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Yao Wang

Publications and source records attributed to Yao Wang.

At least 19 recordsLinked to original sources
arXiv2026

Phonon Fluctuations Enhanced Superconductivity in a Three-Band Cuprate Model

The microscopic origin of high-$T_c$ superconductivity in cuprates remains an outstanding puzzle due to the interplay of intertwined orders. Experimental observations and theoretical studies have suggested crucial roles of phonons, with the $B_{1g}$ oxygen buckling mode emerging as a promising candidate. Here, we investigate the microscopic influence of this phonon in a three-band cuprate model. We find that the $B_{1g}$ phonon plays opposite roles in superconductivity depending on whether it lies in the adiabatic or antiadiabatic regime at a given temperature. Thermally populated adiabatic phonons suppress $d$-wave pairing through the loss of quasiparticle coherence, whereas antiadiabatic phonons simultaneously enhance coherence and the attractive $d$-wave pairing vertex, strengthening superconductivity across multiple diagnostics. This enhancement also exhibits pronounced electron-hole asymmetry arising from the different orbital characters of doped carriers. Our results therefore establish a microscopic mechanism through which $B_{1g}$ phonons can cooperate with electronic correlations to substantially enhance $d$-wave superconducting pairing in cuprates.

cond-mat.str-el↗
arXiv2026

Robust, Estimator-Agnostic Dynamic 3DGS Compression

Dynamic 3D Gaussian splats (3DGS) model time-varying scenes using a separate Gaussian set per frame. While neighboring video frames are highly correlated due to smooth motion, Gaussian representations retain this correlation to varying degrees, depending on whether the estimator tracks them across time. Some 3DGS compression methods integrate the estimation to exploit temporal redundancy; here, we focus on robust compression regardless of the estimator. We concatenate groups of frames into one Gaussian set, augment each Gaussian with a frame index, and pass it to a static (i.e., non-temporal) 3DGS codec, converting temporal redundancy into spatial redundancy. Concatenated sets are spatially partitioned to limit memory. Our technique requires neither a motion model nor knowledge of the training method. Averaged over six N3DV sequences, all six static codecs achieve gains on tracked sets (-42.0% to -71.8% BD-rate) over per-frame coding. On untracked sets, all codecs except HGSC, which appears incompatible with our technique, remain competitive with per-frame coding (-3.5% to +5.0%). We further replace D-FCGS's I-frame coding with our technique while retaining its P-frame coding, yielding an overall BD-rate of -46.2%. We propose to visualize "trackedness" using an inter-frame similarity metric. The project is available at https://wcjj1236.github.io/d3dgs-benchmark.

eess.IV↗
arXiv2026

ZnO/Ni interface-activated enhancement of spintronic terahertz emission from Ni/Pt bilayers

Spintronic terahertz (THz) emitters based on ferromagnet/heavy-metal bilayers provide an efficient route for generating broadband THz radiation through ultrafast spin-to-charge conversion. However, Ni/Pt bilayers usually exhibit relatively weak THz emission, limiting their use as efficient spintronic emitters. Here, we report an interface-assisted enhancement of THz emission in ZnO/Ni/Pt heterostructures. By placing ZnO underneath Ni while preserving the direct Ni/Pt contact, the THz amplitude is increased by up to approximately 2.8 times compared with the Ni/Pt reference. The enhanced emission reverses polarity with the magnetic field and follows the expected angular dependence of ultrafast spin-to-charge conversion. ZnO-, Ni-, and Pt-thickness-dependent measurements show that the emission remains governed by the Ni/Pt conversion process but is strongly affected by the ZnO/Ni bottom interface. Control samples with Al2O3, Cu, and Fe further exclude a simple underlayer, optical, or growth-quality origin. The enhancement is attributed to a short-range ZnO/Ni interface effect, possibly involving oxygen-related defects, interfacial electric fields, and modification of the Ni interfacial state. This work provides a semiconductor/ferromagnet interface-engineering strategy for activating weak Ni/Pt spintronic THz emitters.

cond-mat.mtrl-sci↗
arXiv2026

Poisoning the Neighborhood: A Topology-Aware Topic-Level Attack on Large-Scale HNSW-based RAG Systems

Retrieval-augmented generation (RAG) mitigates hallucinations and knowledge staleness in large language models using external knowledge. However, ingestion from public and third-party sources exposes RAG systems to poisoning attacks. Existing attacks are designed and evaluated under exact retrieval, where poisoned documents participate in global ranking. Deployment-scale systems instead rely on approximate nearest-neighbor (ANN) indexes such as hierarchical navigable small world (HNSW), where limited search budgets, graph-search reachability, and local benign-document competition invalidate this assumption and degrade existing attacks. We present a topology-aware topic-level poisoning attack against large-scale HNSW-RAG that generalizes to unseen topic queries. We organize poisoned documents into a Hub-Satellite structure: Satellites provide competitive entry points across semantic regions, while Hubs strengthen local cohesion and facilitate cascading retrieval. For generation, we propose an entity-claim promotion attack combining evidence anchoring and entity-claim coupling to promote the target entity via a target claim across different query regions. On 2M-document Wikipedia, our attack maintains over 72% retrieval success and reaches 31.29% attack success, substantially outperforming baselines; domain-specific experiments further support its effectiveness. Structural analyses show that the Hub-Satellite topology forms in HNSW, indicating that graph structure contributes to improved poisoned-document retrieval. Existing poisoning defenses cannot mitigate the attack, exposing graph-structural risks in HNSW-RAG systems.

cs.CR↗
arXiv2026

LGGS: Longitudinal MRI Reconstruction via Guided Group Sparsity

Serial MRI is routinely performed in current clinical practice for disease surveillance and applications such as MR-guided radiotherapy. However, long MRI scan times increase costs and limit patient access, making accelerated image reconstruction of broad clinical importance. Previous scans of the same patient contain image information highly correlated with the current exam, yet existing reconstruction methods rarely exploit this information. Major challenges in the effective use of prior scans include inter-visit misregistration, pathological changes, and image phase variation. In this work, we propose Longitudinal Guided Group Sparsity (LGGS), an interpretable deep-learning framework for longitudinally informed MRI reconstruction derived from classical group sparsity. The prior scan is incorporated into the reconstruction regularizer through learned phase-invariant circulant attention that identifies corresponding features across exams. This formulation provides robustness to local in-plane and through-plane misregistration as well as inter-visit phase variation. Across acceleration rates, LGGS achieves state-of-the-art performance on longitudinal T1-weighted multi-coil brain MRI data, outperforming the evaluated unguided and longitudinally informed reconstruction methods, with substantially fewer learned parameters. We further evaluated the contributions of multiple guide slices and phase-invariant similarity and demonstrated that accelerated prior images can also serve as effective guides with minimal degradation in reconstruction quality.

eess.IV↗
arXiv2026

Identifying the principal mechanism of star formation feedback

Feedback from massive stars regulates the formation of next-generation stars and planets and thus shapes the evolution of galaxies, yet observational evidence capable of determining which mechanism dominates the feedback process has long been lacking. Here,we present a large-scale HI survey toward Orion A$.$ With an unprecedented sensitivity and superior resolution, FAST (Five-hundred-meter Aperture Spherical radio Telescope) reveals a well-defined heart-shaped expanding HI bubble, enshrouding the well-known "Veil" bubble of ionized gas. The photoionization-driven and wind-driven gas expands at 10.9 and 13.8 kilometers per second, respectively, with a kinetic energy ratio of 1:2 assuming a spherical symmetry and a uniform HI-to-H2 ratio. This first direct comparison within a single bubble shows that radiation does not dominate the feedback process as predicted by classical theories; instead, stellar winds contribute a comparable share of the injected kinetic energy.

astro-ph.GA↗
arXiv2026

Dynamical many-body control of high harmonic generation in a Mott insulator

High harmonic generation (HHG) has emerged as a powerful probe of ultrafast electron dynamics in solids. However, in strongly correlated materials, where electronic excitations arise from many-body interactions rather than single-particle band dispersion, the field remains in its infancy. Here, we report HHG up to the 23$^{rd}$ order in the prototypical Mott insulator La$_2$CuO$_4$. We observe a robust 4 eV cutoff that is independent of driving field strength, revealing an intrinsic energy scale defined by the Hubbard bands. Using time-resolved high harmonic spectroscopy, we disentangle the distinct contributions of carrier creation, intraband acceleration, and interband recombination to the nonequilibrium radiation spectrum. We demonstrate that photo-doping triggers a fundamental reorganization of the many-body manifolds above a critical excitation density, a process that renormalizes the band curvature and suppresses nonlinear intraband currents, thereby directly imprinting the signature of many-body band reconstruction onto the harmonic emission. These results establish HHG as a sensitive probe of subtle correlation-driven electronic reconstructions and highlight that many-body interactions enable extreme nonlinear optical responses that are highly tunable in nonequilibrium states.

cond-mat.str-el↗
arXiv2026

Correlated memory effect of environment in radical-pair magnetoreception

In the photoreceptor compass model, a light-generated, spin-correlated radical pair undergoes singlet-triplet interconversion driven by Zeeman and anisotropic hyperfine interactions, leading to orientation-dependent reaction yields. In this study, we consider simultaneously the spin environment and the boson environment surrounding the radical-pair system and concentrate on the correlated memory effect of the spin bath. In the linear-response limit, the spin bath is mapped to an effective Gaussian environment. The system dynamics and yields under its influence together with the bosonic bath are propagated uniformly using the hierarchical equations of motion (HEOM) or equivalently the dissipaton equations of motion (DEOM) method. The HEOM/DEOM is non-Markovian and non-perturbative, exact for Gaussian environments. Corresponding results of the Markovian Lindblad master equation are also shown for comparison. Numerical demonstrations highlight the non-Markovian memory effect as a crucial ingredient for radical-pair magnetoreception.

quant-ph↗
arXiv2026

Coupled-cluster molecular properties across the main group that extrapolate beyond training size

Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine application, whereas density-functional theory is affordable yet systematically biased. We resolve this trade-off with a single equivariant network, HARP (Hamiltonian Read-out for Properties), that predicts an effective one-electron Hamiltonian from one inexpensive B3LYP/def2-SVP calculation and derives a broad suite of properties from it (energy, optical gap, dipole, quadrupole, polarizability, Mulliken atomic charges, and Mayer bond orders) at coupled-cluster accuracy across nine main-group elements, including the under-served phosphorus, sulfur, and chlorine chemistries. The model is trained on a new in-house dataset of multi-property labels computed at the CCSD(T) level for all nine elements. On a held-out test set, it reduces the error of every property by a factor of 3.8 to 270 relative to semi-local, hybrid, and double-hybrid DFT (referenced to composite CCSD(T)/cc-pVTZ), while adding only ~0.1 s wall time per molecule, delivering coupled-cluster-quality predictions at the cost of a single DFT calculation. Critically, deriving every property from a predicted Hamiltonian rather than pooling per-atom features builds the correct size-scaling into the model architecture: on pi-conjugated oligothiophenes it matches finite-field CCSD polarizability to ~1% and the EOM-CCSD optical gap to ~3% at the largest sizes where those references remain affordable (44 and 37 atoms, where a single CCSD field point already costs ~500x the model's entire inference) and extrapolates the corrected trends to 58-atom chains, a regime where pooling-based architectures fail by construction. Accurate extrapolation is therefore set by the model's inductive bias rather than by the training data.

physics.chem-ph↗
arXiv2026

LoRo-Mark:Provably Lossless and Robust Agent Watermarking

As LLM agents are increasingly deployed as commercial services, protecting proprietary orchestration logic and tool-use policies is important. We consider agent repackaging: an adversary integrates a protected agent into its own application via API and presents it under its own identity. It may modify parts of execution to obscure the source. The owner typically has only black-box access to the repackaged service, so black-box ownership verification is essential. Agent watermarking embeds ownership evidence into agent behavior for later verification. An effective watermark should satisfy two requirements: losslessness, preserving original functionality, and robustness, keeping ownership evidence recoverable after partial modification of execution. Existing methods often embed signals into behavior selection or execution trajectories, intervene in normal decisions, and offer limited robustness to behavior modification. We propose LoRo-Mark, a provably lossless and robust agent watermarking mechanism. For losslessness, it isolates watermarking into a cryptographically authenticated forensic branch that remains inactive during normal execution and is activated only by owner-authorized requests. By reducing unauthorized branch activation to standard MAC security, LoRo-Mark formally guarantees performance preservation. For robustness, it redundantly distributes ownership information across forensic behavior sequences, enabling reliable recovery under partial behavior substitution and sequence truncation. Experiments across multiple LLM agents show zero degradation on normal tasks and reliable ownership verification under sequence modifications.

cs.CR↗
arXiv2026

HiFloat4 Format for Language Model Pre-training on Ascend NPUs

Training large foundation models at low numerical precision is one of the most promising directions for reducing the compute and memory cost of modern AI. Recent 4-bit floating-point formats such as MXFP4 and NVFP4 can be applied to linear GEMM operations in LLMs, but their limited dynamic range introduces numerical instability that prior work addresses by stacking stabilization mechanisms, typically executed at higher precision and partially eroding the efficiency gains that motivate FP4. In this work, we argue that numerical format design is itself a first-class lever for stable FP4 training, and present the first systematic study of FP4 LLM pretraining on energy-efficient Huawei Ascend NPUs. We compare the recently proposed HiFloat4 (HiF4) format against both MXFP4 and NVFP4, holding one recipe fixed across all three formats across dense (OpenPangu-1B, Llama3-8B) and Mixture-of-Experts (Qwen3-MoE-30B) architectures and executing all linear and expert GEMMs in FP4. At matched storage --- NVFP4 and HiF4 both spend 4.5 bits per value --- the three formats differ far more in what they require before they will train at all than in final accuracy. HiF4 reaches a relative loss of 1.55\% with no stabilization, below fully stabilized MXFP4 (1.79\%) and below NVFP4 carrying the per-tensor scaling it cannot train without (2.00\%); NVFP4 diverges under every combination of stochastic rounding and Hadamard transform we tried. Our results suggest that stable, accurate FP4 training does not require an ever-growing stack of stabilization techniques; it requires the right numerical format.

cs.LG↗
arXiv2026

An Exact Operator Formulation of Statistical Quasiparticle Theory for Nonlinear System-Bath Coupling

We develop an operator formulation of statistical quasiparticle theory for open quantum systems with nonlinear system-bath coupling, extending beyond the linear coupling setting of the standard Gaussian influence functional formulation. For Gaussian baths, the statistical quasiparticles, termed dissipatons, are explicitly constructed as collective operators in the thermofield bath space. Our construction provides a microscopic operator basis for the dissipaton algebra and generates hierarchical equations of motion directly from the Liouville equation. Nonlinear interactions are incorporated through a recursive generalized Wick's theorem, enabling automatic generation of hierarchical equations for polynomial couplings. The resulting framework connects explicit bath operator representations to nonperturbative, non-Markovian dynamics and unifies the treatment of linear and nonlinear system-bath interactions.

quant-ph↗
arXiv2026

Multi-Subject Pretraining Enables Short-Calibration Personalization for Closed-Corpus Surface EMG Speech Decoding

Surface electromyography (sEMG)-based silent speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data setting in which each of 27 speech-typical participants contributed less than 0.5 h of data (21.3 min on average) across Aloud and Mimed speech. Within a closed 50-sentence corpus, we used leave-one-subject-out evaluation, initializing from a released single-subject checkpoint, pretraining on non-held-out participants, and fine-tuning on the target participant. This pipeline achieved 21.7% character error rate (CER) and 31.9% word error rate (WER), compared with 49.3% CER without target-subject calibration and 68.0% CER for direct checkpoint fine-tuning. Multi-subject pretraining from random initialization followed by fine-tuning reached 44.9% CER and did not converge under the fixed schedule in 5 of 27 folds, indicating substantial optimization and accuracy benefits from checkpoint initialization. Macro-averaged CER declined from 74.4% with one pretraining participant to 21.7% with 26. Three minutes of target-subject calibration achieved 20.5% CER and 31.7% WER, with no statistically significant difference from the full approximately 13-min pool (21.7% CER and 31.9% WER). A subject-specific adapter provided no detectable benefit. Excluding the five evaluation sentences from all sEMG model-training data increased CER and WER to 78.6% and 99.9%. These results support short-calibration personalization in a standardized-montage, closed-corpus setting.

cs.LG↗
arXiv2026

FlashVector: Agent for Hierarchical Model Serving Stack Optimization

Model serving is one of the largest cost drivers in production recommender systems. Maximizing its throughput requires navigating a deeply layered hierarchy: GPU kernels, the ML framework computation graph, the model server, and on-demand feature processing -- each demanding specialized domain expertise. Such cross-layer expertise is inherently difficult to acquire, and does not scale with a workload that continuously grows and evolves, leaving significant cost efficiency gains unrealized. While recent AI agents have demonstrated human expert level efficiency in standalone GPU kernel optimization, automated tuning and optimization for the rest of the serving stack remain largely unexplored. We present FlashVector, an agentic system that optimizes performance across all layers of the model serving stack. The key contribution is an extensible framework to generalize the single kernel optimization agent paradigm to heterogeneous technical stacks, and to deliver performance improvements holistically. After deployment in Unity's Vector advertising platform, FlashVector achieved up to 2x throughput increase and up to 1.98x latency speedup on model server, and up to 1.6x throughput increase on feature store. These optimizations were discovered not only at the GPU kernel and computation graph levels, but also across the other components of the model serving stack, such as the model server (NVIDIA Triton's C++ codebase) and the on-demand feature transformation service (Python codebase), demonstrating the extensibility of the framework to more complex system architectures.

cs.AI↗
arXiv2026

An Eccentric Massive Protobinary Assembled via a Core-merger Parabolic Encounter

Most massive stars form in binary systems, which profoundly influence their subsequent evolution. However, how such systems form remains poorly understood, with several competing scenarios proposed, including disk fragmentation, core fragmentation and capture. Determining the orbital architectures of massive binaries, particularly during their earliest embedded phases, is therefore crucial for distinguishing among these formation pathways, but direct measurements of their three-dimensional motions have remained exceptionally challenging. Here we present high-resolution, multi-epoch sub-millimeter-to-centimeter ALMA and JVLA observations of the massive protobinary IRAS 07299$-$1651, complemented by JWST and VLT infrared imaging. We detect orbital proper motion of the binary components, enabling a full three-dimensional orbital reconstruction. Combining orbital fitting, multi-wavelength continuum modelling, hydrogen recombination line kinematics and jet observations, we find that the preferred orbital solutions are highly eccentric and close to parabolic, while both circumstellar disks are strongly misaligned with the orbital plane. These properties are naturally explained by a ``core-merger'' scenario in which the two protostars originated independently from initially unbound cores that recently underwent a near-parabolic encounter, producing an eccentric binary with a current separation of about 200 au. These findings suggest that the core-merger process may represent an important pathway for forming eccentric massive binaries.

astro-ph.SR↗
arXiv2026

FoRIS: Progressive Foreground Refinement for Training-Free In-Context Segmentation

In-Context Segmentation (ICS) aims to precisely segment arbitrary semantic concepts, such as objects or parts, given one or a few annotated visual exemplars. In this paper, we revisit ICS from a more classical segmentation perspective, viewing it as a coarse-to-fine progressive refinement process. Rather than directly predicting the final mask through reference-query matching, we progressively refine the segmentation from coarse and ambiguous foreground responses to precise and complete foreground structures. Building upon this perspective, we propose a training-free in-context segmentation framework, termed FoRIS. Specifically, FoRIS consists of three key stages: Foreground Purification, Foreground Localization, and Foreground Consolidation, which progressively suppress background distractions, localize discriminative target regions, and recover complete foreground structures through semantic aggregation. Experimental results demonstrate that FoRIS achieves SOTA performance across semantic and part segmentation tasks, with average improvements of 4.5 and 4.8 mIoU points over existing approaches in the 1-shot and 5-shot settings, respectively. Code: https://github.com/Xi-Mu-Yu/FoRIS.

cs.CV↗
arXiv2026

Activation Outliers Matter: Robust Recovery for Quantized Multimodal LLMs

Low-bit quantization offers a promising avenue for reducing the computational and memory demands of Multimodal Large Language Models (MLLMs). Recent hardware support for low-precision formats, ranging from MXFP8 to ultra-low-bit formats such as MXFP4 and HiF4, has accelerated research into efficient MLLM training and deployment. In this work, we present a systematic study of these quantization schemes in representative MLLMs that span both video generation and reasoning tasks. Our analysis shows that MXFP8 achieves near-lossless performance, whereas aggressive 4-bit quantization leads to significant degradation. Through extensive ablations, we identify activation quantization as the primary source of this performance loss, contributing substantially more than weight quantization. Motivated by this observation, we propose Residual Fallback Quantization (RFQ), a lightweight activation reconstruction framework that supplements the primary ulta-low-bit activation representation with an auxiliary quantized residual pathway. By explicitly modeling and compensating for quantization errors, RFQ improves activation fidelity while preserving the efficiency advantages of ultra-low-bit computation. RFQ requires no architectural modifications and incurs negligible computational overhead. Extensive experiments on Wan2.2 and Qwen3-VL demonstrate that RFQ consistently recovers a substantial portion of the performance lost under the quantization of MXFP4 and HiF4, significantly narrowing the gap to BF16 baselines across both generation and 4 reasoning benchmarks. Our findings establish activation quantization as the dominant bottleneck in ultra-low-bit MLLMs and highlight residual-based activation reconstruction as an effective and practical strategy for robust 4-bit deployment.

cs.LG↗
arXiv2026

Error Propagation Theory for Variational Non-Markovian Open Quantum Dynamics

Variational approaches based on neural quantum states and physics-informed neural networks provide powerful paradigms for simulating non-Markovian open quantum dynamics. However, extending these methods into the strongly non-Markovian regime reveals a critical bottleneck: even minute errors in the time evolution can translate into substantial deviations in physical observables. The fundamental origin of this stringent precision requirement, as well as how non-Markovianity governs it, remains an open question. Here, we develop a theoretical framework that systematically characterizes error propagation in variational non-Markovian dynamics. By combining analytical derivations with numerical verification, we present the first quantitative description of variational error evolution over time. Our analysis uncovers an intrinsic error-backflow mechanism driven by long-lived environmental memory. This mechanism establishes a fundamental precision barrier and provides concrete guidance for designing robust variational algorithms for strongly non-Markovian quantum systems.

quant-ph↗