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Ying Chen

Publications and source records attributed to Ying Chen.

At least 37 records · Page 2Linked to original sources
arXiv2026

Scalar glueball-$s\bar{s}$ mixing in one flavor lattice QCD

We investigate the mixing between the lowest-lying scalar glueball and the $s\overline{s}$ meson in $N_f=1$ lattice quantum chromodynamics (QCD) utilizing an anisotropic $16^3 \times 128$ lattice ensemble at a lattice spacing $a_s\approx 0.148\,\rm{fm}$. By solving a generalized eigenvalue problem (GEVP) for the optimized glueball and $s\overline{s}$ scalar operators in the $J^\text{PC} = 0^{++}$ channel, the masses of the two lowest-lying eigenstates are determined to be $m_1 = 1.290(20)\,\mathrm{GeV}$ and $m_2 = 1.777(49)\,\mathrm{GeV}$. By extracting the couplings of these mass eigenstates to the glueball and $s\bar{s}$ operators, we determine a substantial mixing angle $|θ| \approx 40.7(2.7)^\circ$ and a large mixing energy $x_s = 239(24)$ MeV. These results indicate a strong glueball-$s\bar{s}$ mixing in the scalar sector, providing important non-perturbative inputs for understanding the nature of the experimental isoscalar scalar mesons. The continuum limit of the mixing energy and its quark mass dependence need to be investigated in the future.

hep-lat↗
arXiv2026

Feedback-Assisted Trust Propagation over Document Relation Graphs for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) systems rely on external corpora that may contain outdated, contradictory, noisy, or unreliable documents, introducing reliability risks. Prior work has leveraged document relations to improve the answer reliability of RAG. To propagate reliability signals beyond directly compared document pairs, we propose TrustPropRAG, which structures document relations as a graph and estimates document reliability through multi-hop propagation across the graph. TrustPropRAG anchors this propagation with a limited set of human feedback on document reliability, extending these costly-to-collect feedback-based reliability signals across the whole corpus. Specifically, based on the constructed document relation graph, TrustPropRAG estimates a trust score for each document by formulating and solving an optimization problem that jointly captures pairwise document relations and user feedback. These scores are then used to improve the selection of reliable documents and support trust-aware answer generation. Evaluation results show that TrustPropRAG improves both retrieval quality and exact match over baselines, and remains robust under sparse and noisy feedback.

cs.AI↗
arXiv2026

Beyond Global Realism: Virtual Try-On Evaluation and Optimization with Dimension-wise Garment Fidelity Assessment

Virtual try-on (VTON) requires not only realistic generation but also faithful preservation of garment characteristics. However, existing evaluation metrics such as PSNR, SSIM, KID and FID struggle to measure the consistency between the generated and reference garments, particularly in capturing the multi-dimensional characteristics of garment fidelity. To address this, we propose DAT: a Dimension-wise Assessment framework for virtual Try-on, which decomposes garment consistency into seven interpretable dimensions: silhouette, color, neckline and sleeve shape, major decoration and structure, material texture, fine-detail fidelity, and logo preservation, each formulated as a discrete attribute-level prediction task. To train this specialized assessment model, we adopt a two-stage learning paradigm comprising large-scale weak supervision on 50K samples, followed by refinement on 10K higher-quality annotations obtained via multi-model voting. Furthermore, we employ weighted cross-entropy loss to mitigate the severe label imbalance inherent across evaluation dimensions. Beyond its role as an evaluation framework, the assessment model can be integrated into reinforcement learning optimization of Qwen-Image-Edit for VTON, where dimension-wise rewards are adaptively aggregated to emphasize under-optimized aspects during training. Experimental results show that our method (8B parameters) achieves state-of-the-art performance in terms of balanced accuracy, SROCC, and PLCC, outperforming strong proprietary models such as Gemini-3.1, Qwen3.7-plus, and GPT-5.5, while also serving as an effective optimization signal for reward-guided VTON generation

cs.CV↗
arXiv2026

ProGVC: Progressive-based Generative Video Compression via Auto-Regressive Context Modeling

Perceptual video compression leverages generative priors to reconstruct realistic textures and motions at low bitrates. However, existing perceptual codecs often lack native support for variable bitrate and progressive delivery, and their generative modules are weakly coupled with entropy coding, limiting bitrate reduction. Inspired by the next-scale prediction in the Visual Auto-Regressive (VAR) models, we propose ProGVC, a Progressive-based Generative Video Compression framework that unifies progressive transmission, efficient entropy coding, and detail synthesis within a single codec. ProGVC encodes videos into hierarchical multi-scale residual token maps, enabling flexible rate adaptation by transmitting a coarse-to-fine subset of scales in a progressive manner. A Transformer-based multi-scale autoregressive context model estimates token probabilities, utilized both for efficient entropy coding of the transmitted tokens and for predicting truncated fine-scale tokens at the decoder to restore perceptual details. Extensive experiments demonstrate that as a new coding paradigm, ProGVC delivers promising perceptual compression performance at low bitrates while offering practical scalability at the same time.

cs.CV↗
arXiv2026

OceanGym: A Benchmark Environment for Underwater Embodied Agents

We introduce OceanGym, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments. Unlike terrestrial or aerial domains, underwater settings present extreme perceptual and decision-making challenges, including low visibility, dynamic ocean currents, making effective agent deployment exceptionally difficult. OceanGym encompasses eight realistic task domains and a unified agent framework driven by Multi-modal Large Language Models (MLLMs), which integrates perception, memory, and sequential decision-making. Agents are required to comprehend optical and sonar data, autonomously explore complex environments, and accomplish long-horizon objectives under these harsh conditions. Extensive experiments reveal substantial gaps between state-of-the-art MLLM-driven agents and human experts, highlighting the persistent difficulty of perception, planning, and adaptability in ocean underwater environments. By providing a high-fidelity, rigorously designed platform, OceanGym establishes a testbed for developing robust embodied AI and transferring these capabilities to real-world autonomous ocean underwater vehicles, marking a decisive step toward intelligent agents capable of operating in one of Earth's last unexplored frontiers. The code and data are available at https://github.com/OceanGPT/OceanGym.

cs.CL↗
arXiv2026

Simplified criteria for identifying and designing altermagnetic crystals in real space

Altermagnetism is a compensated magnetic phase characterized by zero net magnetization and exchange-driven spin splitting. However, identifying altermagnets among collinear antiferromagnets usually requires full magnetic-space-group or spin-group analysis, which is not always intuitive. Here we formulate a simple real-space criterion based on how the crystallographic operations of the host nonmagnetic structure permute the two opposite-spin sublattices. We show that altermagnets usually exist on collinear compensated antiferromagnets whose magnetic primitive cell coincides with the host nonmagnetic crystallographic primitive cell. In this case, altermagnetic spin splitting is generally allowed unless an inversion-type operation exists that exchanges the two opposite-spin sublattices. Using chemically ordered Mn2SSe prototypes derived from zinc-blende or rocksalt parent structures, we demonstrate that these criteria can be easily used to construct the three symmetry classes by controlling chemical ordering and magnetic-sublattice permutation. Similar rules can also be applied to low-dimensional crystals or quasicrystals. Our work reduces the identification of altermagnets to a transparent real-space symmetry test and provides a practical route for designing altermagnetic crystals.

cond-mat.mtrl-sci↗
arXiv2026

One Model, Many Minds: Unlocking Multi-Agent Synergy in a Single Agent via Mixture of Roles

Specializing Large Language Models (LLMs) toward distinct abilities underpins successes ranging from personalized assistants to multi-agent systems (MAS). Single-agent paradigms rely on pre-defined personas or steering vectors to induce specialization, yet they impose a single fixed specialization that fails to adapt to diverse queries. Conversely, MAS achieves dynamic multi-perspective problem solving by orchestrating agents with distinct text-based roles, but fusing these specializations requires multi-turn interactions that inflate context length and inference cost. To address these limitations, we propose Mixture of Roles (MoRe), which adaptively composes multiple specializations into a single steering vector for single-turn inference. Specifically, MoRe learns a diversified codeboox of steering vectors, each of which encodes a latent role. A query-aware router dynamically fuses the codebook into a steering vector that encompasses multiple roles. By steering the backbone LLM with the composed vector, MoRe enables multi-perspective specialization in a single-agent, single-turn inference process. The proposed MoRe can be efficiently trained via a three-stage SFT curriculum and GRPO post-training, while the backbone LLM remains frozen. Experiments across reasoning and personality benchmarks show that MoRe outperforms single-agent baselines by 2.2% on average, and achieves performance on par with MAS while reducing token cost by 20x.

cs.MA↗
arXiv2026

Scalar and tensor structures in $J/ψJ/ψ$ scattering from lattice QCD

The $^1S_0$ and $^5S_2$ amplitudes of $J/ψ~J/ψ$ scattering are determined up to 6.6~GeV from $N_f=2$ lattice QCD simulations at $m_π\approx 420$~MeV and $250$~MeV. We observe an attractive interaction in the ${}^1S_0$ channel, which permits the existence of a near-threshold scalar structure. This structure may correspond to $X(6200)$, but the possible left-hand-cut effect prevents us from a precise pole determination. In the ${}^5S_2$ channel, while a near-threshold repulsive interaction is observed, a resonance appears at higher energies associated with a Castillejo-Dalitz-Dyson zero $\sqrt{s}_{\rm CDD}\approx 6.45~\rm{GeV}$ of the scattering amplitude. The tensor resonance has the parameters $(m_R,Γ_R)=(6.543(10), 0.548(34))~\rm{GeV}$ for $m_π\approx 420~\rm{MeV}$ and $(6.538(13),0.537(56))~\rm{GeV}$ for $m_π\approx 250~\rm{MeV}$, which are compatible with those of $X(6600)$ (or $X(6400)$) reported by ATLAS and CMS, and also support the $2^{++}$ assignment of the recent CMS study. The difference of the $^1S_0$ and $^5S_2$ $J/ψJ/ψ$ interaction is attributed to the dominance of the quark rearrangement effects. The systematic uncertainties owing to the unphysical pion mass, the finite lattice spacing, and the finite volume should be investigated by future lattice QCD studies with more sophisticated lattice setups.

hep-lat↗
arXiv2026

GraftSR: Grafting Authentic Textures for Real-World Image Super-Resolution via Identical-Instance Guidance

Diffusion-based real-world image super-resolution (SR) achieves impressive perceptual quality but inherently suffers from severe texture hallucination. To overcome this limitation, we propose GraftSR, a texture-reference-guided generative SR framework that leverages reference images of the identical instance to anchor the restoration of authentic textures. However, severe spatial misalignment between low-quality inputs and their references poses significant challenges, often leading to ambiguous transfer targets and background feature leakage. To address these issues, GraftSR employs a novel dual-mask reference guidance mechanism that systematically decouples the cross-view texture injection process. By explicitly isolating what authentic textures to extract from the reference and precisely localizing where to apply them within the target, GraftSR achieves robust texture transfer without relying on brittle spatial alignment. Furthermore, to bridge the critical gap in appropriate training data, we construct TexRefSR-141K, the first large-scale dataset providing high-quality reference tuples equipped with complementary spatial masks. Extensive experiments on our newly established benchmark, TexRefSR-Eval, demonstrate that GraftSR sets a new state-of-the-art. Notably, it reduces LPIPS by 20.2\% over top-performing baselines, achieving superior reference-faithful restoration.

cs.CV↗
arXiv2026

Analytical Covariance Propagation for DVL-Aided Loosely Coupled SINS Under Attitude Uncertainty

In loosely coupled strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integration, the body-frame DVL velocity is projected into the navigation frame using the SINS-computed attitude. Attitude uncertainty consequently affects both the projected velocity observation and its associated measurement covariance. This paper develops an analytical covariance propagation (ACP) method that treats these two effects consistently. The attitude-error-aware observation model represents perturbations in the projected DVL velocity, whereas the covariance construction propagates the body-frame DVL covariance at the matrix level and adds a closed-form term derived from the predicted attitude-error covariance, rather than directly rotating component-wise standard deviations. Simulation and a controlled surface-water field experiment compare ACP with conventional SINS/DVL and adaptive-covariance benchmarks. For the evaluated data sets and parameterizations, ACP yields the lowest position-error metrics among the compared methods. Relative to the variational Bayesian adaptive Kalman filter (VBAKF) in the field experiment, ACP reduces the northward, eastward, and downward position root-mean-square errors (RMSEs) by 48.5%, 55.0%, and 74.8%, respectively. Beyond DVL aiding, ACP provides a transferable covariance-construction principle for attitude-dependent vector-measurement fusion and a physically interpretable alternative to heuristic frame-dependent covariance assignment.

cs.RO↗
arXiv2026

InfinityEdit: Infinite Video Editing with a Lightweight Edit-Ignition Adapter

With large pretrained models, existing methods have effectively improved instruction-based video editing. However, most of them rely on an in-place editing assumption. They align the edited video with the given source clip frame by frame over a fixed time span. This pattern fails for open-ended streams, e.g., restyling a live game or applying a camera move to an ongoing shot. In such cases, edits must extend to future frames as they arrive, rather than be applied to a static input clip. In this paper, we study this setting and name it infinite video editing: given a preceding segment and an edit request, a model must generate the next segment that continues the stream while applying the requested edit. This process repeats as an unbounded sequence of edit instructions arrives. This task brings two challenges: the edit must be a faithful continuation rather than a frame-wise rewrite, and generation quality must remain stable as edits accumulate. To address them, we first design a data-collection pipeline for infinite video editing. Based on the collected data, we propose InfinityEdit, a lightweight edit adapter that equips a streaming video generator with unbounded editing ability. The adapter contains three attention modules. History cross-attention guides the denoising frames using the input frames. Temporal causal self-attention keeps temporal cues flowing only from earlier frames to later ones. Edit cross-attention injects the edit request into generation. During inference, the adapter is activated only in the chunk where an edit request arrives. Subsequent chunks are generated by the original model with a reset anchor frame. This scheme applies the edit while preserving the original model's infinite generation ability. Extensive experiments show that InfinityEdit faithfully continues the stream under each edit, and stays stable over unbounded edit sequences.

cs.CV↗
arXiv2026

$η_cη_c$ and $J/ψJ/ψ$ scatterings from lattice QCD

We investigate the $S$-wave $η_cη_c$ and $J/ψJ/ψ$ scattering in the $J^{PC}=(0,2)^{++}$ channels up to a center-of-mass energy of 6.6~GeV. The calculations are carried out at two unphysical pion masses, $m_π\approx 420$~MeV and 250~MeV, in $N_f=2$ lattice QCD. For each $m_π$, we extract the finite-volume energy levels on two lattices with an identical lattice spacing ($a\simeq 0.136~\mathrm{fm}$) but different spatial volumes. Since the coupled-channel effects between the $η_cη_c$ and $J/ψJ/ψ$ channels are found to be negligible, we analyze the corresponding scattering properties using the single-channel L"{u}scher method. We find that the interactions in these dicharmonium systems are dominated by the quark rearrangement effect. In the $0^{++}$ channel, the near-threshold attraction in $J/ψJ/ψ$ and repulsion in $η_cη_c$ can be explained through the Fierz rearrangement. The attractive interaction in the ${}^1S_0$ $J/ψJ/ψ$ channel allows for the existence of a near-threshold scalar structure, which may correspond to the $X(6200)$. However, possible left-hand-cut effects due to light-hadron exchange prevent us from precisely determining the pole positions. In the $2^{++}$ channel, while the ${}^5S_2$ $J/ψJ/ψ$ system exhibits a repulsive interaction near threshold, the scattering amplitude has a Castillejo-Dalitz-Dyson zero at $\sqrt{s}=6.45~\mathrm{GeV}$ and a resonance pole at $\sqrt{s}=\big[6.543(10)-i,0.548(34)/2\big]~\mathrm{GeV}$ for $m_π\approx 420~\mathrm{MeV}$ and $\big[6.538(13)-i,0.537(56)/2\big]~\mathrm{GeV}$ for $m_π\approx 250~\mathrm{MeV}$, where the uncertainties are statistical. This resonance may correspond to the $X(6600)$ (or $X(6400)$) reported by the ATLAS and CMS Collaborations. Our result supports its $2^{++}$ assignment, in agreement with the latest spin-parity determination by CMS.

hep-lat↗
arXiv2026

Generative Video Compression Based on Hierarchical Referencing

Diffusion-based generative video compression has emerged as a promising paradigm to improve perceptual quality, where latent frames are required to be encoded efficiently while serving as denoising conditions. However, existing methods neither carefully design reference and quality structures during latent coding nor account for the impact of frame-level quality variation on denoising procedure, which limits coding efficiency and aggravates artifact propagation during generative reconstruction. In this paper, we propose GVCHR, Generative Video Compression based on Hierarchical Referencing. The key idea is to organize latent frames hierarchically, where the selected high-quality references benefit both latent coding and generative reconstruction. In latent coding, GVCHR couples a hierarchical reference structure with a hierarchical quality structure, assigning more bits to lower-layer frames that are reused more frequently as references. Built on this design, we introduce Hierarchical Temporal Context Mining to exploits complementary short- and long-term temporal context for effective latent coding. In generative reconstruction, the coding-side hierarchy is incorporated into a Hierarchical Attentive Adapter which is attached to a video diffusion transformer. This adapter uses hierarchical attention to restrict each latent frame to attend only to the same- or lower-layer references, thereby reducing artifact propagation during denoising. Experiments validate GVCHR on multiple benchmarks. Compared with the previous state-of-the-art method, GVCHR achieves 50.5% and 54.0% BD-rate gains in terms of LPIPS and DISTS, respectively, while also delivering clearly improved visual quality.

cs.CV↗
arXiv2026

Transformer-Based Neural Quantum Digital Twins for Many-Body Spectral Reconstruction and Adaptive Quantum-Annealing Schedule Design

We introduce Transformer-based Neural Quantum Digital Twins (Tx-NQDTs) to reconstruct the low-energy spectral evolution of many-body quantum systems along quantum-annealing paths, including ground- and first-excited-state energies, spectral gaps, and transition matrix elements, at efficient computational cost. Tx-NQDTs employ a graph-informed Transformer neural network trained to estimate the spectral information needed for annealing-schedule design. We integrate these estimates with an adaptive schedule-construction procedure guided by first-order adiabatic perturbation theory (FOAPT), which is used as a closed-system spectral diagnostic to allocate annealing time near predicted spectral bottlenecks. Experiments on a D-Wave quantum annealer ($N=10,15,20$ logical variables, with schedules represented by up to 12 control points) show that Tx-NQDT-informed schedules can improve empirical ground-state success probabilities relative to the default $20\,μ\mathrm{s}$ linear schedule under the tested hardware conditions. The proposed schedules achieve success probabilities $2.2$--$11.7$ percentage points higher across the reported easy and hard subsets and outperform the default baseline in 44 of 60 cases. The results demonstrate the feasibility of using learned logical spectral information to automatically generate adaptive quantum-annealing schedules for practical hardware experiments.

quant-ph↗
arXiv2026

Capek 0.5: An Execution-Centric Vision-Language Model for Embodied Intelligence

Vision-language models are increasingly serving as the reasoning core of embodied agents. Robot execution is inherently iterative: each action reshapes the scene and physical state, continually renewing what must be perceived, reasoned about, and verified. Meeting these demands requires complementary capabilities that differ in supervision signals, prediction formats, and verification criteria. Existing approaches typically develop these capabilities against isolated, task-specific objectives, leaving open how they should be organized and integrated around execution as a whole. We present Capek 0.5, an embodied vision-language model built around an execution-centric capability taxonomy. Rather than organizing training by datasets or tasks, the taxonomy groups embodied capabilities according to their functional roles throughout execution and comprises four capability families: Spatial Reasoning, Temporal Understanding, Action Guidance, and State Verification. Each capability is first acquired by a dedicated specialist through reinforcement learning with verifiable rewards from a shared backbone, and the specialists are then consolidated into a single inference-time model through weight-space merging followed by routed policy-space distillation. We instantiate Capek 0.5 at the 2B and 35B-A3B scales and evaluate it from three complementary perspectives: comprehensive benchmark suites including Capek-StateBench, a new benchmark for state verification; a controlled study of capability retention from specialists to the unified model; and closed-loop evaluation in simulated embodied environments. Capek 0.5 improves the large majority of matched benchmark rows over its initialization, retains all four specialized capabilities in one checkpoint with quantified losses, and transfers to closed-loop embodied task execution.

cs.AI↗
arXiv2026

HIERA: Hierarchical Multi-Agent Relevance Assessment for Content Discovery Systems

Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs. While Large Language Models show promise as automated assessors, current approaches rely on flat aggregation strategies: single-step prompting, voting ensembles, or uncoordinated multi-agent pipelines that aggregate independent outputs without integration. We propose HIERA, a hierarchical multi-agent relevance assessment framework with four specialized agents: a Relevance Judge, Query Analyzer, Item Analyzer, and Relation Analyzer. The Judge determines when specialist analysis is needed; the Relation Analyzer then coordinates query and item analyses with external knowledge to establish relevance relationships for final judgment. Ablation studies show that the same agents and external knowledge without hierarchical coordination degrade performance, confirming that the coordination structure itself accounts for the improvement. Evaluation across five datasets (EVS, MSRD, ESCI, WANDS, Home Depot) shows improvements over 11 baselines: 10.2\% on Home Depot, 4.8\% on ESCI, and up to 38\% on EVS ($p < 0.05$). Hierarchical coordination yields 12.7\% improvement over uncoordinated collaboration using identical agents.

cs.MA↗
arXiv2026

DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

Most benchmarks for causal inference over time series are observational, small, or domain-specific, leaving interventional and counterfactual estimation under-served exactly where it matters most, such as in healthcare, policy evaluation, and climate science. We introduce \textbf{DoTime}, an open, scalable, and theoretically grounded generator of multivariate temporal structural causal models (TSCMs) with interventions, released as the \code{dotime} PyPI package together with four frozen evaluation suites. Beyond existing work, it adds capabilities absent from prior generators: continuous-time intervention \emph{windows}, counterfactual sampling modes with a positivity guard, regime-switching SCMs as a strict generalization of interrupted time series, non-stationary dynamics by construction with switching SCM parameters, and deterministic ramp and sinusoidal intervention profiles that place trends and structural breaks \emph{inside} the evaluation window. Moreover, it demonstrates the suitability of the generator as a prior for a causal foundation model reference implementation. The released suites span a training-scale snapshot of $100{,}000$ trajectories and eight named identification structures, each with exact ground truth: paired interventional trajectories from the same SCM throughout, and shared-noise counterfactuals in the continuous-time suite. We ship reference baseline implementations with an evaluation harness, and pose a falsifiable claim: interventional training buys a measurable direction-accuracy advantage over an observational model of identical capacity. It is tested across three training seeds per arm. Under structure-matched evaluation on held-out episodes, the interventional prior-fitted network's (PFN) gap is positive in every structure, trajectory length, and seed tested.

cs.LG↗
arXiv2026

Joint Group-Based Trajectory Modeling for Paired Repeated Measures: An Application to Audiometric Phenotypes and Dietary Associations

The assumption of conditional independence in conventional group-based trajectory modeling (GBTM) is often violated by paired repeated-measures data with heterogeneous trajectory patterns. While random-effects models can accommodate this dependence, they inflate within-group variability and blur distinct phenotypic shapes. We propose a joint GBTM framework that explicitly models hierarchical dependence in paired trajectories while allowing them to follow different latent patterns. We develop a robust two-stage approach to address estimation challenges caused by rare latent groups, and a one-stage EM algorithm that serves as a theoretical baseline under balanced group sizes. Simulations demonstrate that our methods correct the biases caused by ignoring hierarchical dependence. The proposed model was applied to real-world data from the Conservation of Hearing Study (CHEARS) Audiology Assessment Arm (AAA), a subcohort of the Nurses' Health Study II (NHS II), to identify distinct audiometric phenotypes and to investigate the association between the Dietary Approaches to Stop Hypertension (DASH) dietary adherence score and the latent audiometric patterns.

stat.ME↗