arXiv subjects

Yuxuan Shi

Publications and source records attributed to Yuxuan Shi.

At least 19 recordsLinked to original sources

arXiv2026

APPSim-Bench: Bridging Real-world Apps and Reproducible Evaluation for Mobile GUI Agents

Mobile GUI agents can execute tasks from natural-language instructions, but their evaluation remains difficult to make both realistic and reproducible. Existing benchmarks typically trade off these goals: simplified apps lack real-world mobile complexity, whereas live commercial apps introduce uncontrolled variation from recommendations, advertisements, accounts, and changing content. We propose AppSim-Bench, which addresses this trade-off through controllable simulated apps that preserve task-relevant interaction logic while supporting deterministic evaluation. Built through a coding-agent-assisted and human-verified workflow, it contains 557 tasks across 17 high-frequency Chinese and English apps. Its controllable backend data and outcome-based verification remove major sources of environmental stochasticity, enabling reproducible cross-model comparison. Evaluating 19 GUI agents, spanning general-purpose and GUI-specialized systems, we find that autonomous mobile execution remains far from solved. The best model completes only 50.27% of tasks, and 28.55% of tasks are not solved by any agent. Further analysis shows that failures concentrate in longer workflows, numerical reasoning tasks, and inefficient trajectories marked by high action overhead and budget exhaustion. Our project is available at https://github.com/Acrab-Agentic-Labs/AppSim.

cs.AI↗

arXiv2026

On the Gaussian-Quadratic Rate-Distortion Function for Vector Sources with Individual Distortion Constraints

This paper investigates the Gaussian-quadratic lossy compression with arbitrary source length under individual distortion constraints. The rate-distortion function (RDF) is lower-bounded by a Hadamard inequality-based rate, which is tight if and only if the semidefinite condition (SDC) holds. Otherwise, this bound becomes loose, and analytical results are lacking. Moreover, the fundamental quantitative relationship between source correlations and the RDF remains incomplete. In this paper, we provide new theoretical results under different source covariance matrices and distortion constraints. First, under arbitrary covariance and distortion constraints, we obtain the spectral properties of the optimal source reconstruction achieving the RDF, and a stronger scalar inequality version of the SDC. We propose a class of source covariance matrices based on hierarchical correlations and show that studying the two-type correlation (2-TC) model is sufficient to establish the analytical foundation for the broader class. Under this covariance, we obtain the RDF with source correlations explicitly incorporated when the SDC holds, and analyze the SDC from the perspectives of distortion constraints and source correlations. Next, under the 2-TC covariance and two-type distortion (2-TD) constraints, we establish the complete RDFs over seven regions on a distortion plane, with the optimal distortion (rate) allocations determined in each region. It is revealed that the essence of pursuing the complete RDF lies in thoroughly analyzing the correlations between the optimal distortions. Finally, under isotropic correlation and identical constraints, we provide the per-component compression rate and show that exploiting correlations can significantly reduce compression costs.

cs.IT↗

arXiv2026

ConsisFormer: Compute-Efficient Transformer for Wireless Foundation Models Based on Channel Consistency

Wireless foundation models (WFMs) have recently emerged as a promising paradigm for AI-native 6G networks, enabling universal channel representations adaptable to diverse communication and sensing tasks. Existing WFMs are predominantly built upon the Transformer architecture, which delivers superior performance but incurs computational complexity proportional to the square of the input sequence length, posing a significant barrier to their deployment under stringent inference latency constraints. To address this issue, in this paper, we propose ConsisFormer, a compute-efficient Transformer design based on short-term consistency of wireless channels, as a WFM backbone. By utilizing the observation that adjacent time or frequency instances share similar clusters of scatterers and thus exhibit similar channel characteristics, we develop an adaptive token aggregation (ATA) module to dynamically merge neighboring channel state information (CSI) tokens, thereby reducing the length of the token sequence involved in self-attention calculations to lower the computational cost. Furthermore, we propose a feature sequence interpolation (FSI) method to recover the full CSI representation based on the sparse feature sequence outputted from the Transformer blocks, thus keeping the performance unaffected while ensuring low complexity. Moreover, we propose an aggregated auto-encoder (AAE) pre-training paradigm for WFMs, enabling robust channel representation learning from sparsified CSI tokens via compression and recovery. Simulation results show that the proposed design reduces the computational complexity of WFM by over $83\%$ with negligible performance loss on various tasks including channel prediction, LoS/NLOS classification, beam prediction, and localization.

eess.SP↗

arXiv2026

Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models

Mobile systems increasingly rely on heterogeneous learning-enabled wireless functions, for which separate taskspecific models incur redundant training and model-management overhead. Wireless foundation models (WFMs) enable these functions to share a pretrained backbone, but existing adaptation either updates the backbone per task or relies on an inflexible final-layer representation. We observe that intermediate WFM layers exhibit distinct depth-dependent correlation structures. Based on this observation, we propose a Routing Adapter for Feature Composition (RAFC), which summarizes selected hidden states into compact descriptors, generates task- and sampledependent routing weights, and combines the original fullresolution features without updating the pretrained backbone. Its gains arise from giving each task adaptive access to complementary representations across multiple depths rather than restricting it to the final layer. Experiments across four task categories and three WFM backbones show improvements in every evaluated backbone-task pair, with mean relative gains across the four task categories ranging from 2.9% to 35.9%, while RAFC adds only 0.003M-0.049M trainable parameters across the evaluated backbones. Experiments on a portable softwaredefined radio testbed further demonstrate improved end-to-end receiver robustness, particularly under partial-band co-channel interference. These results establish RAFC as a lightweight, model-agnostic, and interpretable adaptation interface for WFMs in mobile systems.

cs.LG↗

arXiv2026

SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Prediction

This paper proposes SpikeWFM, a novel hybrid architecture that integrates spiking neural networks (SNNs) with conventional artificial neural network (ANN)-based transformers for wireless foundation models (WFMs). Inspired by the noise-robust and energy-efficient information processing in the human brain, SpikeWFM aims to enhance the resilience of WFMs against noise and interference while maintaining strong generalization capabilities across diverse wireless scenarios. Drawing from the success of large language models, WFMs leverage self-supervised pre-training on large-scale datasets spanning various wireless environments to learn a unified embedding that supports a wide range of downstream tasks, including channel prediction, channel estimation, beam predition, positioning and etc. Such models typically outperform task-specific designs and exhibit superior adaptability to unseen conditions. However, existing WFMs remain vulnerable to realistic noise and interference in practical wireless systems. To address this limitation, we incorporate spiking neurons into the transformer-based WFM architecture. We provide a brief theoretical analysis demonstrating how the SNN-ANN hybrid effectively mitigates noise and interference through temporal sparsity and event-driven processing. Experimental results show that SpikeWFM consistently outperforms conventional ANN-based WFMs in both pre-training convergence and channel prediction accuracy. Additional results on communication and sensing tasks will be presented in the full journal version of this work.

eess.SP↗

arXiv2026

Contextual Wireless Video Semantic Communication in MIMO-OFDM Systems

This paper proposes a MIMO-OFDM-based context video semantic transmission framework, namely M-CVST, for robust video communication over multi-path multiple-input multiple-output (MIMO) channels. It introduces a context-subcarrier correlation map that aligns video feature context with groups of MIMO subcarriers. To leverage the time-correlated nature of multi-path channels, a recursive subcarrier sampling method paired with time-correlated reference embedding is designed, enabling the use of previously sampled MIMO subcarrier CSI to enhance channel state awareness in the entropy coding model. Numerical results verify the superiority of proposed M-CVST over MIMO multi-path channels compared to other semantic schemes and traditional separated schemes.

cs.MM↗

arXiv2026

SpecFed: Accelerating Federated LLM Inference with Speculative Decoding and Compressed Transmission

Federated inference enhances LLM performance in edge computing through weighted averaging of distributed model predictions. However, autoregressive LLM inference requires frequent full-model forward passes across workers, severely limiting decoding throughput. Distributed deployment further aggravates this due to a communication bottleneck: each worker must transmit full token probability distributions per draft token, dominating end-to-end latency. To address these challenges, we introduce speculative decoding to enable parallel LLM processing and propose a top-K compressed transmission scheme with two server-side reconstruction strategies. We theoretically analyze the robustness of our method in terms of local reconstruction error, aggregation bias, and acceptance-rate bias, and derive corresponding bounds. Experiments demonstrate that our scheme achieves high generation fidelity while significantly reducing communication overhead.

eess.SP↗

arXiv2026

Joint Lossy Compression for a Vector Gaussian Source under Individual Distortion Criteria

This paper investigates the joint compression problem of a vector Gaussian source, where an individual distortion constraint is imposed on each source component. It is known that the rate-distortion function (RDF) is lower-bounded by the rate derived from the Hadamard inequality, which becomes exact when the semidefinite condition (SDC) holds. However, existing works often overlook the case where the SDC is not satisfied. Moreover, even when the SDC holds, a quantitative characterization of how correlations enable more efficient compression is lacking. In this work, we refine the results when the SDC is satisfied and derive new theoretical results when the SDC is not satisfied, thereby establishing theoretical limits for practical source compression with correlations. Specifically, we examine the properties of optimal source reconstruction and provide upper bounds on its dimension, showing that lower-dimensional reconstructions are essential for efficient compression when the SDC does not hold. Within a scalable two-type correlation (2TC) covariance framework, we prove that the probability of satisfying the SDC decays exponentially with source length, emphasizing the importance of exploring theoretical limits when the SDC is not met. Additional, we determine the component-wise correlations that a vector source should possess to achieve the Hadamard compression rate, revealing the trade-off between distortion constraints and correlations. More importantly, by deriving an explicit RDF with correlations incorporated, we quantitatively characterize the gain in compression efficiency achieved by fully leveraging source correlations.

cs.IT↗

arXiv2026

FDD CSI Feedback under Finite Downlink Training: A Rate-Distortion Perspective

This paper establishes the theoretical limits of channel state information (CSI) feedback in frequency-division duplexing (FDD) multi-antenna orthogonal frequency-division multiplexing (OFDM) systems under finite-length training with Gaussian pilots. The user employs minimum mean-squared error (MMSE) channel estimation followed by asymptotically optimal uplink feedback. Specifically, we derive a general rate-distortion function (RDF) of the overall CSI feedback system. We then provide both non-asymptotic bounds and asymptotic scaling for the RDF under arbitrary downlink signal-to-noise ratio (SNR) when the number of training symbols exceeds the antenna dimension. A key observation is that, with sufficient training, the overall RDF converges to the direct RDF corresponding to the case where the user has full access to the downlink CSI. More importantly, we demonstrate that even at a fixed downlink SNR, the convergence rate is inversely proportional to the training length. The simulation results show that our bounds are tight, and under very limited training, the deviation between the overall RDF and the direct RDF is substantial.

cs.IT↗

arXiv2025

Neutrino oscillations in a Kalb-Ramond black hole background

The analysis examines how neutrinos behave when their trajectories unfold around a black hole sourced by a Kalb-Ramond field, where spontaneous Lorentz symmetry breaking reshapes the surrounding geometry. Instead of following the conventional order, the study focuses first on the observable consequences: alterations in the neutrino-antineutrino annihilation energy output, shifts in the oscillation phase accumulated along the path, and distortions in flavor conversion probabilities induced by gravitational lensing. These features are then tied to the Lorentz-violating spacetime structure, which governs the propagation of the neutrinos. Numerical simulations are carried out for both two- and three-flavor descriptions, with normal and inverted mass orderings.

gr-qc↗

arXiv2025

Accretion of matter of a new bumblebee black hole

We investigate how the newly obtained static black hole in bumblebee gravity affects the behavior of accreting matter and its observable signatures. The Lorentz-violating parameter that characterizes this geometry modifies photon trajectories and shifts the location of the critical curve that defines the shadow. Using ray tracing, we examine light deflection, the structure of direct emission, lensing rings, and photon rings, and we explore three thin-disk emission models--starting at the ISCO, at the photon sphere, and at the event horizon--together with static and infalling spherical accretions. Larger values of this parameter enlarge the shadow, move all optical features outward, and suppress the observed intensity through gravitational redshift, with additional dimming produced by Doppler effects for infalling matter

gr-qc↗

arXiv2025

Neutrino oscillations induced by a new bumblebee black hole

This work investigates neutrino propagation in the spacetime of a newly introduced black hole arising from spontaneous Lorentz-symmetry breaking in bumblebee gravity. The analysis focuses on three independent components: the rate at which neutrino-antineutrino annihilation deposits energy in the surrounding region, the geometric contribution to the phase accumulated by neutrino mass eigenstates, and the modifications to flavor conversion produced by weak gravitational lensing. Working with a two-flavor system, both mass orderings are examined, and the calculation incorporates the interference between distinct trajectories reaching the detector. The numerical results show that the Lorentz-violating deformation substantially increases the efficiency of the annihilation channel, produces characteristic shifts in the oscillation phase not present in earlier bumblebee configurations, and reshapes the angular dependence of the lensing-induced flavor transition pattern.

gr-qc↗

arXiv2025

Wireless Video Semantic Communication with Decoupled Diffusion Multi-frame Compensation

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework with decoupled diffusion multi-frame compensation (DDMFC), abbreviated as WVSC-D, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC-D first encodes original video frames as semantic frames and then conducts video coding based on such compact representations, enabling the video coding in semantic level rather than pixel level. Moreover, to further reduce the communication overhead, a reference semantic frame is introduced to substitute motion vectors of each frame in common video coding methods. At the receiver, DDMFC is proposed to generate compensated current semantic frame by a two-stage conditional diffusion process. With both the reference frame transmission and DDMFC frame compensation, the bandwidth efficiency improves with satisfying video transmission performance. Experimental results verify the performance gain of WVSC-D over other DL-based methods e.g. DVSC about 1.8 dB in terms of PSNR.

cs.MM↗

arXiv2025

The Flight of the Bumblebee in a Non-Commutative Geometry: A New Black Hole Solution

This paper investigates a new black hole solution within the framework of bumblebee gravity, incorporating non-commutative corrections parameterized by $\Theta$ and implemented through the Moyal twist $\partial_r \wedge \partial_\theta$. Notably, the event horizon remains unaffected by $\Theta$, while the surface gravity becomes ill-defined, in agreement with the behavior previously reported for the non-commutative Schwarzschild black hole [1]. The propagation of light is examined by analyzing null geodesics, identifying critical orbits, and determining the resulting black hole shadow. To complement these analyses, we explore gravitational lensing by evaluating the deflection angle in both the weak- and strong-field regimes. Using these results, constraints are derived for the lensing observables by comparing with the Event Horizon Telescope data for $Sgr A^{*}$ and $M87^{*}$. Finally, we close the analysis by deriving additional constraints from standard Solar System experiments, including Mercury's orbital precession, gravitational light bending, and time-delay measurements.

gr-qc↗

arXiv2025

Filter-and-Attend: Wireless Channel Foundation Model with Noise-Plus-Interference Suppression Structure

Wireless channel foundation model (WCFM) is a task-agnostic AI model that is pre-trained to learn a universal channel representation for a wide range of communications and sensing tasks. While existing works on WCFM have demonstrated its great potentials in various downstream tasks, the models are all trained using perfect (i.e., error-free and complete) channel information state (CSI) data. In practical systems, however, only degraded CSI obtained from pilot-based channel estimation is accessible, leading to distorted channel representations and performance degradation in downstream tasks for some real-world environments with severe noise and interference. To address this issue, this paper proposes a new paradigm for WCFM, termed as Filter-and-Attend. In this paradigm, Filter refers to explicitly suppressing noise-plus-interference (NPI) in the received signals, while Attend means performing correlation-aware CSI completion and feature extraction using attention mechanism. Specifically, an enhanced WCFM architecture is developed. In this architecture, coarse estimates of the CSIs are first obtained and exploited to construct two projection matrices that extract NPI components in the received signals, which are further processed and removed by a subtraction module. The filtered signal is subsequently passed through a CSI completion network to get a clean CSI for feature extraction. Simulation results demonstrated that compared to the state-of-the-art solutions, WCFM with NPI suppression structure achieves improved performance on various downstream tasks including time-domain channel prediction, frequency-domain channel prediction, and localization.

eess.SP↗

arXiv2025

Probing Hernquist dark matter through the optical appearance of black holes: A comprehensive study of various accretions

The observational appearance of a black hole is critically dependent on the surrounding accreting matter, in particular on the central brightness depression and photon ring structure. We perform a systematic comparative analysis of the observational signatures of a Schwarzschild black hole embedded in a Hernquist dark matter (DM) halo under three distinct accretion scenarios: a geometrically thin disk, a static spherical flow, and an infalling spherical flow. For the thin disk model, we find that direct emission dominates the total observed intensity, while the size and brightness of the lensing and photon rings serve as sensitive probes of the Hernquist DM parameters. From a geometric perspective, the Hernquist DM halo significantly enlarges the photon sphere, resulting in an observable critical curve radius approximately $2\%$ to $30\%$ larger than in the vacuum case. Regarding the radiative signatures, the measured intensity profiles, which rely on the particular accretion models, show a general brightness suppression, which is especially affected by the Doppler de-boosting in the infalling scenario. Our results suggest that the size of the central brightness depression and the brightness profile of the black hole image provide a valuable theoretical framework for constraining the distribution of dark matter in galactic centers.

astro-ph.HE↗

arXiv2025

The Barrow entropies in the thermodynamics of high-dimensional Gauss-Bonnet black holes

We study the thermodynamics of $D$-dimensional Gauss-Bonnet black holes with Barrow entropy. It is found that the Gauss-Bonnet coupling and the Barrow factor revise the thermodynamic variables such as event horizon, Hawking temperature, entropy and heat capacity. It is interesting that the larger five-dimensional black holes exist stably and the smaller ones evaporate to disappear owing to the nature of heat capacities amended by the coupling and factor. The discussions exhibit that the $D$-dimensional black holes with $D=6, 7$ set free all of their energy to vanish because of the minus heat capacities as functions of Hawking temperature although the extra term and the fractal power bring about their revisions on the functions, but they cannot change the heat capacity signs, so they also cannot change the fate of six- or seven-dimensional black holes.

gr-qc↗

arXiv2025

Optical Phenomena in a Non-Commutative Kalb-Ramond Black Hole Spacetime

This work investigates additional gravitational features of a newly proposed black hole spacetime within Kalb-Ramond gravity, incorporating non-commutative corrections arising from a gauge-theoretic approach recently introduced in the literature [arXiv:2507.17390]. Accordingly, null geodesics are solved numerically to trace photon paths; the photon sphere and shadow are determined. From Event Horizon Telescope (EHT) measurements of $Sgr A^{*}$, constraints on the parameters $\Theta$ (which encapsulates the non-commutativity) and $\ell$ (the Lorentz-violating parameter) are established. To examine the stability of critical orbits and the deflection angle (gravitational lensing) in the weak field scenario, we compute the Gaussian curvature in order to use the Gauss-Bonnet theorem. Moreover, the deflection angle has been calculated as well in the strong deflection limit. Furthermore, Lensing observables are estimated using EHT data for $Sgr A^{*}$ and $M87$. Topological features such as the topological photon sphere are also explored.

gr-qc↗