arXiv subjects

Xiaodong Xu

Publications and source records attributed to Xiaodong Xu.

At least 19 recordsLinked to original sources

arXiv2026

Revealing quantum geometry effects in magic angle twisted bilayer graphene using the circular photogalvanic effect

Magic-angle twisted bilayer graphene (MATBG) hosts flat bands whose correlated and topological phases depend sensitively on the electronic point-group symmetry, which is set by the device configuration including twist angle, heterostrain, and substrate and cap-layer coupling. Here we report near-infrared photocurrent measurements of a MATBG device encapsulated by an hBN substrate and a WSe$_2$ cap layer, at a twist angle of 1.05°. Using spatially resolved photocurrent imaging with polarization analysis, we disentangle the photothermoelectric response from the circular photogalvanic effect (CPGE) across all fillings of the moiré flat bands. A finite CPGE at normal incidence, forbidden in high-symmetry structures, directly constrains the point-group symmetry of MATBG and reveals a Berry curvature dipole intrinsic to the MATBG flat bands, activated by the symmetry lowering from heterostrain and the asymmetric hBN/WSe$_2$ environment. Our results establish nonlinear photocurrent as an all-optical probe of symmetry and quantum geometry in proximity-engineered moiré materials.

cond-mat.str-el↗

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

Multi-Stream Spatiotemporal Channel Coding for MIMO Systems: Transmission Scheme Design and Achievable Rate Optimization

Spatiotemporal channel coding (STCC) can improve the achievable rate over traditional temporal channel coding (TCC) by leveraging spatial degrees of freedom to extend the codeword length. Although several information-theoretic foundations on STCC have been established, the investigation of transmission schemes from a communication-theoretic perspective remains in its early stages. This paper proposes a multi-stream over multi-subchannel STCC (STCC-MSC) under full channel state information assumption and optimizes its achievable rate in the finite blocklength regime. We first formulate the transmission architecture of STCC-MSC in a point-to-point MIMO system, which introduces a stream-subchannel matching mechanism. We then maximize the achievable rate of STCC-MSC by jointly optimizing the subchannel assignment and power allocation strategies, which is formulated as a mixed-integer-nonlinear-programming problem. Next, a penalized alternating convex approximation (PACA) algorithm is proposed to solve this problem. Subsequently, we extend the point-to-point STCC-MSC designs to the more general multi-user MIMO systems, including both uplink and downlink scenarios. Finally, simulation results indicate that the PACA algorithm achieves a 9.85% rate improvement over the benchmark algorithm within the STCC-MSC scheme. Furthermore, the joint STCC-MSC-PACA scheme improves the achievable rate by 28.68% over TCC scheme.

eess.SP↗

arXiv2026

Type I Solar Radio Bursts Modulated by Solar Flares

Type I solar radio bursts (noise storms) are persistent meter-wave nonthermal emissions above active regions, with their occurrence and proper?ties closely related to the local magnetic configuration and nonthermal electron acceleration. This study examines a type I noise storm on 24 December 2023 and its relation to flare activity. The noise-storm source was co-spatial with active region AR 3529 and showed frequency-dependent spatial dispersion. The associated M2.9 flare strongly modulated the emission, with the storm intensity decreasing at flare onset, recovering afterward, and shifting to higher frequencies. Based on multiwavelength observations, we suggest that pre-flare small-scale reconnection supplied nonthermal electrons to overlying closed magnetic struc?tures and maintained the storm. During the flare, magnetic reconnection above the active region produced bidirectional plasma ejections and type III bursts with bidirectional frequency drifts; the gradually decreasing starting frequency of these bursts may indicate an upward-moving reconnection site. The resulting magnetic reconfiguration disrupted electron trapping and suppressed the storm, whereas post-flare magnetic recovery allowed the emission to resume. These results show that flares can modulate type I noise storms through magnetic restructuring and provide insight into the generation mechanism of noise storms.

astro-ph.SR↗

arXiv2026

Candidate for a Fractional Topological Insulator in Twisted MoTe2

The interplay among electronic correlation, topology, and time-reversal-symmetry (TRS) often leads to exotic quantum states of matter, as highlighted by the discoveries of fractional Chern insulators (FCIs) in twisted bilayer MoTe2 (tMoTe2). Among the FCIs in tMoTe2, the most robust is at a hole filling factor of v=-2/3 per moiré unit cell. Here, employing pump-probe circular dichroism (CD) measurement on tMoTe2 at twist angles (3.9 and 3.7 degrees), we show that a correlated state at v =-4/3 exhibits an unusual Ising antiferromagnet behavior. The v =-4/3 state with no net magnetization undergoes first order phase transitions at extremely low magnetic fields of ~ 2-6 mT to partially valley polarized (PVP) states. This behavior is notably absent for all other correlated states in tMoTe2 and also disappears for v =-4/3 at higher or lower twist angles (4.0 or 3.3 degree). The observed magnetic signature is consistent with a theoretically proposed fractional topological insulator (FTI), consisting of two copies of v =-2/3 FCIs with opposite chirality in the two K valleys. The experimental results are supported by interacting continuum model calculations that reveal the extreme closeness in energy ( < 1 meV) between the putative FTI and PVP states. Our findings present a candidate FTI with TRS and call for advanced transport and imaging measurements to establish the quantized helical edge modes.

cond-mat.str-el↗

arXiv2026

Large scale theoretical investigation of the phase diagram of twisted bilayer MoTe$_2$ at fractional fillings: agreements and contradictions with current experiments

We present a comprehensive exact-diagonalization study of interaction-driven phases in twisted bilayer MoTe$_2$ across experimentally relevant twist angles ($2.13^\circ$--$4^\circ$) and hole fillings. Using continuum-model moiré bands, we compare the one-band-per-valley (1BPV) projection with a two-band-per-valley (2BPV) calculation that includes interaction-driven band mixing, and we benchmark both the widely used first-harmonic continuum model and a parameter-free DFT ``fitting-free'' model. At odd-denominator fillings, the 2BPV calculation reproduces the experimentally observed hierarchy of fractional Chern insulators (FCIs) around $θ\approx 3.7^\circ$, including robust incompressible states at $ν=-2/3$, $-3/5$, and $-4/7$ while correctly finding the absence of an FCI at $ν=-3/7$, and it favors a charge density wave ground state at $ν=-1/3$ over the FCI. At half filling $ν=-1/2$, the 1BPV calculation exhibits clear composite Fermi liquid (CFL) signatures, whereas the band mixing in 2BPV calculations destabilizes the CFL ground state. Finally, motivated by the Landau-level analogy at $θ\approx 2.13^\circ$, we test the proposed non-abelian Pfaffian state at $ν=-3/2$ in the fully-polarized spin sector but find no evidence for this state within the models and parameters studied. Our results establish a unified numerical benchmark for correlated and topological phases in twisted bilayer MoTe$_2$ and clarify where multi-band physics is essential for a quantitative comparison with experiments.

cond-mat.str-el↗

arXiv2026

Towards Semantic Internet of Everything in the Age of Agentic AI

Semantic communication improves task effectiveness by transmitting task-relevant information. However, most existing schemes remain organized as task-specific, end-to-end pipelines, which are difficult to reuse across models, applications, and deployment environments. Against this background, we propose the Semantic Internet of Everything (SIoE), a composable service architecture that represents heterogeneous communication and artificial intelligence (AI) functions as capability-profiled services and coordinates them according to application objectives. SIoE comprises three planes: a task and service plane, an agentic orchestration plane, and a semantic capability plane. In this framework, task requirements are captured via a semantic service-level agreement (SLA), while an agentic planner discovers and composes candidate capabilities under deterministic compatibility, resource, privacy, and policy validation. Feedback from the communication, semantic, and task levels enables continuous adaptation and replanning. A lightweight vehicle-to-everything case study illustrates profile-grounded capability planning under explicit service constraints. The results demonstrate the feasibility of decoupling service objectives from fixed communication implementations and also highlight key open challenges, including semantic SLA design, capability interoperability, scalable planning, and trustworthy execution.

eess.SP↗

arXiv2026

Nonequilibrium dynamics of doped Chern ferromagnets: a case study for false vacuum decay

Even though metastable false vacuum decay is ubiquitous in physics, its underlying dynamics are still not well understood. Dissipative state preparation in moiré quantum materials provides an exceptional setting for exploring this physics since it allows the possibility of generating exotic quantum states that are not the ground state of the system Hamiltonian. Motivated by recent experiments demonstrating steady-state optical orientation of the spin-valley degree of freedom of holes, here we investigate dynamics of itinerant and Chern ferromagnets in the presence of an opposing magnetic field. Optical pumping using a circularly polarized Laguerre-Gauss beam allows us to deterministically prepare a true vacuum bubble embedded inside a metastable state. Depending on its initial size controlled by the pump power, we observe that the bubble collapses or expands due to an interplay between domain wall and bulk dynamics. For external magnetic fields comparable to the coercive field of ferromagnetism, we observe up to two-orders-of-magnitude prolongation of the spin polarization decay time at commensurate fillings corresponding to integer and fractional Chern insulator states. Our experiments reveal that the nonequilibrium dynamics of the ferromagnetic domains is substantially more sensitive to the precise filling factor around Chern insulator states than standard transport or optical measurements.

cond-mat.str-el↗

arXiv2026

Adaptive Source-Channel Coding for Bi-static Integrated Sensing and Semantic Communications

Semantic communication (SemCom) has emerged as a new paradigm to facilitate the performance of integrated sensing and communication systems in 6G, due to its potential to enhance transmission efficiency by transmitting task-relevant semantic features rather than raw bits. However, most of the existing works mainly focus on sensing data compression to reduce the subsequent communication overheads, without considering the integrated transmission framework for both the SemCom and sensing tasks. This paper proposes a sensing-aware adaptive source-channel coding (SA-ASCC) and beamforming design framework for bi-static integrated sensing and SemCom (ISSC) systems by jointly optimizing the coding rate for SemCom task and the transmit beamforming for both the SemCom and sensing tasks. Specifically, an end-to-end semantic distortion function is approximated by deriving an upper bound composing of source and channel coding induced components, and then a hybrid Cramér-Rao bound (HCRB) is derived for target position under imperfect time synchronization due to the transceiver deployed at different places in our considered bi-static ISSC system. To characterize the achievable region between SemCom and sensing performance, a distortion minimization problem is formulated by considering the HCRB threshold, channel uses, and power budget, which is non-convex due to the coupled design variables and the mixed-integer program. Subsequently, an alternating optimization (AO) algorithm is proposed to decompose this problem into the model selection and joint rate and beamforming optimization subproblems, which are solved by the exhaustive search method and the combination of successive convex approximation and fractional programming, respectively. Finally, simulation results demonstrate that the proposed scheme outperforms the DJSCC-WF-ZF and BPG-WF-ZF benchmarks.

eess.SP↗

arXiv2026

Observation of metastable chiral domain walls in a topological magnet

The interplay between topology and correlation can give rise to exotic collective excitations. The integer and fractional quantum anomalous Hall (QAH) magnets recently discovered in two-dimensional (2D) flatband systems are predicted to host spin excitations distinct from those in conventional magnets. Experimentally, nevertheless, these new excitations remain largely unexplored. Here we investigate spin-valley excitations in a twisted MoTe2 moiré superlattice using resonant ultrafast pump-probe spectroscopy. We observe a metastable spin-valley excitation in the QAH magnet below T ~ 3.7 K that survives reverse magnetic field several times larger than the saturation field. The behavior of this excitation is sharply distinct from ordinary domain walls and magnons, indicating a new type of spin-valley textures unique to topological magnets. We propose that these textures are chiral domain walls with an in-plane winding of the pseudospin order parameter along the domain wall. Their metastability arises from the interplay between the topological winding in real space and the quantum geometry of the parent bands in momentum space through a universal mechanism. These chiral domain walls govern the nonequilibrium dynamics of QAH magnets and may play a central role in their stability. Our study highlights intrinsic quantum geometry effects on spin excitations in topological magnets; and provides key insights into the fundamental mechanism limiting stability of topological protection.

cond-mat.mes-hall↗

arXiv2026

Restoration Flow Matching-Based Channel Refinement and Equalization Correction for MIMO Semantic Communications

In multiple-input multiple-output (MIMO) semantic communication, imperfect channel state information (CSI) and equalization mismatch can seriously degrade semantic reconstruction quality. To address this issue, we propose a unified restoration flow matching (RFM)-based framework for channel refinement and equalization correction. Specifically, the channel RFM (CRFM) module is developed to refine the coarse channel, thereby improving channel estimation accuracy. Based on the refined channel, the developed semantic RFM (SRFM) module is employed to correct the residual distortions in the post-equalization latent space. The key idea is to formulate the two cascaded inverse problems of channel estimation and equalization as the unified conditional restoration task, in which the learned conditional velocity field guides the perturbed distribution towards the target distribution. To enhance the robustness of these two modules under various distortion conditions, we develop a dual-anchor perturbation training strategy that jointly learns near-manifold refinement and large-error correction, and implement inference through a few-step deterministic ordinary differential equation (ODE) solver. Extensive experiments on MIMO channels and visual semantic transmission tasks demonstrate that the proposed scheme improves key metrics for channel estimation and semantic reconstruction quality. Moreover, compared with representative diffusion-based generative baselines, the proposed method requires fewer sampling steps.

cs.LG↗

arXiv2026

RIXS Identification of Optical Phonon-Spin Coupling Effects in CrSBr

Resonant inelastic x-ray scattering provides experimental signatures of spin-phonon coupling in CrSBr through temperature-dependent Cr $L$-edge spectra. Low-energy excitations are observed exclusively in the low-temperature antiferromagnetic phase as energy-loss features. A quasi-elastic peak at approximately 42 meV is observed under $π$-polarization. Density functional theory phonon-mode calculations identify these RIXS features as occurring within the same energy range as bond-bending optical phonon modes associated with distortions of the Cr--S--Cr network. The pronounced suppression of these low-energy excitations upon warming into the paramagnetic phase, together with their polarization dependence, the calculated phonon spectrum, and a spin-renormalized electron-phonon RIXS framework, indicates a strong interplay between magnetic correlations and lattice dynamics. While the loss features appear at energies characteristic of optical phonons, the significant overlap of the optical-phonon and magnon bands suggests that the temperature-dependent behavior should not be regarded as purely lattice-derived excitations. Instead, the room-temperature suppression of the low-energy RIXS peaks is explained in terms of a spin-phonon coupling effect on the $L$-edge electron-phonon RIXS mechanism. The interpretation is supported by the combined experimental observations, phonon calculations, and theoretical modeling, rather than by temperature contrast alone. These results support spin-phonon coupling as a plausible and consistent interpretation of the observed temperature-dependent RIXS response and demonstrate that magnetic order can strongly influence phonon-related spectral weight in the RIXS spectrum.

cond-mat.mtrl-sci↗

arXiv2026

SemDPLA: Semantic Communication-based Distributed Physical-Layer Authentication for 6G-enabled Dense IoT

With the rapid development of 6G, increasingly dense device connectivity imposes more strict requirements on multi-users Physical-Layer Authentication (PLA). Compared with cryptography-based methods, PLA enables lightweight authentication by using the uniqueness of wireless channels. However, existing PLA schemes in dense wireless scenarios often suffer from weak fingerprint discriminability and limited computation and communication resources. To address these challenges, we propose a Semantic Communication-based Distributed PLA (SemDPLA) framework. The framework constructs fused central semantic Channel State Information (CSI) fingerprints by fusing semantic information from a central node and multiple distributed nodes. Specifically, we introduce semantic communication to reduce the impact of low Signal Noise Ratio (SNR) and the consumption of communication resource during the transmission from distributed nodes to central node. Furthermore, we propose an ArcFace-based classification method and a semantic fingerprint-oriented distributed voting consistency mechanism to enhance device classification accuracy. Simulation results demonstrate that the proposed SemDPLA scheme performs better than single-node authentication, decision fusion, raw-CSI transmission, and feature fusion baselines. It achieves equal error rates (EERs) of 8.6% at 0 dB and 3.4% at 20 dB. It also achieves classification accuracies of 91.4% at 0 dB and above 95.8% from 5 to 20 dB. Moreover, SemDPLA is robust in low SNR environment and under attacks of abnormal nodes.

eess.SP↗

arXiv2026

-8 dB SNR + 90% Packet Loss: MamVSC -- CSI-Guided Semantic Mamba for Extreme-Robust Video Semantic Communication

Semantic communication, leveraging joint source-channel coding, is designed to mitigate semantic distortion introduced by the channel. However, most current studies focus solely on semantic deviation distortion caused by physical wireless channels, while overlooking semantic erasure distortion due to packet loss. A CSI-Guided Mamba-based video semantic wireless digital communication system (MamVSC) employing semantic grouping is proposed to simultaneously address both semantic deviation and erasure distortions. In this system, a semantic Mamba module, guided by channel state information (CSI) feedback, is utilized to dynamically adjust the granularity of extracted semantic information, adapting to channel conditions. Furthermore, a Semantic Channel Codec based on dynamic Semantic clustering centers is introduced, where the distance between semantic vectors within the same semantic class and their corresponding Semantic clustering center is dynamically adjusted according to channel conditions, enhancing robustness against channel noise. Additionally, a adaptive packet loss recovery module, dynamically adaptive to the CSI, is proposed. The system achieves an MS-SSIM greater than 0.6 and a PSNR exceeding 21 dB at an SNR of -8 dB and a packet loss rate of 90% in AWGN channel.

cs.ET↗

arXiv2026

LLM-Empowered Multimodal Fusion Framework for Autonomous Driving: Semantic Enhancement and Channel-Adaptive Design

Vision-radar fusion is central to robust autonomous driving, combining dense visual semantics with precise range and velocity measurements from radar. However, real-world fusion quality is fundamentally challenged by dynamically varying input quality, stemming from occlusion, adverse weather, and channel noise. To address this, we re-frame the problem from static data fusion to channel-aware semantic reasoning and propose a Large Language Model-centric Semantic-layer Channel-aware Integrated Perception (LM-SCIP) framework. It places a Large Language Model (LLM) as a central reasoning core to fuse a local visual stream with a quality-varying external radar stream used to cover perception-blind spots. Concretely, LM-SCIP couples a hierarchical radar-vision encoder with a Channel-Adaptive Semantic Module (CASM) that maps link indicators into a "Channel Prompt" to dynamically gate external radar features. A parameter-efficient, LoRA-tuned LLM, in conjunction with a heterogeneous Mixture-of-Experts (H-MoE), then arbitrates between local visual cues and the channel-conditioned radar context. Finally, a decoupled multi-task decoder outputs localization, trajectory forecasting, and image reconstruction. Experiments on nuScenes and VIRAT validate our approach. On nuScenes, under a controlled toggle of radar input, LM-SCIP reduces localization RMSE by 40.0% versus a vision-only baseline. On VIRAT, the model attains a 0.214m localization RMSE and 0.179m minFDE (k=1). These results reveal that the proposed LM-SCIP enables a robust vision-dominant fallback at low SNR and synergistic fusion at high SNR.

cs.CV↗

arXiv2026

Evolving Intelligent Complex Systems via Intellicise Networks: Architecture, Technologies, and Pathways

Future engineering infrastructures are evolving into large-scale, open, heterogeneous, and wirelessly interconnected complex systems. These systems present significant challenges in optimizing network resource utilization, managing high-dimensional information spaces, and accommodating diverse business requirements. Intellicise networks, characterized by Intent-driven operation, semantic-native capability, and distributed intelligence, offer a promising paradigm for enabling such intelligent complex systems. We provide a systematic exploration of future intelligent complex systems from the perspective of intellicise networks. Specifically, we propose a cross-domain intelligent communication network architecture based on intellicise networks, grounded in information theory, systems theory, game theory, and cybernetics. The architecture comprises a cross-layer organizational framework, multi-functional planes, and novel information flows. The cross-layer framework defines the vertical evolution from perception and cognition to decision, while the control, user, data, computation, intelligence, and security planes deliver horizontal intellicise capabilities. Moreover, data, knowledge, model, and task flows interconnect the various layers and planes, forming a closed-loop process that derives simplicity from high-level intelligene while concurrently pursuing enhanced. Building on this architecture, we review key enabling technologies, tracing their evolution from semantic extraction to intent understanding, from heterogeneous resource integration to self-configuration and self-optimization, from generative artificial intelligence (AI) to agentic AI, and from embodied AI to symbodied AI. Additionally, we present a case study on intellicise networks for embodied agent communications and discuss representative applications and services for intelligent complex systems.

eess.SP↗

arXiv2026

Semantic-based Internet of Embodied Intelligence: Visions and Frontiers

Recent advances in generative artificial intelligence (AI) and embodied intelligence (EI) enable autonomous agents to interact with the physical world. However, scaling these systems into networks of multiple agents, namely the Internet of EI (IoEI), faces critical bottlenecks. These include the overhead of massive multimodal data transmission and the decoupling of logical reasoning from physical constraints. To address these challenges, we envision the Semantic-based IoEI (SIoEI), which leverages semantic information as a unified metric throughout the agent lifecycle. We systematically define four key dimensions of EI: perception, intelligence, control, and communication. We further elaborate how semantic empowerment revolutionizes environmental perception, cognition and task planning, action generation and robust control, and communication and networking. We also present a case study to verify that, the semantic-empowered end-to-end process significantly improves channel robustness and reduces end-to-end latency for EI. Finally, we outline critical open research directions for the SIoEI paradigm.

eess.SP↗

arXiv2026

Effective Depth in Joint Source-Channel Coding: An Implicit Equilibrium Analysis

A fundamental design question in deep joint source-channel coding (Deep JSCC) remains insufficiently explored: given a channel signal-to-noise ratio (SNR), what effective computation depth is required for semantic reconstruction? Existing Deep JSCC systems typically employ fixed-depth neural architectures selected through empirical hyperparameter tuning, which may lead to unnecessary computation under favorable channel conditions and insufficient refinement under severe channel noise. This paper proposes \emph{Implicit-JSCC}, an implicit equilibrium framework in which semantic encoding and decoding are formulated as fixed-point equilibrium processes. The effective encoder and decoder depths are determined by residual-based solver convergence rather than manually predefined layer numbers, while parameter sharing across equilibrium iterations enables depth-independent parameter complexity. To analyze the resulting effective-depth behavior, we develop a Gaussian-process-inspired kernel evolution framework that models equilibrium iterations as an effective-depth propagation process. Since channel noise is injected between the encoder and decoder, the analysis tracks channel-induced representation perturbations across receiver-side equilibrium iterations and derives a theory-guided depth--SNR relationship. After offline calibration of the system-specific parameters, the resulting model characterizes the required receiver-side refinement depth under different SNRs. Extensive experiments show that Implicit-JSCC achieves competitive reconstruction performance while enabling residual-based adaptive inference and controllable computation--quality tradeoffs. The depth--SNR model further provides a characterization of the SNR-dependent refinement depth required to reach a prescribed perturbation tolerance.

eess.SP↗