parallel processing of massive retrieval targets through task-adaptive decomposition and structured result aggregation. Meanwhile, it leverages experiential memory to drive the continual evolution of query-conditioned task allocation and recomposition, enabling progressive improvement in large-scale wide-search regimes. Extensive experiments on five agentic benchmarks demonstrate that A-MapReduce is (i) high-performing, achieving state-of-the-art performance on WideSearch and DeepWideSearch, and delivering 5.11% - 17.50% average Item F1 improvements compared with strong baselines with OpenAI o3 or Gemini 2.5 Pro backbones; (ii) cost-effective and efficient, delivering superior cost-performance trade-offs and reducing running time by 45.8\% compared to representative multi-agent baselines. The code is available at https://github.com/mingju-c/AMapReduce.

cs.MA↗

arXiv2025

A New Framework for Quantum Phases in Open Systems: Steady State of Imaginary-Time Lindbladian Evolution

This study delves into the concept of quantum phases in open quantum systems, examining the shortcomings of existing approaches that focus on steady states of Lindbladians and highlighting their limitations in capturing key phase transitions. In contrast to these methods, we introduce the concept of imaginary-time Lindbladian evolution as an alternative framework. This new approach defines gapped quantum phases in open systems through the spectrum properties of the imaginary-Liouville superoperator. We find that, in addition to all pure gapped ground states, the Gibbs state of a stabilizer Hamiltonian at any finite temperature can also be characterized by our scheme, demonstrated through explicit construction. Moreover, the closing of the imaginary Liouville gap is associated with the divergence of the Markov length, which has recently been proposed as an indicator of phase transitions in open quantum systems. To illustrate the effectiveness of this framework, we apply it to investigate the phase diagram for open systems with $\mathbb{Z}_2^σ\times \mathbb{Z}_2^τ$ symmetry, including cases with nontrivial average symmetry protected topological order or spontaneous symmetry breaking order. Our findings demonstrate universal properties at quantum criticality, such as nonanalytic behaviors of steady-state observables, divergence of correlation lengths, and closing of the imaginary-Liouville gap. These results advance our understanding of quantum phase transitions in open quantum systems. In contrast, we find that the steady states of real-time Lindbladians do not provide an effective framework for characterizing phase transitions in open systems.

quant-ph↗

arXiv2025

Diverse Teaching and Label Propagation for Generic Semi-Supervised Medical Image Segmentation

Both limited annotation and domain shift are significant challenges frequently encountered in medical image segmentation, leading to derivative scenarios like semi-supervised medical (SSMIS), semi-supervised medical domain generalization (Semi-MDG) and unsupervised medical domain adaptation (UMDA). Conventional methods are generally tailored to specific tasks in isolation, the error accumulation hinders the effective utilization of unlabeled data and limits further improvements, resulting in suboptimal performance when these issues occur. In this paper, we aim to develop a generic framework that masters all three tasks. We found that the key to solving the problem lies in how to generate reliable pseudo labels for the unlabeled data in the presence of domain shift with labeled data and increasing the diversity of the model. To tackle this issue, we employ a Diverse Teaching and Label Propagation Network (DTLP-Net) to boosting the Generic Semi-Supervised Medical Image Segmentation. Our DTLP-Net involves a single student model and two diverse teacher models, which can generate reliable pseudo-labels for the student model. The first teacher model decouple the training process with labeled and unlabeled data, The second teacher is momentum-updated periodically, thus generating reliable yet divers pseudo-labels. To fully utilize the information within the data, we adopt inter-sample and intra-sample data augmentation to learn the global and local knowledge. In addition, to further capture the voxel-level correlations, we propose label propagation to enhance the model robust. We evaluate our proposed framework on five benchmark datasets for SSMIS, UMDA, and Semi-MDG tasks. The results showcase notable improvements compared to state-of-the-art methods across all five settings, indicating the potential of our framework to tackle more challenging SSL scenarios.

cs.CV↗

arXiv2025

The CEERS Photometric and Physical Parameter Catalog

We present the Cosmic Evolution Early Release Science Survey (CEERS) catalog, including space-based photometry, photometric redshifts, and physical parameters for more than 80,000 galaxies. The imaging used for this catalog comes from the CEERS survey, which has NIRCam coverage over ~100 sq. arcmin of the Extended Groth Strip (EGS) in seven filters from 1.15$μ$m to 4.44$μ$m. Alongside these data, we also include ancillary HST imaging in seven filters from 0.435$μ$m to 1.6$μ$m. We used Source Extractor with hot and cold detection settings to extract photometry. We derive photometric redshifts using the spectral energy distribution (SED) modeling code, LePHARE, and estimate their accuracy using spectroscopically confirmed galaxies out to $z\sim10$, with $σ_{NMAD}$ ranging from 0.035-0.073, depending strongly on galaxy magnitude and redshift. We compute stellar masses, star formation rates, and E(B-V) using three different SED fitting codes with different templates and assumptions about the galaxy star formation histories. All of these measurements, as well as the full mosaics in all filters, and redshift probability distribution functions, are made available via the CEERS DR1.0 data release.

astro-ph.GA↗

arXiv2025

Towards a Unified Textual Graph Framework for Spectral Reasoning via Physical and Chemical Information Fusion

Motivated by the limitations of current spectral analysis methods-such as reliance on single-modality data, limited generalizability, and poor interpretability-we propose a novel multi-modal spectral analysis framework that integrates prior knowledge graphs with Large Language Models. Our method explicitly bridges physical spectral measurements and chemical structural semantics by representing them in a unified Textual Graph format, enabling flexible, interpretable, and generalizable spectral understanding. Raw spectra are first transformed into TAGs, where nodes and edges are enriched with textual attributes describing both spectral properties and chemical context. These are then merged with relevant prior knowledge-including functional groups and molecular graphs-to form a Task Graph that incorporates "Prompt Nodes" supporting LLM-based contextual reasoning. A Graph Neural Network further processes this structure to complete downstream tasks. This unified design enables seamless multi-modal integration and automated feature decoding with minimal manual annotation. Our framework achieves consistently high performance across multiple spectral analysis tasks, including node-level, edge-level, and graph-level classification. It demonstrates robust generalization in both zero-shot and few-shot settings, highlighting its effectiveness in learning from limited data and supporting in-context reasoning. This work establishes a scalable and interpretable foundation for LLM-driven spectral analysis, unifying physical and chemical modalities for scientific applications.

cs.LG↗

arXiv2025

Unveiling Stripe-shaped Charge Density Modulations in Doped Mott Insulators

Inspired by recent experimental findings, we investigate various scenarios of the doped Hubbard model with impurity potentials. We calculate the lattice Green's function in a finite-size cluster and then map it to the continuum real space, which allows for a direct comparison with scanning-tunneling-microscopy measurements on the local density of states. Our simulations successfully reproduce experimental data, including the characteristic stripe- and ladder-shaped structures observed in cuprate systems. Moreover, our results establish a connection between previous numerical findings on stripe-ordered ground states and experimental observations, thus providing new insights into microscopic mechanisms of the Mott insulator to superconductor transition in cuprates.

cond-mat.str-el↗

arXiv2025

Flavor Physics at the CEPC: a General Perspective

We discuss the landscape of flavor physics at the Circular Electron-Positron Collider (CEPC), based on the nominal luminosity outlined in its Technical Design Report. The CEPC is designed to operate in multiple modes to address a variety of tasks. At the $Z$ pole, the expected production of 4 Tera $Z$ bosons will provide unique and highly precise measurements of $Z$ boson couplings, while the substantial number of boosted heavy-flavored quarks and leptons produced in clean $Z$ decays will facilitate investigations into their flavor physics with unprecedented precision. We investigate the prospects of measuring various physics benchmarks and discuss their implications for particle theories and phenomenological models. Our studies indicate that, with its highlighted advantages and anticipated excellent detector performance, the CEPC can explore beauty and $τ$ physics in ways that are superior to or complementary with the Belle II and Large-Hadron-Collider-beauty experiments, potentially enabling the detection of new physics at energy scales of 10 TeV and above. This potential also extends to the observation of yet-to-be-discovered rare and exotic processes, as well as testing fundamental principles such as lepton flavor universality, lepton and baryon number conservation, etc., making the CEPC a vibrant platform for flavor physics research. The $WW$ threshold scan, Higgs-factory operation and top-pair productions of the CEPC further enhance its merits in this regard, especially for measuring the Cabibbo-Kobayashi-Maskawa matrix elements, and Flavor-Changing-Neutral-Current physics of Higgs boson and top quarks. We outline the requirements for detector performance and considerations for future development to achieve the anticipated scientific goals.

hep-ex↗

arXiv2025

Efficient Quantum Circuit Compilation for Near-Term Quantum Advantage

Quantum noise in real-world devices poses a significant challenge in achieving practical quantum advantage, since accurately compiled and executed circuits are typically deep and highly susceptible to decoherence. To facilitate the implementation of complex quantum algorithms on noisy hardware, we propose an approximate method for compiling target quantum circuits into brick-wall layouts. This new circuit design consists of two-qubit CNOT gates that can be directly implemented on real quantum computers, in conjunction with optimized one-qubit gates, to approximate the essential dynamics of the original circuit while significantly reducing its depth. Our approach is evaluated through numerical simulations of time-evolution circuits for the critical Ising model, quantum Fourier transformation, and Haar-random quantum circuits, as well as experiments on IBM quantum platforms. By accounting for compilation error and circuit noise, we demonstrate that time evolution and quantum Fourier transformation circuits achieve high compression rates, while random quantum circuits are less compressible. The degree of compression is related to the rate of entanglement accumulation in the target circuit. In particular, experiments on IBM platforms achieve a compression rate of $12.5$ for $N=12$, significantly extending the application of current quantum devices. Furthermore, large-scale numerical simulations for system sizes up to $N=30$ reveal that the optimal depth $d_{\rm max}$ to achieve maximal overall fidelity is independent of system size $N$, suggesting the scalability of our method for large quantum devices in terms of quantum resources.

quant-ph↗

arXiv2025

Measuring Human Involvement in AI-Generated Text: A Case Study on Academic Writing

Content creation has dramatically progressed with the rapid advancement of large language models like ChatGPT and Claude. While this progress has greatly enhanced various aspects of life and work, it has also negatively affected certain areas of society. A recent survey revealed that nearly 30% of college students use generative AI to help write academic papers and reports. Most countermeasures treat the detection of AI-generated text as a binary classification task and thus lack robustness. This approach overlooks human involvement in the generation of content even though human-machine collaboration is becoming mainstream. Besides generating entire texts, people may use machines to complete or revise texts. Such human involvement varies case by case, which makes binary classification a less than satisfactory approach. We refer to this situation as participation detection obfuscation. We propose using BERTScore as a metric to measure human involvement in the generation process and a multi-task RoBERTa-based regressor trained on a token classification task to address this problem. To evaluate the effectiveness of this approach, we simulated academic-based scenarios and created a continuous dataset reflecting various levels of human involvement. All of the existing detectors we examined failed to detect the level of human involvement on this dataset. Our method, however, succeeded (F1 score of 0.9423 and a regressor mean squared error of 0.004). Moreover, it demonstrated some generalizability across generative models. Our code is available at https://github.com/gyc-nii/CAS-CS-and-dual-head-detector

cs.CL↗

arXiv2025

DAE-Fuse: An Adaptive Discriminative Autoencoder for Multi-Modality Image Fusion

In extreme scenarios such as nighttime or low-visibility environments, achieving reliable perception is critical for applications like autonomous driving, robotics, and surveillance. Multi-modality image fusion, particularly integrating infrared imaging, offers a robust solution by combining complementary information from different modalities to enhance scene understanding and decision-making. However, current methods face significant limitations: GAN-based approaches often produce blurry images that lack fine-grained details, while AE-based methods may introduce bias toward specific modalities, leading to unnatural fusion results. To address these challenges, we propose DAE-Fuse, a novel two-phase discriminative autoencoder framework that generates sharp and natural fused images. Furthermore, We pioneer the extension of image fusion techniques from static images to the video domain while preserving temporal consistency across frames, thus advancing the perceptual capabilities required for autonomous navigation. Extensive experiments on public datasets demonstrate that DAE-Fuse achieves state-of-the-art performance on multiple benchmarks, with superior generalizability to tasks like medical image fusion.

cs.CV↗

arXiv2025

Galaxy Zoo CEERS: Bar fractions up to z~4.0

We study the evolution of the bar fraction in disc galaxies between $0.5 < z < 4.0$ using multi-band coloured images from JWST CEERS. These images were classified by citizen scientists in a new phase of the Galaxy Zoo project called GZ CEERS. Citizen scientists were asked whether a strong or weak bar was visible in the host galaxy. After considering multiple corrections for observational biases, we find that the bar fraction decreases with redshift in our volume-limited sample (n = 398); from $25^{+6}_{-4}$% at $0.5 < z < 1.0$ to $3^{+6}_{-1}$% at $3.0 < z < 4.0$. However, we argue it is appropriate to interpret these fractions as lower limits. Disentangling real changes in the bar fraction from detection biases remains challenging. Nevertheless, we find a significant number of bars up to $z = 2.5$. This implies that discs are dynamically cool or baryon-dominated, enabling them to host bars. This also suggests that bar-driven secular evolution likely plays an important role at higher redshifts. When we distinguish between strong and weak bars, we find that the weak bar fraction decreases with increasing redshift. In contrast, the strong bar fraction is constant between $0.5 < z < 2.5$. This implies that the strong bars found in this work are robust long-lived structures, unless the rate of bar destruction is similar to the rate of bar formation. Finally, our results are consistent with disc instabilities being the dominant mode of bar formation at lower redshifts, while bar formation through interactions and mergers is more common at higher redshifts.

astro-ph.GA↗

arXiv2025

Strong-to-weak spontaneous symmetry breaking meets average symmetry-protected topological order

Recent studies have unveiled new possibilities for discovering intrinsic quantum phases that are unique to open systems, including phases with average symmetry-protected topological (ASPT) order and strong-to-weak spontaneous symmetry breaking (SWSSB) order in systems with global symmetry. In this work, we propose a new class of phases, termed the double ASPT phase, which emerges from a nontrivial extension of these two orders. This new phase is absent from prior studies and cannot exist in conventional closed systems. Using the recently developed imaginary-Lindbladian formalism, we explore the phase diagram of a one-dimensional open system with $\mathbb{Z}_2^σ\times \mathbb{Z}_2^τ$ symmetry. We identify universal critical behaviors along each critical line and observe the emergence of an intermediate phase that completely breaks the $\mathbb{Z}_2^σ$ symmetry, leading to the formation of two triple points in the phase diagram. These two triple points are topologically distinct and connected by a domain-wall decoration duality map. Our results promote the establishment of a complete classification for quantum phases in open systems with various symmetry conditions.

cond-mat.str-el↗

arXiv2025

Locally Purified Density Operators for Symmetry-Protected Topological Phases in Mixed States

We propose a tensor network approach known as the locally purified density operator (LPDO) to investigate the classification and characterization of symmetry-protected topological (SPT) phases in open quantum systems. We extend the concept of injectivity, originally associated with matrix product states and projected entangled pair states, to LPDOs in $(1+1)D$ and $(2+1)D$ systems, unveiling two distinct types of injectivity conditions inherent in short-range entangled density matrices. Within the LPDO framework, we outline a classification scheme for decohered average symmetry-protected topological (ASPT) phases, consistent with earlier results obtained through spectrum sequence techniques. We first illustrate our framework with ASPT phases protected by fermion parity symmetry, then extend the classification of ASPT phases to a general group extension. We demonstrate examples of explicit construction of fixed-point LPDOs for ASPT phases including intrinsic ASPTs in both $(1+1)D$ and $(2+1)D$ systems.

cond-mat.str-el↗

arXiv2025

The Abundance and Properties of Barred Galaxies out to $z \sim$ 4 Using $\textit{JWST}$ CEERS Data

We present the first estimate of the observed fraction and properties of bars out to $ z\sim4$ using $\textit{JWST}$ CEERS NIRCam images. We analyze 1770 galaxies with $M_\star > 10^{10} M_\odot$ at $0.5 \leq z \leq 4$ and identify barred galaxies from 839 moderately inclined disk galaxies via ellipse fits and visual classification of both F200W and F444W images. Our results apply mainly to bars with projected semi-major axis $a_{\rm bar}$ $> 1.5 $ kpc ($\sim$ 2 $\times$ PSF in F200W images) that can be robustly traced by ellipse fits. For such bars, the observed bar fraction at $z\sim$ 2-4 is low ($\lesssim 10\%$), and they appear to be emerging at least as early as $z\sim 4$. Our observed bar fraction is consistent with the bar fraction predicted by TNG50 simulations for large bars with $a_{\rm bar}$ $> 1.5$ kpc at $z \sim$ 0.5-4 and with the bar fraction from Auriga simulations out to $z \sim$ 1.5. However, TNG50 simulations predict a large population of smaller bars that our data cannot robustly detect. If such bars exist, the true bar fraction at $z \sim$ 2-4 may be significantly higher than our results. At $z \ge 1.5$, many barred galaxies show nearby neighbors, suggesting bars may be tidally triggered. From $z \sim$ 4-0.5, the observed bar fraction, average projected bar length, and projected bar strength rise. Our results highlight the early emergence and evolution of barred galaxies and the rising importance of bar-driven secular evolution from $z \sim$4 to today.

astro-ph.GA↗