arXiv subjects

Haopeng Wang

Publications and source records attributed to Haopeng Wang.

At least 19 recordsLinked to original sources

arXiv2026

Looking Around by Looking Around: Omnidirectional Gaze-based VR Viewport Control

Traditional VR viewport control primarily relies on head and torso movement, which can be effortful and limiting in both constrained and extended-use settings. We introduce Looking Around by Looking Around (LALA), a gaze-based VR pitch-and-yaw viewport control technique designed for natural and effortless omnidirectional exploration via eye movements, without requiring or obstructing movement of the head, hand, or body, offering a low-effort and highly accessible interaction method. Because gaze is primarily used for perception and exhibits oculomotor and perceptual asymmetries, using it directly for control is difficult. To address this, we designed an asymmetric omnidirectional control profile for the eye, then built on it to exploit tendencies for eyes to stay within comfortable regions for viewport control. We evaluated LALA in a user study (N=18) featuring two contrasting tasks: alignment towards known directions and open-ended visual search towards unknown directions. LALA was strongly preferred over the traditional baseline, achieving competitive performance while enabling fully hands-free interaction with minimal physical movement.

cs.HC↗

arXiv2026

VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method

Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions. However, route-level instructions commonly encode spatial priors, such as orientation, distance, and layout, that are not explicitly available from onboard sensing at deployment in open, GPS-denied environments. Benchmark performance under such interfaces therefore jointly reflects visual navigation ability and the use of route structure explicitly supplied by the task description. As a complementary formulation, we propose Vision-Only Long-Horizon Navigation (VoLN), which shifts route-relevant information from externally supplied instructions and global guidance to locally observable in-scene cues. In VoLN, goal views specify the destination, while route-relevant information is available only through locally observable in-scene cues that the agent must detect, interpret, and select online. We instantiate VoLN for aerial navigation through VoLN-UAV, a 7,210-episode benchmark that combines long-horizon goal-directed flight, continuous 3D motion, large viewpoint changes, and context-dependent beacon selection. We further provide VoLN-MLLM as an initial reference baseline. It aligns self-supervised visual features with a structured semantic space and predicts short-horizon waypoint segments from observation history, goal views, retrieved visual--semantic tokens, and proprioception. On the five-environment Test-Unseen split, it obtains success rates of 7.4%, 4.5%, and 1.8% on Easy, Normal, and Hard episodes, respectively. These results provide an initial evaluation of VoLN and reveal substantial remaining challenges in long-horizon evidence integration, cross-view goal matching, and closed-loop stability. Project page: https://admire-ljb.github.io/VoLN-UAV/

cs.RO↗

arXiv2026

AERIS: Aerial-Edge Role-Driven Intelligence at Runtime via Orchestrated Language-Model Swarm

Integrating large language models into robotic systems holds promise for enhancing autonomy, yet practical deployment remains constrained by strict heartbeat-constrained scheduling and limited computational power. We propose AERIS: an edge deployment framework for aerial platforms. It organizes dedicated small language models combined with lightweight perception and control modules into roles that can be instantiated at runtime, and dynamically rebinds them across different executors as resources change, thereby pushing intelligent capabilities to the edge. AERIS achieves long-horizon instruction decomposition through an attention-subgoal alignment mechanism, which involves annotating the currently active instruction step in messages, thereby progressively approaching long-term objectives. We evaluate AERIS on a high-fidelity UAV Vision-and-Language Navigation benchmark. Under a heartbeat-timed execution mechanism, AERIS maintains a stable perception-decision-control loop between a low-frequency planner and a high-frequency controller, supporting real-time closed-loop operation. We further validate its deployability through two real-world experiments focused on planning and fast response. A demonstration video is provided in the supplementary materials.

cs.RO↗

arXiv2026

Code Is More Than Text: Uncertainty Estimation for Code Generation

Large language models (LLMs) are increasingly deployed as code generators, where silently wrong programs pose real safety and reliability risks. Reliable uncertainty estimation (UE) is essential for selective prediction, human-in-the-loop review, and downstream agentic decisions. Yet most existing code UE methods are inherited from natural language (NL) generation and ignore properties that make code distinct. We argue that code differs from NL in three ways: a single wrong token can break an entire program (token fragility); algorithmic intent and concrete implementation can disagree independently (intent-code gap); and programs can be executed (executability). We instantiate these properties as three orthogonal uncertainty axes: lexical (Top-K token entropy), algorithmic (pseudo-code consistency), and functional (behavioral consistency). Across five code LLMs, our three-axis ensemble improves average AUROC from 0.696 for the strongest NL-derived baseline to 0.776 (+8.1 points). Notably, on Qwen3-14B, our single-pass Top-K token entropy matches the strongest multi-pass baseline while being over 3x cheaper; across models, it remains a competitive low-cost signal. These results suggest that code UE deserves code-specific design rather than direct NL ports.

cs.CL↗

arXiv2026

Toward Native Multimodal Modeling: A Roadmap

Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders and frozen language backbones with output heads, recent efforts have shifted the paradigm toward native multimodal modeling (NMM) with the intrinsic integration of modalities for superior multimodal performance. Despite its potential, the design space of native architectures remains insufficiently defined. In this paper, we present the community with a formalized roadmap for this transition. Specifically, we formally define the architectural nativity, distinguishing mid-fusion and early-fusion from non-native paradigms. We further organize the existing native models through the lens of input-output duality into three categories: (i) Multi-to-Text for cross-modal comprehension with text-only output; (ii) Multi-to-Target for scenario-oriented generation, e.g., image, audio and video generation, and (iii) Multi-to-Multi for unified modeling with symmetric input-output. We deliver a comprehensive and industrial-grade investigation into the transition toward the definitive NMM framework, where understanding and generation seamlessly coexist within a unified transformer paradigm. We systematically unpack the end-to-end pipeline from industrial perspectives from architectural coordination, massive data curation, to full-stack training recipes, inference & deployment, and the comprehensive evaluation for truly native modeling.

cs.CV↗

arXiv2026

COCONUT: Toward practical time-evolving Sun-to-Earth magnetohydrodynamic modeling

Due to computational efficiency and numerical stability limitations, coronal simulations constrained by static magnetograms are typically performed first and then used to drive inner-heliosphere (IH) models. In this paper, we calculate the Sun-to-Earth coronal and wind evolutions using a single time-evolving MHD model, showing that implicit MHD models have the potential to meaningfully simplify and improve the overall Sun-to-Earth modelling pipeline. We extend the implicit time-evolving coronal MHD model COCONUT out to 1 AU, and utilise it to investigate solar coronal and wind evolutions around a solar maximum Carrington rotation (CR). We compare quasi-steady-state and time-evolving Sun-to-Earth simulations to evaluate the impact of the inner-boundary magnetic field evolution, which is neglected in steady-state simulations, on background plasma parameters. Comparisons with commonly used coupled Sun-to-Earth simulations are also conducted to further validate and assess the Sun-to-Earth model COCONUT. The results show that the time-evolving implicit MHD modelling approach yields noticeable differences compared to oversimplified steady-state simulations, and is efficient enough for practical applications. Modelling the solar corona and wind using a single MHD model simplifies the modelling pipeline and avoids uncertainties associated with coupling different coronal and IH models. The noticeable differences in the temporal evolution of plasma parameters at the L1 and L5 points highlight the need to use continuously evolving, synchronised magnetic field observations to improve global coronal and solar wind simulations, whereas the overall consistent evolutionary trend reveals the reliability of using L5 observations to forecast solar wind conditions near Earth about four days in advance.

astro-ph.SR↗

arXiv2026

3D Wi-Fi Signal Measurement in Realistic Digital Twin Testbed Environments Using Ray Tracing

Accurate and efficient modeling of indoor wireless signal propagation is crucial for the deployment of next-generation Wi-Fi. This paper presents a digital twin-based measurement system that integrates real-world 3D environment reconstruction with deterministic ray tracing for physically grounded electromagnetic modeling. Building geometry is obtained through LiDAR scanning, followed by object segmentation and assignment of ITU-R standard material parameters. The propagation process is simulated with a GPU-accelerated ray-tracing engine that generates path-level channel attributes, including delay, power, angular dispersion, and Ricean K-factor. Under identical runtime constraints, the proposed system is evaluated against a commercial measurement simulator, demonstrating up to 21 dB higher path gain and consistently improved signal-to-interference-plus-noise ratio in line-of-sight conditions. Additionally, experiments against onsite RSSI measurements confirm a high spatial correlation of 0.98 after calibration, proving the system's fidelity in real-world settings. Furthermore, coverage analysis across 2.4 GHz, 5 GHz, and 6 GHz bands demonstrates the capability of system to model frequency-dependent material attenuation for Wi-Fi 6E/7 networks. Finally, the system offers interactive 3D visualization and on-demand data extraction, highlighting its potential for digital twin-driven wireless system design and optimization.

cs.NI↗

arXiv2026

MHD modeling of magnetic flux evolution around solar maximum by the coronal model COCONUT

In this paper, we simulate the magnetic flux evolution at different heliocentric distances during two solar-maximum Carrington rotations (CRs) using the time-evolving coronal magnetohydrodynamic (MHD) model COCONUT to investigate the ``open flux problem". The simulated open magnetic flux (OMF) near the solar surface is comparable to that derived from \textit{in situ} observations by PSP and WIND satellites, and is about 5 times larger than that derived from SDO coronal hole (CH) observations, and the variation in the simulated radial solar wind speed is consistent with the evolution of the OMF evaluated around the corresponding solar disk center. We find that the OMF is reduced by up to $45\%$ from 1.01~$R_s$ to 0.1~AU and increases with a higher-resolution mesh. The OMF decreases mainly within 3~$R_s$, where the closed magnetic flux drops more rapidly, from about $60\%$ of the total magnetic flux at 1.01~$R_s$ to about $4\%$ at 3~$R_s$. Moderate adjustment of the heating source term can effectively regulate the simulated OMF. Preprocessing the photospheric magnetograms with a potential field solver that removes many high-order spherical harmonic components reduces the OMF in the low corona, while having little impact beyond 3~$R_s$. Additionally, the ratio of the maximum to the minimum OMF can reach 1.4 during a single solar maximum CR. These findings highlight the necessity of considering higher grid resolution, more realistic heating mechanisms, and the time-evolving regime of coronal MHD modeling when further addressing the ``open flux problem".

astro-ph.SR↗

arXiv2025

Hallucination Detection and Evaluation of Large Language Model

Hallucinations in Large Language Models (LLMs) pose a significant challenge, generating misleading or unverifiable content that undermines trust and reliability. Existing evaluation methods, such as KnowHalu, employ multi-stage verification but suffer from high computational costs. To address this, we integrate the Hughes Hallucination Evaluation Model (HHEM), a lightweight classification-based framework that operates independently of LLM-based judgments, significantly improving efficiency while maintaining high detection accuracy. We conduct a comparative analysis of hallucination detection methods across various LLMs, evaluating True Positive Rate (TPR), True Negative Rate (TNR), and Accuracy on question-answering (QA) and summarization tasks. Our results show that HHEM reduces evaluation time from 8 hours to 10 minutes, while HHEM with non-fabrication checking achieves the highest accuracy $82.2\%$ and TPR $78.9\%$. However, HHEM struggles with localized hallucinations in summarization tasks. To address this, we introduce segment-based retrieval, improving detection by verifying smaller text components. Additionally, our cumulative distribution function (CDF) analysis indicates that larger models (7B-9B parameters) generally exhibit fewer hallucinations, while intermediate-sized models show higher instability. These findings highlight the need for structured evaluation frameworks that balance computational efficiency with robust factual validation, enhancing the reliability of LLM-generated content.

cs.CL↗

arXiv2025

Total solar eclipse 2024 modelling with COCONUT

Coronal modelling is crucial for a better understanding of solar and helio-physics. Due to the strong brightness of the Sun and the lack of white light observations of the solar atmosphere and low corona (1-1.5R$_\odot$), total solar eclipses have become a standard approach for validating the coronal models. In this study, we validate the COCONUT coronal model by predicting the coronal configuration during the total solar eclipse on April 8, 2024. We aim to predict the accurate configuration of the solar corona during the total solar eclipse on April 8, 2024. We utilise the full 3D MHD model to reconstruct the solar corona from the solar surface to $30\;R_\odot$. The upcoming total solar eclipse predictions were conducted in three different regimes: quasi-steady driving of the inner boundary conditions (BCs) with a daily cadence and dynamic driving of the inner BCs with both daily and hourly cadences. The results from all the simulations are compared to the total solar eclipse images. Additionally, the synthetic white-light (WL) images are generated from the STEREO-A field of view and compared to COR2 observed images. Normalised polarised brightness is compared in the COR2 and synthetic WL images. The predicted solar corona does not vary significantly in the first half of the prediction window. The dynamic simulations yielded better results than the quasi-steady predictions. The west limb was reconstructed better in the simulations than the east limb. We have predicted the total solar eclipse coronal configuration 18 days before the total solar eclipse. We can conclude that the dynamic simulations produced more accurate predictions. The availability of comprehensive observations is crucial, as the emergence of the active region on the east limb made it difficult to accurately predict the east limb coronal configuration due to incorrect input of magnetic field data.

astro-ph.SR↗

arXiv2025

COCONUT: A coronal model with an energy decomposition strategy

In this paper, we propose an energy decomposition method combined with an HLL Riemann solver that includes an additional dissipation term in the energy equation to improve the numerical stability of the fully implicit, time-evolving coronal model COCONUT and extend its applicability to solar-maximum phases. In MHD simulations that evolve conservative variables in time, the thermal pressure is typically computed by subtracting the magnetic and kinetic energies from the total energy. In low-beta (the ratio of thermal to magnetic pressure; $< 10^{-3}$) regions, discretization errors of magnetic energy can be comparable to the thermal pressure, potentially leading to negative thermal pressure and causing the simulation to crash. Therefore, we update the decomposed energy, excluding the magnetic energy, at each time step. It avoids subtracting a large magnetic energy from the total energy to obtain a very small thermal pressure in low-$\beta$ regions, thereby improving the numerical stability of MHD models. We validate the algorithm using a time-evolving solar-maximum Carrington rotation simulation in 2025, which the previous code failed to run to completion. We also perform quasi-steady-state coronal simulations and 2D benchmark tests to further assess the algorithm's performance. The simulation results show that the algorithm produces results nearly identical to those obtained using the traditional full energy equation during solar minimum, while significantly improving COCONUT's ability to simulate coronal evolution under strong magnetic fields, even including fields exceeding 100 Gauss with $\beta<10^{-3}$. This method provides a promising approach for performing quasi-realistic coronal simulations during solar maxima.

astro-ph.SR↗

arXiv2025

SIP-IFVM: An observation-based magnetohydrodynamic model of coronal mass ejection

Currently, achieving a balance between computational efficiency, accuracy, and numerical stability in CME simulations, particularly in the sub-Alfv{'e}nic coronal region, remains a significant challenge. This paper aims to address the challenge by integrating observational data and developing advanced numerical algorithms, focusing on reproducing large-scale CME evolutions that are consistent with observations in the coronal region. Based on the recently developed fully implicit thermodynamic MHD coronal model (Wang et al. 2025a), we further use an observation-based RBSL flux rope to trigger a CME event during CR 2111. Additionally, we improve the temporal accuracy using a 2nd-order accurate ESDIRK2 method, with the intermediate stage solutions computed by the 2nd-order accurate BDF2 pseudo-time marching method. To enhance the numerical stability of ESDIRK2, we apply approximate linearisation in the implicitly solved intermediate stages. Furthermore, we adjust the time-evolving magnetic field B1 to zero at the end of each physical time step to further validate the extended magnetic field decomposition approach proposed by (Wang et al. 2025a). It is noticed that the model successfully reproduces the CME evolution consistent with white-light coronagraph observations, enables faster-than-real-time CME propagation simulations from solar surface to 0.1 AU using only a few dozen CPU cores on approximately 1 million grid cells, and remains numerically stable in CME simulations involving low-\b{eta} regions. The simulation results show that this novel MHD coronal model, combined with an observation-based magnetic flux rope, is sufficiently numerically stable and computationally efficient to reproduce real CME events propagating through the sub-Alfv{\'e}nic coronal region. Thus, the observation-based CME model is well suited for practical applications in daily space weather forecasting.

astro-ph.SR↗

arXiv2025

Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning

The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, privacy remains a major challenge, as immersive interactions require continuous collection of biometric and behavioral data. At the same time, ensuring high-quality, low-latency streaming is difficult due to the demands of real-time interaction, immersive rendering, and bandwidth optimization. To address these issues, we propose ASMS (Adaptive Social Metaverse Streaming), a novel streaming system based on Federated Multi-Agent Proximal Policy Optimization (F-MAPPO). ASMS leverages F-MAPPO, which integrates federated learning (FL) and deep reinforcement learning (DRL) to dynamically adjust streaming bit rates while preserving user privacy. Experimental results show that ASMS improves user experience by at least 14% compared to existing streaming methods across various network conditions. Therefore, ASMS enhances the social metaverse experience by providing seamless and immersive streaming, even in dynamic and resource-constrained networks, while ensuring that sensitive user data remains on local devices.

cs.LG↗

arXiv2025

Time-evolving coronal modelling of the solar maximum around the solar storms in May 2024 by COCONUT

Time-evolving MHD coronal models deliver more realistic results than traditional quasi-steady-state models. The fully implicit time-evolving coronal model COCONUT performs efficiently enough for real-time coronal simulations during solar minimum. However, during solar maxima, the coronal magnetic field is more complex and stronger, and coronal structures evolve more rapidly than during solar minima. Time-evolving MHD coronal modelling of solar maxima often struggles with poor numerical stability and low computational efficiency. We enhanced the numerical stability of the time-evolving coronal model COCONUT to mitigate these issues with the aim to evaluate the differences between the time-evolving and quasi-steady-state coronal simulation results, and to assess the impact of the spatial resolution on global MHD coronal modelling of solar maxima. After enhancing the positivity-preserving property of COCONUT, we employed it to simulate the evolution of coronal structures within 0.1 AU in an inertial coordinate system over two CRs around the solar storms in May 2024. These simulations were performed on unstructured geodesic meshes containing 6.06, 1.52, and 0.38 M cells. We also conducted a quasi-steady-state coronal simulation that treated the solar surface as a rigidly rotating spherical shell. A comparison with observations further validated the reliability of the time-evolving coronal modelling technique. It shows that incorporating the evolution of the magnetic field on the solar surface can significantly improve the fidelity of global MHD coronal simulations around a solar maximum. A simulated magnetic field strength using a mesh with 6.06 M cells can be stronger by more than 40% than that in a mesh with 0.38 M cells. The fully implicit time-evolving model COCONUT shows promise for accurately conducting real-time global coronal simulations of solar maxima.

astro-ph.SR↗

arXiv2025

Empirical Studies of Large Scale Environment Scanning by Consumer Electronics

This paper presents an empirical evaluation of the Matterport Pro3, a consumer-grade 3D scanning device, for large-scale environment reconstruction. We conduct detailed scanning (1,099 scanning points) of a six-floor building (17,567 square meters) and assess the device's effectiveness, limitations, and performance enhancements in diverse scenarios. Challenges encountered during the scanning are addressed through proposed solutions, while we also explore advanced methods to overcome them more effectively. Comparative analysis with another consumer-grade device (iPhone) highlights the Pro3's balance between cost-effectiveness and performance. The Matterport Pro3 achieves a denser point cloud with 1,877,324 points compared to the iPhone's 506,961 points and higher alignment accuracy with an RMSE of 0.0118 meters. The cloud-to-cloud (C2C) average distance error between the two point cloud models is 0.0408 meters, with a standard deviation of 0.0715 meters. The study demonstrates the Pro3's ability to generate high-quality 3D models suitable for large-scale applications, leveraging features such as LiDAR and advanced alignment techniques.

eess.IV↗

arXiv2025

Immersive Multimedia Communication: State-of-the-Art on eXtended Reality Streaming

Extended reality (XR) is rapidly advancing, and poised to revolutionize content creation and consumption. In XR, users integrate various sensory inputs to form a cohesive perception of the virtual environment. This survey reviews the state-of-the-art in XR streaming, focusing on multiple paradigms. To begin, we define XR and introduce various XR headsets along with their multimodal interaction methods to provide a foundational understanding. We then analyze XR traffic characteristics to highlight the unique data transmission requirements. We also explore factors that influence the quality of experience in XR systems, aiming to identify key elements for enhancing user satisfaction. Following this, we present visual attention-based optimization methods for XR streaming to improve efficiency and performance. Finally, we examine current applications and highlight challenges to provide insights into ongoing and future developments of XR.

cs.MM↗

arXiv2025

SIP-IFVM: A time-evolving coronal model with an extended magnetic field decomposition strategy

Time-evolving magnetohydrodynamic (MHD) coronal modeling, driven by a series of time-dependent photospheric magnetograms, represents a new generation of coronal simulations. This approach offers greater realism compared to traditional coronal models constrained by a static magnetogram. However, its practical application is seriously limited by low computational efficiency and poor numerical stability. Therefore, we propose an extended magnetic field decomposition strategy and implement it in the implicit MHD model to develop a coronal model that is both efficient and numerically stable enough for simulating the long-term evolutions of the global corona. The traditional decomposition strategies split the magnetic field into a time-invariant potential field and a time-dependent component $\mathbf{B}_1$. It works well for quasi-steady-state coronal simulations where $\left|\mathbf{B}_1\right|$ is typically small. However, as the inner-boundary magnetic field evolves, $\left|\mathbf{B}_1\right|$ can grow significantly larger and its discretization errors often lead to nonphysical negative thermal pressure, ultimately causing the code to crash. In this paper, we mitigate such undesired situations by introducing a temporally piecewise-constant variable to accommodate part of the non-potential field and remain $\left|\mathbf{B}_1\right|$ consistently small throughout the simulations. We incorporate this novel magnetic field decomposition strategy into our implicit MHD coronal model and apply it to simulate the evolution of coronal structures within 0.1 AU over two solar-maximum Carrington rotations. The results show that this coronal model effectively captures observations and performs more than 80 times faster than real time using only 192 CPU cores, making it well-suited for practical applications in simulating the time-evolving corona.

astro-ph.SR↗

arXiv2024

Efficient magnetohydrodynamic modelling of the time-evolving corona by COCONUT

Compared to quasi-steady-state corona models that are constrained by a time-invariant magnetogram over a CR period, time-evolving corona models driven by time-varying photospheric magnetograms are more realistic and can maintain more useful information to accurately describe solar wind evolution and forecast CME propagation. This paper demonstrate that time-evolving corona simulations can be performed efficiently and accurately using an implicit method with relatively large time steps. We also evaluate differences between coronal structures captured by time-evolving and quasi-steady simulations over a CR period during solar minimum. We used a series of hourly updated photospheric magnetograms to drive the evolution of coronal structures from the solar surface to $25\; R_s$ during two CRs around the 2019 eclipse in an inertial coordinate system. We compare the time-evolving and quasi-steady simulations to demonstrate that the differences in these two types of coronal modelling can be obvious even for a solar minimum. The relative differences in radial velocity and density can be over $15 \%$ and $25 \%$ at 20$\;R_s$ during one CR period. We also evaluated the impact of time steps on the simulation results. Using a time step of approximately 10 minutes balances efficiency and necessary numerical stability and accuracy for time-evolving corona simulations around solar minima, with coronal evolution during a full CR simulated within only 9 hours (using 1080 CPU cores for 1.5M grid cells). The simulation results demonstrate that time-evolving MHD coronal simulations can be performed efficiently and accurately using an implicit method, offering a more realistic alternative to quasi-steady-state simulations. The fully implicit time-evolving corona model thus promises to simulate the time-evolving corona accurately in practical space weather forecasting.

astro-ph.SR↗