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Zhe Wang

Publications and source records attributed to Zhe Wang.

At least 55 records · Page 3Linked to original sources
arXiv2026

Odd-Parity Chiral Magnons in Collinear Antiferromagnetic Multiferroics

Odd-parity magnetism represents an intriguing frontier in unconventional magnets; however, its realization has traditionally relied on noncollinear magnetic orders accompanied by broken spin conservation, which inevitably causes spin relaxation and dissipative transport of spin-encoded information. Here, we uncover a distinct route toward spin-conserving odd-parity magnon splitting enabled by antichiral Haldane-like flux (AHF) in collinear antiferromagnetic multiferroics. Symmetry analysis reveals that such AHF driven by intra-sublattice Dzyaloshinskii-Moriya interactions (DMI) gives rise to symmetry-dependent odd-parity magnon splittings, ranging from $p$-wave and $f$-wave to nodeless forms. Moreover, the coupling between ferroelectric modes and DMI provides an electric-field knob for manipulating chiral band splitting and magnon transport. Combining density functional theory (DFT) calculations, we identify a series of promising candidate materials in both 2D and bulk antiferromagnetic multiferroics. Our work provides new insights into the realization of odd-parity chiral magnons in collinear antiferromagnets and magnetoelectric coupling mechanisms in multiferroics.

cond-mat.mtrl-sci↗
arXiv2026

Experimental Demonstration of a Measurement-Feedback Quantum Information Engine

Harnessing finite-time nonadiabatic transitions that are conventionally associated with quantum inner friction for useful work extraction remains an open experimental challenge. Here we address this issue by introducing and experimentally realizing an innovative measurement-feedback quantum information engine model in the trapped 40Ca+ ion system, in which the projective measurement replaces the hot reservoir as a nonthermal energy source and the feedback control conditionally steers the system through either unitary compression-expansion strokes or thermalization. We experimentally show that the repeated feedback cycle converges to a stable operating regime with a resolved energetic balance, and, by controlling the measurement angle and stroke duration, measurement-induced coherence and the finite-time nonadiabatic contribution can enhance work extraction and raise the efficiency above the corresponding Otto benchmark. The experimental results further show that, over finite ranges of stroke durations, the efficiency and intrinsic cycle power can increase simultaneously. Our experiment establishes a route toward information-to-work quantum engines that convert finite-time irreversibility into performance-enhancing resources.

quant-ph↗
arXiv2026

FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution

Frontier open-weight models are increasingly available, but serving them still largely assumes datacenter infrastructure. We present FreeToken, an edge-native MoE serving system that treats a personal machine not as a small GPU, but as a unified, elastic inference platform. FreeToken co-designs the full serving stack, including model layout and loading, expert residency, CPU--GPU execution, agentic state reuse, and runtime memory management, around two realities of local AI: agent workloads continuously change their execution pattern, and edge hardware exposes heterogeneous resources whose balance differs from machine to machine. Rather than committing to a fixed offloading strategy, FreeToken continuously maps computation and model state onto the resources actually available. FreeToken supports more than 20 MoE models and real coding and tool-using agents across hardware ranging from an 8GB laptop GPU to a single workstation GPU. More importantly, it changes what these machines can practically serve, from a 35B model on a laptop to a 284B model on a gaming desktop and the 753B GLM-5.2 on a single workstation GPU. FreeToken turns open weights into deployable local software, making the machines users already own a practical platform for frontier-scale intelligence. We release the system at flashml.ai.

cs.DC↗
arXiv2026

Deformation of scalar generalized bi-Hamiltonian structure

We study the classification of scalar generalized bi-Hamiltonian structures with semisimple leading terms. As an application, we prove the existence of integrable viscous conservation laws for any given viscous central invariant.

math.DG↗
arXiv2026

Scaling of the disorder operator at (3+1)D O(3) quantum criticality

The disorder operator, as an easily measured nonlocal observable, displays great potential in detecting intrinsic information of field theories. It has been systematically studied in one- and two-dimensional (1D and 2D) quantum systems, while the knowledge of 3D is still limited. The disorder operator associated with U(1) global symmetry exhibits rich geometric dependence on the shape of the spatial region at a quantum critical point, meanwhile, (3+1)D is the upper critical dimension for O(N) criticality, both of which pose a challenge for exploring the disorder operator in high dimensions. In this Letter, we investigate the scaling behaviors of disorder operators in (3+1)D O(3) models through large-scale quantum Monte Carlo simulation combined with theoretical analysis. Although the upper critical dimension introduces logarithmic corrections in correlations, we analytically prove and numerically demonstrate that the corrections do not modify the universal trihedral-corner contribution of the disorder operator. The universal contributions, such as the current central charge, have been revealed in our calculation, which establishes a concrete link between lattice simulations and continuum field theory. This work opens promising directions for the experimental and numerical exploration of universal properties at quantum critical points in (3+1)D models.

cond-mat.str-el↗
arXiv2026

Giant enhancement of terahertz high-harmonic generation by cavity engineering of a three-dimensional Dirac semimetal

We report on time-resolved ultrafast terahertz high-harmonic generation of strong field driven-dynamics of many-body Dirac fermions. We demonstrate an experimental realization of near saturation regime of the high-order nonlinear responses with a giant enhancement of terahertz third- and fifth-order harmonic yields by cavity-engineering a three-dimensional Dirac semimetal Cd3As2. By fabricating a designed structure of metasurface microcavities on a nanometer Cd3As2 thin film, we significantly enhance the near-field intensity of a picosecond terahertz excitation pulse in resonance with the microcavity eigenmode. The strong terahertz field drives the far-from-equilibrium Dirac fermions deeply into a nonperturbative regime, leading to the observation of near-saturation high-harmonic emission. Our experimental results confirm the predictions by Boltzmann transport theory, and substantiate a field-driven kinetic description of the strong nonthermal nonlinearity at the terahertz frequencies.

cond-mat.mtrl-sci↗
arXiv2026

Potassium-40 geoneutrinos detection and the Earth's large-scale structures imaging by directional geoneutrino detection

Geoneutrinos, the electron (anti)neutrinos generated in decays or decay chains of radioactive elements within the Earth, primarily K-40, U-238, and Th-232, serve as a unique probe of the Earth's inner chemical composition. A directional geoneutrino detection method with a Cherenkov liquid scintillator is investigated in this work. Neutrino-electron elastic scattering in the Cherenkov scintillator is employed to detect geoneutrinos. The direction reconstruction resolution for neutrinos is studied. The radioactive and solar neutrino backgrounds are taken into account. The intrinsic neutrino background from the Sun is suppressed with an optimized solar angle cut. The required exposure to reach a 3 sigma sensitivity to discover geoneutrinos in a potassium-dominated energy region is 6.9 kiloton-years. The potential to image Earth's large-scale structures using directional geoneutrino information is also studied. The required exposure to reject a uniform terrestrial angle distribution of geoneutrinos at the 3 sigma level exceeds 27 kiloton-years.

hep-ex↗
arXiv2026

ProtoAct: Turning Wet-Lab Protocols into Embodied Robotic Actions

Biological wet-lab protocols are written for trained researchers and often leave routine operations, state-dependent conditions, and contextual parameters implicit, making them difficult to translate into robot-executable actions. We present ProtoAct, a structured protocol-grounding framework that converts free-form biological procedures into state-aware, embodiment-ready action sequences. ProtoAct uses ProtoRAG to retrieve manually annotated examples for context-sensitive parsing, employs RefineChecker to detect and revise missing or inconsistent steps, and applies ActSchema to map the refined procedure into constrained JSON function sequences. We further introduce BioP2E, for which we manually annotate 22 cell-culture protocols into 258 monitoring conditions, 910 executable subtasks, and 962 grounded action calls. Evaluation across seven large language models demonstrates that ProtoAct can be effectively instantiated with different backbones. Ablations confirm that retrieval, posterior checking, and schema constraints make complementary contributions. The parsed subtasks further support demonstration collection and VLA model training, enabling successful execution in both simulation and real-robot settings. ProtoAct thus provides a practical interface between biological protocol understanding and embodied robotic execution.

cs.RO↗
arXiv2026

OneShot: Index-in-Ranking with Neural Scoring for Large-Scale Retrieval

In modern recommendation systems, retrieval serves as a primary stage responsible for filtering billions of candidate items down to thousands prior to refined ranking. To make this massive search effective and efficient, the system relies on ranking accuracy and indexing efficiency. However, these two objectives are traditionally misaligned: while the former optimizes for the alignment between ranking predictions and user behavior, the latter optimizes for a structural grouping of item representations which enables fast search among billions of candidates. Thus, despite extensive efforts to scale up interaction modeling for retrieval, they remain fundamentally limited by the structural misalignment between the ranking objectives and the proximity-learned index. In this work, we address this long-standing dichotomy by proposing a new holistic retrieval framework, OneShot. It is an end-to-end, in-model index learning framework that natively aligns index learning with ranking objectives. Using this joint learning as a structural foundation, OneShot pushes the boundaries of retrieval expressiveness by scaling interaction modeling with neural scoring beyond the persistent dot-product bottleneck. OneShot is fully deployed in Instagram's industrial short-video recommendation system, driving significant wins in user daily sessions, engagement, and time-spent. Additionally, OneShot achieves a $20\%$ recall gain at the operational ranking volume and a 10x efficiency improvement at an equivalent recall level.

cs.IR↗
arXiv2026

BioVLN: A Simulation Platform for Visual Language Navigation in Biomedical Laboratories

Biomedical laboratory robots must navigate to instruments before performing experimental procedures. Existing embodied navigation platforms are designed for household environments and treat a target as an object center or an arbitrary nearby position. This representation is inadequate for laboratory instruments, which must be approached from their operating side while maintaining safe clearance from surrounding equipment. We introduce BioVLN, a simulation platform for developing and evaluating visual-language navigation agents in biomedical laboratories. BioVLN represents each instrument with three regions: its physical body, a surrounding clearance region, and an operation area in front of the usable side. This model is applied consistently to scene generation, target placement, navigation evaluation, and safety analysis, so success depends on reaching a position from which the instrument can be accessed. BioVLN supports procedural scene generation and manually designed environments, producing 47 scenes and 1667 episodes. Standardized navigation and reinforcement-learning interfaces enable trajectory collection and policy training. Experiments show that geometric exploration reaches 74.4--87.5% success, while sampling multiple valid positions in the operation area improves success to 83.3--92.5% and reduces unsafe proximity.

cs.RO↗
arXiv2026

The Research and Development of New Electronics System and its Testing on the JNE-1ton Prototype Detector

The Jinping Neutrino Experiment (JNE), a next-generation neutrino observatory under construction at the China Jinping Underground Laboratory II (CJPL-II), requires high-precision waveform-based event reconstruction, imposing stringent demands on its readout electronics. To meet these requirements, we have developed a high-performance readout system featuring 1 GSa/s real-time sampling, 14-bit physical resolution with an effective number of bits (ENOB) of 10.6, a total data throughput of 64 Gbps, and a deterministic zero-delay clock distribution architecture. The new single-crate 64-channel system (PDS1500) was validated through bench tests and deployment on the upgraded JNE-1ton prototype detector. Its performance was further evaluated against a commercial reference system. The results demonstrate that all key metrics meet the JNE experimental requirements: zero data loss within a 1000 ns acquisition window, baseline noise reduced to one-third of the reference level, timing drift limited to 0.3 ns across power cycles, and an energy threshold as low as 0.1 MeV, enabling the detection of low-energy solar neutrinos. While the 14-bit physical resolution provides significantly higher waveform fidelity, the overall energy resolution in this test remains dominated by the intrinsic limitations of the JNE-1ton detector, as expected. Furthermore, the modular architecture provides the throughput and scalability required to support the full-scale 3000-channel JNE detector. These results collectively demonstrate that the newly developed electronics system fully satisfies the technical requirements of the future JNE experiment.

physics.ins-det↗
arXiv2026

Quantum metric quadrupoles in elemental bismuth thin films

The nonlinear transport properties of solids are deeply rooted in the quantum geometry of their electronic wavefunctions, which is encoded in the quantum geometric tensor. Its real part, known as the quantum metric, has been recently identified as a primary origin of nonlinear transport in quantum materials where time-reversal and inversion symmetries are not simultaneously present. Consequently, the influence of the quantum metric on the largest class of materials -- non-magnetic and centrosymmetric systems -- has remained entirely elusive. Here, we demonstrate that third-order transport in centrosymmetric materials hosting relativistic fermions is governed by quantum metric quadrupoles (QMQs). We show that these QMQs can originate from both the non-Abelian quantum geometry of bulk three-dimensional Dirac fermions and the Abelian quantum geometry of spin-orbit-coupled surface states. In stark contrast to all zero-field nonlinear transport signatures known to date, the current driven by these QMQs persists as a robust, non-vanishing observable even in highly scalable polycrystalline thin films. We experimentally validate this quantum metric footprint by measuring nonlinear transport in thin films of elemental bismuth, observing a robust, surface-dominated, and broadband third-harmonic generation that persists up to room temperature. Our findings uncover a hidden role of the quantum metric in polycrystalline systems, establishing third-order nonlinear transport as a high-precision diagnostic tool of wavefunction geometry under ambient conditions.

cond-mat.mes-hall↗
arXiv2026

Observation of an emergent energy scale close to dimensional reduction in a quasi-two-dimensional quantum magnet

By appropriately perturbing a critical transverse-field Ising chain away from its critical point, the system can develop a finite correlation length with a characteristic purely massive spectrum, whose ratios and correlations are precisely described by an integrable field theory and an infinite set of integrals of motion corresponding to the $E_8$ Lie algebra. In this work, we report on experimental observation of a characteristic massive spectrum close to transverse field-induced dimensional reduction in a quasi-two-dimensional quantum magnet Cu$_2$(OH)$_3$Br, providing evidence for an emergent $E_8$ symmetry and the corresponding excitations of bound states in the sublattice of its ferromagnetic chains. These results demonstrate the power of integrable field theory in describing emergent many-body quantum critical phenomena in condensed matter systems.

cond-mat.str-el↗
arXiv2026

Finite-time Scaling of the surface special transition in a 3D classical Heisenberg model

We investigate nonequilibrium driven dynamics across the special surface phase transition in the three-dimensional classical Heisenberg model with open boundaries, where tuning the surface coupling gives access to an extraordinary-log boundary critical state characterized by logarithmic, rather than power-law, decay of correlations. Using Monte Carlo simulations, we realize four driving protocols: temperature heating and cooling across the special transition, and surface-coupling ramps from the ordinary and extraordinary-log critical states into the special point. For temperature-driven protocols, the surface order parameter obeys a generalization of the finite-time scaling (FTS) and the Kibble-Zurek mechanism. The central finding emerges when the system is driven from the extraordinary-log critical state: the large-rate scaling relation acquires a logarithmic correction and takes the novel form $M^{2}_{s}\propto R^{(1+η_{s})/r_{s}}[\log(LR^{1/η_{s}})]^{-q}$ , where $R$ is the driving rate, $L$ the system size, $η_{s}$ the surface anomalous dimension, $r_{s}$ the scaling dimension of $R$, and $q$ the exponent governing the logarithmic boundary criticality. We demonstrate that this form follows from the general FTS framework by incorporating the logarithmic initial-state memory, and we achieve excellent data collapse over a wide range of system sizes and driving rates. Our results establish that extraordinary-log initial states alter nonequilibrium critical scaling, extending boundary FTS beyond conventional power-law initial conditions.

cond-mat.stat-mech↗
arXiv2026

A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation

Pathological assessment guides lung cancer diagnosis, treatment selection, and prognostic evaluation, yet current CPath approaches rely on task-specific models for isolated objectives. Although pan-cancer foundation models offer versatility, they lack subspecialty-level depth and have not been evaluated across clinical workflows or prospectively validated in real-world settings. We introduce PulmoFoundation, a multi-center, prospectively validated, randomized controlled trial (RCT)-evaluated foundation model for comprehensive lung pathology assessment across pre-operative, intra-operative, and post-operative care. Built upon Virchow2 via subspecialty-specific pretraining using ~40,000 diagnostic H&E-stained whole-slide images (WSIs), PulmoFoundation was systematically evaluated on ~26,000 WSIs across 32 clinically relevant tasks. In addition to accurately predicting molecular markers and patient survival, our model achieves clinical-grade performance in core diagnostic tasks across biopsy, frozen section, and surgical resection slides. In a registered prospective study of 1,357 patients across 11 diagnostic tasks, our model achieved an average AUC of 92.3%. Using pre-specified triage thresholds, PulmoFoundation could reduce additional second-review burden for 68.8% of biopsies and 83.0% of frozen sections, and defer 44.5% of IHC stain orders, with PPVs of 1.000, 0.991, and 0.966. Beyond prospective validation, we conducted a crossover RCT with eight pathologists, in which AI assistance improved diagnostic accuracy across 5,264 case-reader pairs (91.7% w/ AI vs. 83.2% w/o AI). AI assistance also reduced median diagnostic time by 18.3%, increased diagnostic confidence by 9.0%, and improved inter-rater agreement from moderate (kappa = 0.55) to substantial (kappa = 0.76). Together, these evaluations support PulmoFoundation as a clinically validated decision-support system for lung pathology.

eess.IV↗
arXiv2026

Mixed-State Phase Transitions in Measurement-Dressed Imaginary-Time Evolution

Motivated by the ubiquity of decoherence in quantum hardware and the growing role of imaginary-time evolution (ITE) in quantum algorithms, we investigate how many-body correlations generated by imaginary-time filtering are modified by local decoherence. We introduce measurement-dressed imaginary-time evolution (MDITE), which alternates ITE with projective-measurement channels, producing a competition between low-energy filtering and local dephasing. By developing a new efficient quantum Monte Carlo method, we uncover MDITE mixed-state transitions with spontaneous-symmetry-breaking signatures in the driving of 1D transverse-field Ising and 2D columnar dimerized Heisenberg Hamiltonians in the resulting density matrices. In the continuous limit, the Choi-Jamiolkowski mapping yields a tractable equilibrium description with conformal criticality that qualitatively captures the phase transitions. At finite protocol parameters, however, the four-point correlator violates the conformal cross-ratio form and the critical exponents deviate from their continuous-limit values, signaling the loss of conformal symmetry and richer nonequilibrium criticality. Our results establish MDITE as a controlled setting for exploring mixed-state phases and critical phenomena driven by the interplay between imaginary-time filtering and decoherence.

quant-ph↗
arXiv2026

Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics

Wet-lab robots can improve the reproducibility, throughput, and safety of biomedical experiments, but scaling their learning requires customizable simulators for safe and reproducible task generation, open editable laboratory assets, and efficient pipelines that turn limited demonstrations into usable training data. We present Pipette, an embodied simulation platform, benchmark, and data-efficient augmentation framework for wet-lab robot learning. Pipette provides over 100 open-source and re-editable wet-lab assets through an extensible asset-building pipeline with built-in Tencent Hunyuan support for text- and image-conditioned 3D asset generation, and supports three robotic-arm embodiments through a unified simulation interface for task construction, data collection, augmentation, and evaluation. A key component of Pipette is its simulation-based data augmentation pipeline, which replays human demonstrations in simulation, applies lighting, camera, speed, and action perturbations, and filters generated episodes with automatic task success checks, rapidly expanding usable training data from limited manual demonstrations. We further introduce a 12-task wet-lab embodied benchmark covering sample handling, culture-ware manipulation, device operation, and precision placement. With only 30 demonstrations per task, ACT achieves 60.3% average success rate, while simulation augmentation improves SmolVLA from 40.4% to 71.8% and pi0 from 37.3% to 44.1%, validating the effectiveness of Pipette for data-efficient VLA training and evaluation. Pipette also supports natural-language-driven scene construction and task registration, lowering the barrier for non-expert users to define new wet-lab robotic tasks

cs.RO↗
arXiv2026

SD-MAR: Multi-image Analytical Reasoning via Synthetic Data and Reinforcement Learning

Vision Language Models (VLMs) demonstrate strong perceptual abilities but remain limited in tasks requiring analytical reasoning across multiple visual states, such as multi-image comparison, change detection, and multi-step visual inference. These capabilities are critical for real-world multimodal applications where reasoning must be grounded in systematic differences between visual contexts. However, existing benchmarks rarely require both explicit visual comparison and analytical reasoning, leaving this capability underexplored. To address this gap, we introduce SD-MAR (Synthetic Data for Multi-image Analytical Reasoning), a framework for training and evaluating VLMs on multi-image analytical reasoning. SD-MAR constructs paired visual scenarios through controlled perturbations and generates reasoning tasks spanning semantic change attribution and quantitative comparison. We further train VLMs using GRPO-lite with Backward Discounted Allocation (BDA), a reinforcement learning approach that removes KL regularization to encourage stronger policy optimization while allocating greater credit to the later reasoning steps where analytical conclusions are formed. Experiments on Qwen2.5-VL-7B and InternVL3-8B show that GRPO-lite fine-tuning on SD-MAR improves in-domain accuracy by up to 36.95%, with Qwen2.5-VL-7B outperforming GPT-4.1 on the SD-MAR benchmark. Importantly, out-of-domain generalization is preserved or improved: performance remains within 1% on MME, MMMU-Pro, and MathVista, while improving by up to 4% on MMBench. LLM-as-judge evaluation further demonstrates consistent improvements in logical coherence and explanation quality across both models.

cs.CV↗