arXiv subjects

Weibin Li

Publications and source records attributed to Weibin Li.

At least 19 recordsLinked to original sources

arXiv2026

Entanglement-enabled Criticality in One-dimensional Quantum Contact Process

The contact process is a paradigmatic example of nonequilibrium dynamics, with broad applications ranging from chemistry to sociology. Its quantum counterpart, the quantum contact process (QCP), extends the classical model to include coherent processes. Despite sustained interest, the nature of the transition in the one-dimensional (1D) QCP remains debatable. Here, combining Liouvillian spectral analysis, the tensor jump method, exact quantum jump Monte Carlo, and truncated Wigner simulations, we show that 1D QCP undergoes a continuous absorbing-state phase transition, with critical exponents distinct from the classical case. We further find Liouvillian gap closes well below the critical point, highlighting that spectral gap analysis alone cannot distinguish a phase transition from metastability in the QCP. Crucially, the 1D QCP is weakly entangled, yet even this weak entanglement is indispensable for capturing the correct critical behavior, whereas semiclassical methods artificially stabilize the active state and predict a spurious first-order transition. Our work establishes the quantum origin of the phase transition in the 1D QCP and underscores the essential role of entanglement in dissipative quantum many-body systems.

quant-ph↗

arXiv2026

Charge transfer and competing symmetry breaking drive orbital reconstruction and emergent ferromagnetism in insulating oxide superlattices

Electron correlation, hopping, and ligand-to-metal charge transfer collectively lead to diverse electronic and magnetic phenomena in 3$d$ transition-metal oxides, where directional d orbitals make hopping highly sensitive to symmetry-dependent orbital overlap. Heterostructure engineering with atomically flat interfaces adds symmetry-breaking charge transfer as a further route to emergent behavior, yet whether interfacial mismatch between constituent oxides of a superlattice shapes ground states independent of epitaxial strain remains unresolved. Here we examine superlattices combining NdNiO$_3$ with Mott-insulating NdMnO$_3$. Varying layer thickness and combining transport with X-ray spectroscopy, we show that electron transfer from NdMnO$_3$ to NdNiO$_3$ drives a room-temperature insulating state with a distinct electronic structure, accompanied by a reversal in orbital symmetry beyond simple strain considerations, underscoring the interface's central role. These reconstructions stabilize an emergent ferromagnetic insulating state arising from interfacial Ni$^{2+}$-O-Mn$^{4+}$ superexchange. Our results establish a pathway to interface-engineered ferromagnetic insulating phases via competing interactions, with potential for spin-insulatronic applications.

cond-mat.mtrl-sci↗

arXiv2026

DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton

Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action semantics depend on coordinated roles and spatial-temporal dependencies. We formalize this capability as \textbf{multi-agent tactical reasoning} and introduce \textbf{DoublesEval}, a diagnostic evaluation framework that leverages professional doubles badminton as a structurally tractable testbed. DoublesEval employs a key-moment-based protocol that decomposes rallies into tactically salient instants and probes models across four interpretable dimensions: atomic recognition, intra-segment composite understanding, cross-segment causal reasoning, and high-level tactical abstraction. This design isolates \emph{where} reasoning fails, rather than merely measuring answer correctness. To address observed failure modes, we propose \textbf{TacticCheck}, a lightweight constraint-guided test-time consistency checker that reranks candidate answers using the model's own lower-level tactical predictions, requiring no parameter updates or ground-truth labels at inference time. Evaluating four representative open-source VLMs on 60 curated rallies (yielding $\sim$9.6K structured instances) via a zero-shot protocol, we find that models remain weak across all diagnostic levels, with especially clear bottlenecks in spatial state, interaction binding, and terminal evidence. TacticCheck delivers consistent gains across all evaluated models, while still leaving a substantial gap to robust tactical reasoning. These results highlight the need for structured, interaction-aware evaluation paradigms for next-generation VLMs. The source code is available in \href{https://github.com/Chengjt1999/DoublesEval}{\textcolor{blue}{our GitHub repository}}.

cs.CV↗

arXiv2026

Nonthermal Dynamics of a One-dimensional Rydberg-atom Chain with Constraint Four-body Interactions

We investigate dynamics of a linear chain of Rydberg atoms driven by a constrained four-body interaction, where two neighboring atoms are excited simultaneously from the electronic ground state $|0\rangle$ to Rydberg state $|1\rangle$ only when their closest neighbors are in $|0\rangle$ state. By employing an ansatz for the many-body ground state, the low-energy Hamiltonian is given by a tridiagonal form. The many-body ground state energy, which scales linearly with the chain length $L$, is obtained analytically in the thermodynamic limit and agrees with the one of the exact diagonalization. Our model supports quantum many-body scar eigenstates that are nearly equally spaced energetically. The scar states overlap strongly with the basis state $|\mathbf{0}\rangle=|0\cdots 0\rangle$. We show that the overlap distribution is tilted by the state-dependent four-body interaction. This leads to non-ergodic dynamics, evidenced by the revival of the initial state. The four-body constrained model can be realized with Rydberg atoms in a Peierls array with alternating bond lengths, where atoms on the shorter and longer bonds experience Rydberg blockade and antiblockade, respectively. Our study provides a pathway to explore constrained non-ergodic dynamics with four-body interactions by combining the Rydberg blockade and antiblockade.

quant-ph↗

arXiv2026

Optical nonlinearity in a strongly interacting Rydberg atom ensemble

Rydberg atoms, characterized by their giant polarizability and strong long-range interactions, provide an ideal platform for exploring optical nonlinearities. By coupling light to ensembles of Rydberg atoms, one can map the strong and nonlocal interactions between Rydberg atoms and light fields. A particularly important scheme for realizing such nonlinearity is electromagnetically induced transparency (EIT). This article presents a review of the research progress in nonlinear optics based on Rydberg-EIT systems, particularly nonlinear light propagation in Rydberg atomic gases. We first introduce the fundamental properties of Rydberg atoms and the physical mechanism of EIT-enhanced nonlinearity, emphasizing how the Rydberg blockade effect endows the system with a giant nonlocal Kerr nonlinearity. Subsequently, we discuss some typical nonlinear optical phenomena in such a system with focusing and defocusing nonlocal nonlinearities. In the focusing regime, we discuss spatial solitons, vortex solitons, soliton cloning, soliton molecules, optical bullets, and self-induced transparency solitons. Other topics are also discussed. In the defocusing regime, we analyze pattern formation and shock wave dynamics. Finally, we provide an outlook on the future development of this field toward quantum control, hybrid-system integration, and novel functional devices.

physics.optics↗

arXiv2026

Test-Time Prototype Adaptation for Open-Vocabulary Semantic Segmentation

Open-vocabulary semantic segmentation (OVSS) repurposes a pretrained CLIP encoder for dense prediction without additional labeled supervision. Existing methods improve CLIP's spatial behavior either by redesigning its internal attention or by injecting features from auxiliary vision foundation models; both require access to the host's internal computation and are tailored to its specific forward pass. In this work, we propose Test-time Prototype Adaptation (TPA), a training-free plug-in that operates at the output level, leaving the host's forward pass and weights unmodified. By leveraging a lightweight transductive adaptation phase, TPA identifies confident anchor patches from the host's own output predictions on a small pool of unlabeled deployment-domain images, and aggregates their frozen DINO features into per-class prototypes; at inference, a single cosine similarity lookup against this frozen bank provides an auxiliary score fused linearly with the host's logits. TPA composes with five representative OVSS hosts spanning attention-redesign and VFM-injection designs, across three CLIP backbones, eight benchmarks, and multiple internal VFM choices. Under a single set of hyper-parameters and without per-host tuning or parameter updates, TPA consistently improves segmentation accuracy, with as few as approximately 10% of unlabeled deployment-domain images sufficing for effective bank construction on most benchmarks.

cs.CV↗

arXiv2026

Dynamical phase transition in generalized Dicke model with strongly interacting trapped Rydberg ions

We study dynamical phase transitions in the generalized dissipative Dicke model in an array of trapped Rydberg ions, where their density-density interactions compete with the collective spin-phonon coupling, laser driving and dissipation. This setting offers a versatile approach to study equilibrium as well as non-equilibrium many-body phenomena, as parameters, such as the Ising interaction, laser-ion and spin-phonon coupling can be tuned. Through analyzing the mean-field phase diagram, we find a variety of distinct phases and the emergence of a tricritical point that are sensitively dependent of the interaction between Rydberg ions. We then study the quantum dynamics for a finite system size and characterize parameter dependent dynamics using the spin average, entropy, and Loschmidt echo. Distinctive signatures of the dynamical phases, such as slow relaxation and metastability, arise near the phase transition. This analysis predicts rich quantum dynamics of the finite system that link to the non-equilibrium mean-field phases. Our study widens the exploration of collective and non-equilibrium phases in Dicke models, and reveals that the Rydberg ion interaction drastically affects the phase diagram and dynamics.

quant-ph↗

arXiv2026

Many-Body Physics with Rydberg Atoms: Quantum Simulation and Non-equilibrium Dynamics

Rydberg atoms, characterized by their strong and long-range dipole-dipole interactions, provide a versatile platform for exploring intriguing collective and many-body effects. Recently, the experimental realization of these effects in dense ensembles and reconfigurable atomic arrays has attracted significant interest, particularly for applications in quantum simulations and non-equilibrium physics. This review focuses on such recent development, discussing the theoretical foundations of the interactions between Rydberg atoms and the ensuing many-body physics, while providing a critical survey of experimental techniques for their precise manipulation and observation. We further discuss recent breakthroughs in leveraging Rydberg collective effects to probe novel many-body phases and non-equilibrium dynamics of these systems. By synthesizing theoretical insights with experimental milestones, we provide a comprehensive perspective on this rapidly evolving field and its transformative potential for future quantum technologies.

quant-ph↗

arXiv2026

NeuroOnline: Bridging Pretraining and Online Adaptation for EEG Foundation Models

EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks. However, most existing approaches follow a pretraining-static deployment paradigm, which suffers from two key limitations: (1) misalignment between pretraining objectives and downstream tasks, and (2) limited adaptability to distribution shifts in online settings. We propose Online Neural Adaptation (NeuroOnline), a unified framework that enables continuous adaptation in online scenarios. NeuroOnline integrates two complementary mechanisms: (1) multi-view consistency learning, which enforces cross-view alignment to promote consistent and task-relevant representations, and (2) context-aware representation modulation, which leverages a learnable context prompt with cross-attention to dynamically adapt representations to evolving data distributions. Together, these mechanisms unify representation alignment and dynamic adaptation. Experiments on multiple EEG benchmarks show that NeuroOnline consistently outperforms strong baselines in online settings, achieving better performance under distribution shifts. Ablation and sensitivity studies further validate the necessity of each component and the effectiveness of the overall design.

cs.LG↗

arXiv2026

Observation of quantum multi-Mpemba effect in a trapped-ion system

The quantum Mpemba effect (ME) in Markovian systems is conventionally explained by a smaller overlap between the initial state and the slowest decay mode (SDM). Such state, initially farther away from equilibrium or steady state, relaxes faster than closer ones, resulting to a crossing of their trajectories. This picture, by neglecting the transient dynamics, holds in the long-time limit. Here we experimentally observe multiple trajectory crossings (multi-ME) in the relaxation dynamics of a trapped ion. Such novel dynamics takes place in a unusual scenario where the initial state instead has a larger overlap with the SDM. We develop a theoretical framework based on relaxation speed to understand the multi-ME. We show that the initial relaxation speed is governed by the fastest decay mode, which together with the SDM overlap gives a phase diagram that reveals both the occurrence and the types of quantum ME observed in our experiment. Our study goes beyond the simple picture based on the long-time limit, tracks continuously the quantum ME dynamics, and establishes a comprehensive framework to describe the transient quantum relaxation.

quant-ph↗

arXiv2026

Atomic-Scale Detection of N\'eel Vector Switching in the Single-Layer A-type Antiferromagnet Cr2S3-2D

The detection of N\'eel vector switching in a single-layer A-type antiferromagnet marks an important step toward functional two-dimensional spintronics. Here, Cr$_2$S$_3$-2D, grown on graphene on Ir(110), is established as a first single-layer A-type antiferromagnet. Spin-polarized scanning tunneling microscopy reveals hysteresis loops with a large switching field and a pronounced dependence on island size. X-ray magnetic circular dichroism at the Cr L$_{2,3}$ edges exhibits a tiny signal with a linear magnetic field dependence, consistent with a nearly compensated antiferromagnetic ground state and a N\'eel temperature of about 160 K. Quantitative analysis of the island-size dependence of the switching field, together with first principles calculations, indicates a slight imbalance between the magnetic moments of the two Cr planes of Cr$_2$S$_3$-2D when supported on a substrate. This imbalance results in a net magnetization for the A-type antiferromagnet, which enables the 180$^\circ$ rotation of the N\'eel vector. Moreover, Cr$_2$S$_3$-2D retains its magnetic properties after several days of exposure to air.

cond-mat.mes-hall↗

arXiv2026

Multipartite controlled-NOT gates using molecules and Rydberg atoms

We propose high-fidelity controlled-NOT (CNOT) gates in a hybrid system of polar molecules and Rydberg atoms based on the unconventional Rydberg pumping mechanism. By combining the rich internal structure of polar molecules with the strong dipole-dipole interactions of Rydberg atoms, we realize both two-to-one and one-to-two gate configurations. Numerical simulations show that the gate performance is robust against spontaneous emission from Rydberg states. The approach naturally extends to larger systems, as demonstrated by four-qubit implementations achieving three-to-one and one-to-three CNOT gates with fidelities exceeding 99\%. These results highlight hybrid molecule-Rydberg atom architectures as a promising platform for scalable quantum information processing.

quant-ph↗

arXiv2026

Training-Free Interaction-Aligned Visual Token Pruning for Efficient Embodied Manipulation

Efficient visual representation is a central image-processing challenge in embodied manipulation, where policies repeatedly process dense visual-token sequences during closed-loop control. Existing methods rank or prune tokens using semantic relevance, VLM attention, cross-frame redundancy, or motion in the action space. These signals may discard task-relevant regions when instruction-related appearance and observed image motion are not yet spatially aligned. We introduce Interaction-Aligned Pruning (IAprune), a training-free method that treats per-frame budget setting and within-budget token selection as two linked decisions. Semantic--motion spatial agreement guides the choice between Conservative and Aggressive coverage, and the resulting region size is mapped to a calibrated dynamic budget. Continuous semantic and motion responses rank the existing tokens, while geometric residual correction redirects fixed selection slots toward under-represented boundaries without increasing the sequence length. Across four embodied manipulation policies, three simulation benchmarks, and a real-robot platform, IAprune provides a favorable accuracy--efficiency trade-off, matching the unpruned policy on LIBERO with a $1.54\times$ speedup and reaching $1.48\times$ acceleration on a real robot. Phase-wise analysis shows that retention gains are largest under tight budgets early in an episode, while fixed-budget analysis confirms that geometry replaces low-priority tokens with boundary and contact-region evidence rather than retaining more tokens. Our project website is: \href{https://chengjt1999.github.io/VLA-IAP.github.io/}{IAprune.com}.

cs.CV↗

arXiv2026

Accelerated Rydberg electromagnetically induced transparency quantum memory via shortcuts to adiabaticity

Electromagnetically induced transparency (EIT) enables coherent light-matter storage, forming the basis of photonic quantum memories that are essential for scalable quantum networks and distributed quantum computing. However, accelerating the storage process violates the adiabatic condition, resulting in the excitation of the lossy intermediate state and a reduction in writing efficiency. We propose and numerically investigate a high-speed, high-fidelity quantum storage scheme by incorporating a shortcut-to-adiabaticity (STA) technique based on counter-diabatic (CD) driving. By introducing a precisely engineered auxiliary field into a conventional EIT system, our protocol significantly shortens the writing time beyond the conventional adiabatic limit while effectively suppressing the transient population of the lossy intermediate state. Furthermore, our scheme demonstrates strong flexibility in pulse design, remaining effective across different temporal profiles of both the control and signal fields. It also exhibits robustness against imperfections in the CD drive. Even with imperfect single-photon writing and non-ideal Rydberg blockade, the scheme retains clear advantages, maintaining high storage performance and overcoming the intrinsic speed-fidelity trade-off of traditional EIT protocols. These features pave the way for fast and robust quantum devices suitable for high-throughput quantum repeaters and advanced quantum information processing.

quant-ph↗

arXiv2026

Recent advances in Ultralong-range Rydberg molecules

Rydberg molecule, formed by one or more Rydberg atoms, exhibits remarkable properties, including an exceptionally large spatial extent, rich rovibrational level structures, permanent electric dipole moments, and a pronounced sensitivity to external fields. Based on the underlying binding mechanisms, Rydberg molecules can be divided into three categories, the ground-Rydberg molecule that is bound via a low-energy electron-atom scattering interaction between ground atom and Rydberg electron, the Rydberg-Rydberg molecule that is bound via a long-range electrostatic interaction between Rydberg atoms, and the ion-Rydberg molecule that is bound via single- or multi-polar interactions between Rydberg atom and ion. This review focuses on recent theoretical and experimental advances in diatomic Rydberg molecules, covering their formation and binding mechanisms, potential energy curves, experimental observations, and spectroscopic properties, with the aim of providing a comprehensive overview of the current state and future prospects of this rapidly developing field.

physics.atom-ph↗

arXiv2026

AutoHealth: An Uncertainty-Aware Multi-Agent System for Autonomous Health Data Modeling

LLM-based agents have demonstrated strong potential for autonomous machine learning, yet their applicability to health data remains limited. Existing systems often struggle to generalize across heterogeneous health data modalities, rely heavily on predefined solution templates with insufficient adaptation to task-specific objectives, and largely overlook uncertainty estimation, which is essential for reliable decision-making in healthcare. To address these challenges, we propose \textit{AutoHealth}, a novel uncertainty-aware multi-agent system that autonomously models health data and assesses model reliability. \textit{AutoHealth} employs closed-loop coordination among five specialized agents to perform data exploration, task-conditioned model construction, training, and optimization, while jointly prioritizing predictive performance and uncertainty quantification. Beyond producing ready-to-use models, the system generates comprehensive reports to support trustworthy interpretation and risk-aware decision-making. To rigorously evaluate its effectiveness, we curate a challenging real-world benchmark comprising 17 tasks across diverse data modalities and learning settings. \textit{AutoHealth} completes all tasks and outperforms state-of-the-art baselines by 29.2\% in prediction performance and 50.2\% in uncertainty estimation.

cs.AI↗

arXiv2026

Decoding Ambiguous Emotions with Test-Time Scaling in Audio-Language Models

Emotion recognition from human speech is a critical enabler for socially aware conversational AI. However, while most prior work frames emotion recognition as a categorical classification problem, real-world affective states are often ambiguous, overlapping, and context-dependent, posing significant challenges for both annotation and automatic modeling. Recent large-scale audio language models (ALMs) offer new opportunities for nuanced affective reasoning without explicit emotion supervision, but their capacity to handle ambiguous emotions remains underexplored. At the same time, advances in inference-time techniques such as test-time scaling (TTS) have shown promise for improving generalization and adaptability in hard NLP tasks, but their relevance to affective computing is still largely unknown. In this work, we introduce the first benchmark for ambiguous emotion recognition in speech with ALMs under test-time scaling. Our evaluation systematically compares eight state-of-the-art ALMs and five TTS strategies across three prominent speech emotion datasets. We further provide an in-depth analysis of the interaction between model capacity, TTS, and affective ambiguity, offering new insights into the computational and representational challenges of ambiguous emotion understanding. Our benchmark establishes a foundation for developing more robust, context-aware, and emotionally intelligent speech-based AI systems, and highlights key future directions for bridging the gap between model assumptions and the complexity of real-world human emotion.

cs.SD↗

arXiv2026

Beyond First-Order: Learning Riemannian Geometries for Invariant Visual Place Recognition

Visual Place Recognition (VPR) demands representations robust to drastic environmental and viewpoint shifts. Existing aggregation paradigms either depend on extensive supervised training or rely on first-order pooling, often struggling to preserve structural correlations under extreme shifts or incurring high adaptation costs. In this work, we propose Riemannian Invariant Aggregation (RIA), a unified geometric framework that explicitly models second-order scene structure on the Symmetric Positive Definite (SPD) manifold. By treating perturbations as tractable congruence transformations, RIA leverages geometry-aware Riemannian mappings to project covariance descriptors into a linearized Euclidean space, effectively preserving invariant structural components while suppressing noise. Extensive evaluations demonstrate that RIA achieves zero-shot performance comparable to supervised methods, and establishes state-of-the-art accuracy with simple fine-tuning, particularly in unstructured environments. The source code will be released.

cs.CV↗