arXiv subjects

Zhen Gao

Publications and source records attributed to Zhen Gao.

At least 19 recordsLinked to original sources

arXiv2026

Observation of pseudospin-locked chiral edge transport in gyromagnetic photonic crystals

Photonic pseudospins enable selective excitation and routing of light, but perturbations that mix opposite pseudospins can induce backscattering in helical edge channels. Here, we experimentally demonstrate pseudospin-locked chiral edge transport in gyromagnetic photonic crystals, combining pseudospin selectivity with nonreciprocal unidirectional propagation. By jointly engineering lattice deformation and magnetic bias, we control dipolar-quadrupolar band inversions and construct interfaces supporting pseudospin-selective chiral channels. Microwave near-field and transmission measurements reveal a single chiral edge branch that is selectively excited by only one source handedness and exhibits pronounced nonreciprocal transport. Extending this mechanism to multidomain structures, we combine two pseudospin channels into a common waveguide, separate them into distinct outputs, and route them through intersecting interfaces. These configurations realize a nonreciprocal photonic pseudospin multiplexer and demultiplexer, together with a waveguide crossing exhibiting average measured crosstalk of -14.1 and -15.8 dB for its two input channels. Our results present an experimental route to integrating pseudospin-selective channel control with chiral edge transport, providing a foundation for nonreciprocal photonic networks that use pseudospin to organize and route multiple wave channels.

physics.optics↗

arXiv2026

Photonic Exceptional Topological Bound States in the Continuum

Exceptional bound states in the continuum (EBICs) arise when bound states in the continuum (BICs) with distinct intrinsic losses coalesce at exceptional points (EPs), combining continuum-embedded wave confinement with a non-analytic spectral response. However, conventional EBICs rely on precisely controlled modal coupling and intrinsic loss, making them fragile and difficult to realize experimentally. Here, we introduce bulk-band topology to mitigate this fragility and experimentally realize a photonic exceptional topological BIC (ETBIC) in spoof surface plasmon polariton waveguides by merging two symmetry-protected topological BICs (TBICs) to coalesce at an EP, with the topological design supporting robust localization under symmetry-preserving perturbations. By engineering an intrinsic loss contrast and tuning the intermodal coupling, we directly resolve the coalescence of the complex eigenfrequencies, accompanied by measured near-field distributions consistent with the exceptional eigenstate. Furthermore, weak perturbations induce approximately square-root eigenfrequency splitting, yielding an enhanced spectral response compared with the approximately linear frequency shift of an isolated TBIC. These results establish bulk-band topology as a design principle for exceptional BIC engineering and provide a foundation for EBIC-based photonic sensing.

physics.optics↗

arXiv2026

Koszul duality for boundary categories

We study how Koszul duality relates two kinds of boundary constructions associated with a smooth category and a proper category. For a smooth category with a proper Koszul-dualizing subcategory, we construct a fully faithful functor from its categorical neighborhood of infinity to the singularity category associated to the proper category. We prove that this functor is an equivalence precisely when the smooth category is reflexive, meaning that it can be reconstructed from its finite-dimensional module theory. Under a Gorenstein condition, satisfied in the Calabi--Yau setting, the functor is induced by restriction of modules. We give algebraic criteria that ensure equivalence and obtain an identification of Hochschild cohomology.

math.KT↗

arXiv2026

Joint Transmit Power and Reflection Coefficient Optimization for Underlay Cognitive Radio Ambient Backscatter Communication

The integration of cognitive radio (CR) technology with backscatter communication offers a promising solution for developing spectrum-efficient and low-power wireless networks. In this paper, we investigate the performance of an underlay cognitive radio ambient backscatter communication (CR-AmBC) system operating under primary network (PN) interference constraints. The objective is to maximize the performance of the CR-AmBC system while protecting the PN from interference. Differing from prior work, our formulation jointly accounts for: (i) interference from the PN on the CR receivers, (ii) interference from the backscatter device (BD) on the PN receiver, and (iii) imperfect successive interference cancellation (i-SIC) at the secondary destination. We propose a joint transmit power and reflection coefficient optimization framework that incorporates the minimum data rate requirements, the PN interference constraint, and i-SIC for decoding the BD signal. The nonconvex problem is decomposed into two subproblems solved via alternating optimization: (a) a closed-form expression for the feasible transmit power given a fixed reflection coefficient, and (b) an efficient one-dimensional search for the reflection coefficient. We prove that the power update is optimal, reducing the joint problem to a one-dimensional search, with monotone convergence and $\mathcal{O}(KN_β)$ complexity for $K$ iterations and $N_β$ search points. We present a comprehensive simulation-based performance evaluation and the results demonstrate that the proposed joint transmission power and reflection coefficient optimization provides an effective strategy to maximize the performance of the CR-AmBC system without violating the PN protection constraints.

eess.SP↗

arXiv2026

Time-Reversal-Invariant Altermagnetic Acoustic Crystals

Altermagnets have emerged as a new class of magnetic materials that combine spin-split electronic bands with zero net magnetization. Extending this paradigm to classical-wave systems has, however, been fundamentally challenging because conventional realizations require broken time-reversal symmetry (TRS). Here, we overcome this limitation by introducing a pseudospin degree of freedom and constructing a pseudo-time-reversal operator that faithfully reproduces the action of its physical counterpart while preserving actual TRS. Building on this framework, we theoretically propose and experimentally realize the first time-reversal-invariant altermagnetic acoustic crystal. Acoustic measurements directly reveal pseudospin-dependent band splitting--a defining hallmark of altermagnetism--under strictly TRS-preserving conditions. Moreover, the altermagnetic acoustic crystal exhibits sublattice-pseudospin locking, enabling flexible control over acoustic pseudospin splitting and filtering. Our work establishes acoustic crystals as a versatile platform for exploring altermagnetic physics and opens new avenues for spin-inspired wave manipulation in nonmagnetic devices.

cond-mat.mes-hall↗

arXiv2026

SPARE: Shared Perception and Adaptive Retrieval of Evidence for Streaming Video Understanding

Streaming video understanding requires answering questions from the observed video, with evidence drawn from recent observations or earlier events. Historical context can improve recall but may interfere with judgments based on recent frames. Its usefulness depends on both when it is introduced and which clips are selected, while relevance alone does not guarantee sufficient evidence for an answer. We propose SPARE, a framework that combines adaptive historical retrieval with visual encoder sharing. A lightweight gate predicts whether recent observations provide sufficient evidence to answer the question. When additional evidence is predicted to be necessary, a multimodal embedding model retrieves relevant historical clips to supplement the current context. LoRA instruction tuning trains the understanding model to use recent observations and retrieved history jointly and abstain when the evidence remains insufficient. To reduce redundant visual parameters, the understanding and retrieval branches share a visual encoder while retaining separate language backbones. Four linear adapters align the shared features with the retrieval model through distillation at the visual token and final retrieval embedding levels. With visual-only inputs, SPARE achieves 77.78% on the evaluated Real-time and Backward task groups of OVO-Bench and 73.31% on StreamingBench. Compared with Qwen3-VL-8B-Instruct evaluated at 2 fps, SPARE improves accuracy by 20.68 percentage points on OVO-Bench and 14.15 percentage points on StreamingBench.

cs.CV↗

arXiv2026

Cavity-tunable Pseudospin-Hall Topology and Edge Transport

Topological edge states are a hallmark of topological phases, with their existence tied to bulk band topology through bulk-edge correspondence. In photonic pseudospin-Hall systems, these states exhibit pseudospin-momentum locking, with lattice interactions and interface geometry determining each pseudospin's propagation direction. Reversing the pseudospin-dependent propagation direction without altering the lattice configuration or excitation handedness remains a fundamental challenge in controlling topological edge transport. Here, we theoretically and experimentally demonstrate cavity-tunable topological edge transport in a microwave metasurface embedded within a metallic cavity. Varying the cavity height reshapes resonator interactions, driving band inversion and topological phase transition while preserving the lattice geometry. For a fixed domain-wall configuration, this transition reverses the propagation direction associated with each pseudospin while preserving pseudospin-momentum locking. Excitations with the same handedness are observed to launch edge waves propagating in opposite directions at their respective edge-band frequencies determined by the cavity height. These results establish cavity electrodynamics as an external means of controlling the bulk topology and reconfiguring the directional response of topological interfaces, creating new opportunities for topological wave routing in fixed photonic lattice structures.

physics.optics↗

arXiv2026

LUNA: Luneburg-Lens-Aided Reconfigurable Array for 6G-and-Advanced Wireless Networks

This article introduces the LUneburg-lens-aided recoNfigurable Array (LUNA), an antenna architecture that unifies multiple-input multiple-output (MIMO) and network-controlled repeater (NCR) functionalities in the Luneburg lens-enabled hardware platform. A Luneburg lens, fabricated from graded-index dielectric materials, passively converts the radiation of a low-gain feed into a highly directional beam without active phase shifting, while a dense passive feed bank and a reconfigurable feed-selection network electronically switch the beam directions with minimal hardware complexity and power consumption. We commence by reviewing the basic principles and application history of Luneburg lenses in radar and wireless communications, which motivates their role in 6G-and-advanced networks. Then, we highlight how a Luneburg lens and a reconfigurable feed array construct both LUNA-MIMO and LUNA-NCR, where the lens and feed bank can be reused across functions and frequency bands. Case studies demonstrate that LUNA achieves the satisfactory spectral and energy efficiency with a few radio-frequency chains, and it also improves positioning performance for sensing tasks. Finally, some open problems and research directions are provided to inspire follow-up research on LUNA.

eess.SP↗

arXiv2026

Deep Learning-Based Tri-Hybrid Multi-User MIMO Precoding: The Blessing of EM-Reconfigurable Antennas

Electromagnetic (EM)-reconfigurable antennas provide multiple candidate radiation patterns per element, thereby introducing an additional EM-domain degree of freedom. Integrating radiation-pattern reconfigurability, realized as EM-domain precoding, with conventional hybrid analog-digital precoding yields tri-hybrid multiple-input multiple-output (MIMO) precoding, which can substantially improve the spectral efficiency of wideband multi-user MIMO orthogonal frequency-division multiplexing (OFDM) systems. However, the joint design of EM, analog, and digital precoding remains challenging. To address this challenge, we propose a tri-hybrid precoding network (Tri-PNet) based on Conformer, an emerging neural architecture that combines the local modeling strength of convolutional neural networks with the global dependency modeling of Transformers. Furthermore, two representative radiation-pattern modes, i.e., the non-regular mode and the 3rd Generation Partnership Project (3GPP) Technical Report (TR) 38.901 mode, are investigated. Tri-PNet is trained in an unsupervised manner to jointly learn EM, analog, and digital precoding by maximizing the average sum spectral efficiency. Its radiation-pattern selection network (RPSNet) employs a Conformer encoder to capture both local and global frequency-domain correlations, whereas its hybrid analog-digital precoding network (HPNet) combines cross-attention and dual-path processing with singular-value-decomposition (SVD) and zero-forcing (ZF) priors. Simulation results under both radiation-pattern modes demonstrate that Tri-PNet outperforms random EM precoding and conventional hybrid MIMO without EM precoding, approaches the greedy EM precoding search scheme with substantially lower online complexity, and remains robust to imperfect channel state information (CSI).

eess.SP↗

arXiv2026

Robust multichannel bulk transport in a time-reversal-invariant insulator-free photonic waveguide array

Ultracompact cladding-free waveguide arrays with zero inter-channel spacing and negligible crosstalk open a new avenue for high-density integrated photonic circuits. However, existing cladding-free waveguide arrays typically rely on conventional trivial bulk modes, making them highly susceptible to scattering losses at sharp bends or in the presence of obstacles and defects. To overcome this limitation, we theoretically propose and experimentally demonstrate a robust, crosstalk-free, and cladding-free photonic waveguide array based on chiral anomaly bulk states (CABSs) in photonic crystals. By interfacing distinct Dirac photonic crystals that host Dirac cones at different high-symmetry points (Γ and K) in the Brillouin zone and carefully engineering the boundary conditions, the boundary-induced CABSs in adjacent channels become effectively decoupled due to a large momentum separation, thereby eliminating inter-channel crosstalk. More importantly, we experimentally demonstrate that these crosstalk-free CABSs are robust to perturbations, including metallic obstacles, air defects, and sharp bends. We further extend the CABS-based waveguide array to two dimensions and demonstrate a cladding-free triangular resonator and a crosstalk-free waveguide crossing, both of which are previously unattainable. Our work establishes a new design paradigm for cladding-free, crosstalk-free, and ultracompact topological photonic devices, paving the way for robust, highly integrated photonic circuits.

physics.optics↗

arXiv2026

SARA: SLO-Aware Resource Allocation for Disaggregated Agentic LLM Services

Recent advances in large language models (LLMs) are driving the emergence of multi-modal and agentic services for mobile users through cloud and edge infrastructures, where long-context workloads pose daunting challenges for inference latency. Existing disaggregated LLM serving systems largely rely on hardware profiling, configuration enumeration, or heuristic scheduling, offering limited analytical guidance for cost-efficient resource allocation. In this paper, we propose SARA, a Service level objectives (SLOs)-Aware Resource Allocation framework for disaggregated agentic LLM serving systems, which maximizes goodput under a deployment cost constraint and a series of quantile-based SLO constraints. By capitalizing on queuing theory, we first model the prefill, KV cache transfer, and decode stages as an M/G/k queue, an M/G/1 queue, and a generalized birth-death process, respectively. The analysis reveals that the prefill and decode stages are dominantly limited by computational capacity and high-bandwidth memory (HBM) resources, respectively. With these mathematical models, we further derive tractable tail behaviors of different stage-wise service level metrics for both light- and heavy-tailed workloads. These characterizations explicitly map workload, model architecture, and hardware parameters to stage-wise SLO constraints and minimum resource requirements. Finally, we develop an effective resource allocation framework to maximize system goodput under limited cost budgets. Simulation and hardware results demonstrate that the proposed framework accurately predicts the stage-wise SLO with mean errors below 5%, and improves system goodput by 26.6% on average over state-of-the-art baseline methods under the same deployment cost.

cs.PF↗

arXiv2026

Spatiotemporal topological phase transitions in photonic spacetime crystals

Topological phase transitions have played a central role in topological physics. However, such transitions have so far been restricted to spatial or temporal crystals. Here, we transcend this conventional framework and report, for the first time, spatiotemporal topological phase transitions in photonic spacetime crystals - structures that are periodically modulated in both space and time. In a genuine photonic spacetime crystal composed of a dynamically modulated transmission-line metamaterial, we theoretically propose and experimentally demonstrate complete spatiotemporal topological phase transitions, characterized by the closing and reopening of both energy and momentum band gaps, along with changes in spatiotemporal topological invariants and topological phases. Furthermore, we directly observe a spatiotemporal, topologically localized state that exhibits causality-governed excitation and robustness to spatiotemporal disorders. Our findings reveal the interplay among space, time, and topology, establishing a unified framework that provides a comprehensive picture of the emerging topological spacetime physics and opening new avenues for robust spatiotemporal topological wave manipulations.

physics.optics↗

arXiv2026

From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity

The ambitious requirements of sixth-generation (6G) networks are driving communication systems from reliable bit delivery toward meaning-aware and task-oriented connectivity. Large models (LMs), with strong multimodal understanding and generation capabilities, have accelerated this shift and made semantic communication (SemCom) increasingly practical. Yet current LM-driven SemCom remains fragmented: semantic representations are typically tied to specific modalities, models, or tasks. While the bit provides a universal unit for digital transport, there is still no analogous unit for representing and processing semantics, which limits interoperability, theoretical unification, and scalable system design. We argue that tokens provide a natural candidate for this missing abstraction. Two trends support this: unified multimodal LMs now encode text, images, audio, video, and robot actions in one token space, while distributed LM inference already generates substantial token-level traffic through expert routing, cache transfer, and speculative decoding. Token communication (TokenCom) emerges by unifying these trends, using the LM's native processing unit as a communication abstraction above the bit level and enabling importance assignment, error handling, and resource allocation directly at token granularity. This survey traces the evolution from LM-driven SemCom to TokenCom. We review three major directions of LM-driven SemCom: source-centric semantic coding, channel semantics for physical-layer tasks, and collaborative edge-device intelligence. We then examine the token abstraction, the transmission techniques it requires, and two emerging paradigms, namely TokenCom for LM services and for embodied and agentic intelligence. Finally, we identify open challenges toward unified, scalable, and AI-native 6G communication systems.

eess.SP↗

arXiv2026

Feature Reconfiguration With Visual Prior for Medical Lesion Segmentation

Lesion segmentation in medical images plays a critical role in clinical diagnosis and treatment planning. Despite significant advances, lesion segmentation remains challenging due to two major factors: (1) complex background interference; (2) diverse lesion morphology. Existing encoder-decoder based methods mainly focus on enhancing feature extraction or redesigning decoding strategies. However, they lack early prior guidance and feature reconfiguration during the encoding stage, limiting their effectiveness in handling these challenges. To address these limitations, we propose FreNet, a feature reconfiguration framework with visual priors, which performs pixel-level reconfiguration before encoding and feature-level reconfiguration during encoding for precise medical lesion segmentation. To suppress background responses, we propose an Implicit Prior Neural Network (IPNN), which models a continuous spatial field and leverages visual prior from SAM to reconfigure input image before encoding stage. To better handle diverse lesion morphology, we design a Dual-domain Feature Reconfiguration (DFR) module to progressively reconfigure backbone features during encoding stage. Within DFR, the Frequency Decoupling Module (FDM) decouples backbone features in frequency domain to enhance foreground-background discriminability, while the Spatial Localization Module (SLM) spatially relocates and improving spatial stability after frequency decoupling. Extensive experiments on 9 medical image segmentation benchmarks across three imaging modalities demonstrate that FreNet significantly outperforms state-of-the-art (SOTA) methods. On the challenging ETIS dataset, our method achieves Dice improvements of 5.0% over SOTA method and 7.2% over SAM.

cs.AI↗

arXiv2026

TokenComSR: Task-Sensitivity-Guided Token Communication for Wireless Image Super-Resolution

For resource-constrained wireless edge devices over bandwidth-limited fading channels, wireless image transmission using traditional separate coding suffers from the cliff-effect collapse. Prevailing deep joint source-channel coding (JSCC) based on convolutional neural networks can mitigate this issue but usually fail to preserve patch-level structures, thereby preventing adaptive per-token power allocation and limiting token-domain compensation for super-resolution (SR). To address these challenges, we propose a token communication framework with SR (TokenComSR). Specifically, we conceive a task-sensitive power allocation (TSPA) module and a signal-to-noise ratio (SNR)-conditioned token refinement module (TRM). TSPA distills training estimates of task sensitivity into inference token power weights, while TRM estimates an SNR-conditioned residual to correct channel-induced distortion in the token domain before decoding. Building on TSPA and TRM, the proposed TokenComSR pairs a Swin Transformer-based token transceiver with a receiver-side SR module for resource-constrained wireless image transmission. Simulation results confirm the effectiveness of the proposed TSPA and TRM, demonstrating improvements over separate coding and JSCC-SR baselines in both reconstruction fidelity and perceptual quality.

cs.IT↗

arXiv2026

AirTF: Over-the-Air Token Fusion for Task-Oriented Multi-Modal Token Communications

In the Internet of Vehicles (IoV), transmitting high-dimensional multi-modal sensory data to edge servers for time-sensitive tasks faces severe spectrum bottlenecks. To address this, we propose a foundation model-driven over-the-air token fusion (AirTF) framework for task-oriented multi-modal token communications. Unlike existing schemes for segmentation that rely on convolutional neural networks (CNNs) with limited local receptive fields, AirTF leverages vision transformer (ViT) encoders to extract globally contextualized semantic tokens from distributed heterogeneous sensors. By concurrently transmitting these spatially aligned tokens over a shared wireless channel, our framework exploits the superposition property of the multiple access channel to inherently fuse complementary multi-modal semantics (e.g., RGB and infrared) directly over the air. This mechanism significantly enhances spectral efficiency compared to orthogonal transmission. Furthermore, the integration of a pre-trained foundation model provides critical visual priors, effectively addressing the data-hungry nature of ViTs on limited, scenario-specific semantic segmentation datasets. Experiments demonstrate that AirTF consistently outperforms orthogonal transmission and CNN-based fusion baselines across AWGN and fading channels. Additional evaluations under a three-user setting, residual synchronization errors, and imperfect channel state information estimation further confirm its robustness. The source code will be made publicly available upon acceptance.

eess.IV↗

arXiv2026

Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation

Token Communications (TokenCom) has recently emerged as a new paradigm in which tokens serve as unified units for communication and computation, enabling efficient multimodal semantic and goal-oriented transmission. In this paper, we develop Ada-TokenCom, a rate-adaptive TokenCom framework based on large autoregressive models, which integrates next-token prediction with arithmetic coding to achieve ultra-low bitrate semantic communication at the token level. We propose a mixed reconstruction/generation scheme, where the transmitter encodes and transmits the highly informative tokens at the beginning of the token sequence leveraging a pre-trained autoregressive large model, while the receiver uses an identical model to predict the rest. Moreover, we design a Lyapunov-based algorithm to dynamically optimize both the source compression rate and the modulation and coding scheme, adapting to time-varying network conditions. Simulation results demonstrate that our proposed Ada-TokenCom framework outperforms both digital and deep joint source-channel coding-based semantic communication baselines.

cs.IT↗

arXiv2026

Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks

To achieve sustainable intelligent mobility, 6G-empowered robotic vehicles (RVs) require high-fidelity visual perception under stringent bandwidth and energy constraints. Semantic communication offers a spectral-efficient solution but suffers from severe interference in uplink non-orthogonal multiple access (NOMA) RV networks. To address this, we propose a knowledge distillation-driven and generative models-enhanced NOMA framework for robust and green RV communications, named KDG-SemNOMA. First, we develop a ConvNeXt-based deep joint source-channel coding (DeepJSCC) architecture with an enhanced attention feature (AF) module for dynamic channel adaptation. Second, to mitigate interference without inference overhead, an orthogonal transmission teacher model guides the NOMA student model via a two-stage knowledge distillation strategy. Finally, to address the over-smoothing artifacts of pixel-wise optimization, we introduce a channel-conditional GAN (cGAN). By explicitly taking the Stage-I initial reconstruction and channel states as conditional inputs, this module refines coarse outputs into high-fidelity images with realistic textures. Experiments on FFHQ-256 demonstrate that KDG-SemNOMA significantly outperforms state-of-the-art methods in both pixel-level accuracy and perceptual fidelity.

cs.IT↗