arXiv subjects

Sheng Yang

Publications and source records attributed to Sheng Yang.

At least 55 records · Page 3Linked to original sources

arXiv2026

Performance of the Gamma-ray Transient Monitor at the IHEP Electron-Beam Facility

Gamma-Ray Transient Monitor (GTM) is an all-sky monitor onboard the Distant Retrograde Orbit-A (DRO-A) satellite, with the scientific objective of detecting gamma-ray bursts in the energy range of 20 keV to 1 MeV. GTM is equipped with five Gamma-Ray Transient Probes (GTPs), utilizing NaI(Tl) scintillators coupled with silicon photomultiplier (SiPM) arrays for signal readout. To test the performance of the GTP in detecting electrons, we used the IHEP Electron-Beam Facility (a continuous-energy-tunable, low-current, quasi-single-electron accelerator) for ground-based electron tests of the GTP. This paper provides a detailed description of the operating principles of the electron accelerator and presents the process and results of the GTP electron-beam tests. The test results show that the GTP has a dead time of less than 4 $μ$s for normal signals and approximately 70 $μ$s for overflow signals, consistent with the design specifications. The time-recording capability of the GTP was tested and found to be normal, with accurate recording of overflow events. The GTP's response to electrons in the 0.4-1.4 MeV range is also normal. Additionally, we used Geant4 to simulate the GTP's energy response and performed a comparative analysis of the simulation and experimental results. The performance tests and ground-based electron calibration validated the design of the GTP and enhanced the GTP's mass model, laying the foundation for payload development, in-orbit observation strategies, and scientific data analysis.

astro-ph.IM↗

arXiv2026

GLM-OCR Technical Report

GLM-OCR is an efficient 0.9B-parameter compact multimodal model designed for real-world document understanding. It combines a 0.4B-parameter CogViT visual encoder with a 0.5B-parameter GLM language decoder, achieving a strong balance between computational efficiency and recognition performance. To address the inefficiency of standard autoregressive decoding in deterministic OCR tasks, GLM-OCR introduces a Multi-Token Prediction (MTP) mechanism that predicts multiple tokens per step, significantly improving decoding throughput while keeping memory overhead low through shared parameters. At the system level, a two-stage pipeline is adopted: PP-DocLayout-V3 first performs layout analysis, followed by parallel region-level recognition. Extensive evaluations on public benchmarks and industrial scenarios show that GLM-OCR achieves competitive or state-of-the-art performance in document parsing, text and formula transcription, table structure recovery, and key information extraction. Its compact architecture and structured generation make it suitable for both resource-constrained edge deployment and large-scale production systems.

cs.CL↗

arXiv2026

A New Method for Identifying Contaminating Sources and Locating Target Sources through the Cross-Arm Features of Micro Pore Optics

The Pathfinder of the Type-A satellites in the Chasing All Transients Constellation Hunters (CATCH) space mission is equipped with Micro-Pore Optics (MPOs) and four single-pixel Silicon Drift Detectors (SDDs). Due to the lack of position resolution in an individual SDD, we propose a new method based on the cross-arms in the point spread function (PSF) of MPOs to enhance the satellite's capability in identifying contaminating sources and locating target sources. By placing one detector on each of the horizontal and vertical cross-arms on the focal plane, we can use the changes in the relative counts on the cross-arms detectors to deduce the location of the source. Simulated observations demonstrate that, for a target source with a flux of 1 Crab and an exposure time of 200 s, the cross-arms detectors can identify contaminating source with the same flux level at an off-axis angle larger than 8', and improve positioning accuracy to 6'. Furthermore, we extend the simulation study to CATCH Type-A, which plans to use an SDD array. In situations where sources exhibit the same flux of 1 Crab and the exposure time is merely 1 s, a 16x16 SDD array is capable of identifying contaminating source with an off-axis angle greater than 2.4' and can achieve a positioning precision of 1.8'.

astro-ph.IM↗

arXiv2026

LiDAR Prompted Spatio-Temporal Multi-View Stereo for Autonomous Driving

Accurate metric depth is critical for autonomous driving perception and simulation, yet current approaches struggle to achieve high metric accuracy, multi-view and temporal consistency, and cross-domain generalization. To address these challenges, we present DriveMVS, a novel multi-view stereo framework that reconciles these competing objectives through two key insights: (1) Sparse but metrically accurate LiDAR observations can serve as geometric prompts to anchor depth estimation in absolute scale, and (2) deep fusion of diverse cues is essential for resolving ambiguities and enhancing robustness, while a spatio-temporal decoder ensures consistency across frames. Built upon these principles, DriveMVS embeds the LiDAR prompt in two ways: as a hard geometric prior that anchors the cost volume, and as soft feature-wise guidance fused by a triple-cue combiner. Regarding temporal consistency, DriveMVS employs a spatio-temporal decoder that jointly leverages geometric cues from the MVS cost volume and temporal context from neighboring frames. Experiments show that DriveMVS achieves state-of-the-art performance on multiple benchmarks, excelling in metric accuracy, temporal stability, and zero-shot cross-domain transfer, demonstrating its practical value for scalable, reliable autonomous driving systems.

cs.CV↗

arXiv2026

BEV-VLM: Trajectory Planning via Unified BEV Abstraction

This paper introduces BEV-VLM, a novel approach for trajectory planning in autonomous driving that leverages Vision-Language Models (VLMs) with Bird's-Eye View (BEV) feature maps as visual input. Unlike conventional trajectory planning approaches that rely solely on raw visual data (e.g., camera images), our method utilizes a highly compressed and informative BEV representation generated by fusing camera and LiDAR data, with subsequent alignment to High-Definition (HD) maps. This unified BEV-HD map format provides a geometrically consistent and semantically rich scene description, which enables VLMs to perform accurate and robust trajectory planning. Experimental results on the nuScenes dataset demonstrate that, compared with state-of-the-art vision-only methods, our approach achieves a 53.1% improvement in planning accuracy and realizes complete collision avoidance in evaluation scenarios. Our work highlights that VLMs can effectively interpret processed visual representations such as BEV features, expanding their applicability beyond raw image inputs for the task of trajectory planning.

cs.RO↗

arXiv2026

Less is More: Lean yet Powerful Vision-Language Model for Autonomous Driving

In this work, we reconceptualize autonomous driving as a generalized language problem and formulate the trajectory planning task as next waypoint prediction. We introduce Max-V1, a novel framework for one-stage end-to-end autonomous driving, named in tribute to the renowned Dutch racing driver Max Verstappen. Our framework presents a single-pass generation paradigm that aligns with the inherent sequentiality of driving. This approach leverages the generative capacity of the Vision-Language Model (VLM) to enable end-to-end trajectory prediction directly from front-view camera input. The efficacy of this method is underpinned by a principled supervision strategy derived from statistical modeling. This provides a well-defined learning objective, which makes the framework highly amenable to mastering complex driving policies through imitation learning from large-scale expert demonstrations. Empirically, our method achieves state-of-the-art performance on the nuScenes dataset, delivering an overall improvement of over 30% compared to prior baselines. Furthermore, it exhibits superior generalization performance on cross-domain datasets acquired from diverse vehicles, demonstrating notable potential for cross-vehicle robustness and adaptability. With these empirical strengths, this work introduces a model that enables fundamental driving behaviors, laying the foundation for the development of more capable self-driving agents. Code will be available upon publication.

cs.CV↗

arXiv2026

From Bayesian Asymptotics to General Large-Scale MIMO Capacity

We present a unifying framework that bridges Bayesian asymptotics and information theory to analyze the asymptotic Shannon capacity of general large-scale MIMO channels including ones with nonlinearities or imperfect hardware. We derive both an analytic capacity formula and an asymptotically optimal input distribution in the large-antenna regime, each of which depends solely on the single-output channel's Fisher information through a term we call the (tilted) Jeffreys factor. We demonstrate how our method applies broadly to scenarios with clipping, coarse quantization (including 1-bit ADCs), phase noise, fading with imperfect CSI, and even optical Poisson channels. Our asymptotic analysis motivates a practical approach to constellation design via a compander-like transformation. Furthermore, we introduce a low-complexity receiver structure that approximates the log-likelihood by quantizing the channel outputs into finitely many bins, enabling near-capacity performance with computational complexity independent of the output dimension. Numerical results confirm that the proposed method unifies and simplifies many previously intractable MIMO capacity problems and reveals how the Fisher information alone governs the channel's asymptotic behavior.

cs.IT↗

arXiv2026

GECAM discovery of the second FRB-associated Magnetar X-ray Burst from SGR J1935+2154

Fast radio burst (FRB) is mysterious phenomenon with millisecond-duration radio pulses observed mostly from cosmological distance. The association between FRB 200428 and a magnetar X-ray burst (MXB) from SGR J1935+2154 has significantly advanced the understanding of FRB and magnetar bursts. However, it is uncertain whether this association between MXB and FRB (i.e. MXB/FRB 200428) is genuine or just coincidental only based on this single event. Here we report the discovery of a bright ($\rm\sim7.6\times10^{-7}\,erg \cdot cm^{-2}$ in 1-250 keV) magnetar X-ray burst detected by GECAM on October 14th, 2022 (dubbed as MXB 221014) from SGR J1935+2154, which is associated with a FRB detected by CHIME and GBT. We conducted a detailed temporal and spectral analysis of MXB 221014 with GECAM data and find that it is a bright and typical ($T_{90}\sim$250 ms) X-ray burst from this magnetar. Interestingly, we find two narrow X-ray pulses in the MXB, one of which temporally aligns with the main pulse of the FRB 221014 $\sim5.70$ ms latter than the peak time of FRB 221014), resembling the feature found in MXB/FRB 200428. Furthermore, we did comprehensive comparison between MXB/FRB 221014 and MXB/FRB 200428, and find that while the two events share several common features, they also exhibit distinct differences, highlighting the variety of the MXB-FRB association morphology. This finding not only confirms the association between MXB and FRB but also provides new insights into the mechanism of and the relationship between FRB and MXB.

astro-ph.HE↗

arXiv2026

Systematic Study of the Simultaneous Events Detected by GECAM

GECAM is a constellation of all-sky monitors in hard X-ray and gamma-ray band primarily aimed at high energy transients such as gamma-ray bursts, soft gamma-ray repeaters, solar flares and terrestrial gamma-ray flashes. As GECAM has the highest temporal resolution (0.1~$μ$s) among instruments of its kind, it can identify the so-called simultaneous events (STE) that deposit signals in multiple detectors nearly at the same time (with a 0.3~$μ$s window). However, the properties and origin of STE have not yet been explored. In this work, we implemented, for the first time, a comprehensive analysis of the STE detected by GECAM, including their morphology, energy deposition, and the dependence on the geomagnetic coordinates. We find that these STE probably result from direct interactions between high-energy charged cosmic rays and satellite. These results demonstrate that GECAM can detect, identify, and characterize high-energy cosmic rays, making it a Micro Cosmic-Ray Observatory (MICRO) in low Earth orbit.

astro-ph.HE↗

arXiv2026

An Archival Optical Counterpart Search for Extragalactic Fast X-Ray Transients Discovered by Einstein Probe

Extragalactic fast X-ray transients (eFXTs) represent a rapidly growing class of high-energy phenomena, whose physical origins remain poorly understood. With its wide-field, sensitive all-sky monitoring, the Einstein Probe (EP) has greatly increased the discovery rate of eFXTs. The search and identification of the optical counterparts of eFXT are vital for understanding their classification and constraining their physical origin. Yet, a considerable fraction of eFXTs still lack secure classifications due to the absence of timely follow-up observations. We carry out a systematic search of publicly available optical survey data and transient databases (including the Zwicky Transient Facility, ZTF, and the Transient Name Server, TNS) for optical counterparts to eFXT candidates detected by EP. In this paper, we describe our ongoing program and report the first results. Specifically, we identified the eFXT EP240506a to be associated with a UV/optical counterpart, AT 2024ofs. Spectroscopy of its host galaxy with VLT yields a redshift of $z = 0.120 \pm 0.002$. By combining archival survey data with early-time multiwavelength observations, we find that the luminosity and light-curve evolution of AT~2024ofs are consistent with a core-collapse supernova origin. From detectability simulations, we estimate a local event rate density $ρ_{0}=8.8^{+21.2}_{-3.9}\ \mathrm{yr^{-1}\, Gpc^{-3}}$ for EP240506a-like events, and completeness-corrected rate of about $36$--$78\ \mathrm{yr^{-1}\ Gpc^{-3}}$ for EP-detected X-ray transients associated with supernovae. Our results demonstrate the potential of EP to uncover prompt high-energy emission from core-collapse supernovae and underscore the critical importance of timely follow-up of future eFXT events.

astro-ph.HE↗

arXiv2026

Evaluating S-Band Interference: Impact of Satellite Systems on Terrestrial Networks

The co-existence of terrestrial and non-terrestrial networks (NTNs) is essential for achieving global coverage in sixth-generation cellular networks. Due to increasing spectrum demand, there is discussion in the world level to share some frequencies used in terrestrial Networks (TNs) with NTNs, resulting in co-channel interference and performance degradation. This paper analyzes the interference caused by satellite networks on TN in the S-band. We examined the transmission mechanisms of satellite signals and conducted simulations to evaluate interference intensity across varying slant ranges. Our findings indicate that the angle between the user equipment direction and the sub-satellite point direction from the beam center significantly impacts the interference level.

eess.SP↗

arXiv2026

Capacity Bounds on Doppler OFDM Channels

Low Earth orbit (LEO) satellite systems experience significant Doppler effects due to high mobility. While Doppler shifts can be largely compensated, residual frequency uncertainty induces a structured form of channel uncertainty that can limit achievable rates. We model this effect using a block-fading channel of the form $ \mathbf{H} = \mathbf{F} + s \mathbf{G} $, where $s$ is an unknown scalar random parameter. We first study this model in a general $N\times N$ MIMO setting. For this channel, we derive achievable rate lower bounds based on explicit transmission schemes and capacity upper bounds using a duality approach. We study Gaussian signaling and propose a practical superposition scheme with subspace alignment (SN) and successive interference cancellation, where a coarse-layer stream serves as an implicit pilot for decoding refined-layer data. We characterize asymptotic capacity in the near-coherent and high-SNR regimes, and show via Doppler-OFDM simulations that the proposed SN scheme achieves near-optimal rates with low complexity.

cs.IT↗

arXiv2026

TIGaussian: Disentangle Gaussians for Spatial-Awared Text-Image-3D Alignment

While visual-language models have profoundly linked features between texts and images, the incorporation of 3D modality data, such as point clouds and 3D Gaussians, further enables pretraining for 3D-related tasks, e.g., cross-modal retrieval, zero-shot classification, and scene recognition. As challenges remain in extracting 3D modal features and bridging the gap between different modalities, we propose TIGaussian, a framework that harnesses 3D Gaussian Splatting (3DGS) characteristics to strengthen cross-modality alignment through multi-branch 3DGS tokenizer and modality-specific 3D feature alignment strategies. Specifically, our multi-branch 3DGS tokenizer decouples the intrinsic properties of 3DGS structures into compact latent representations, enabling more generalizable feature extraction. To further bridge the modality gap, we develop a bidirectional cross-modal alignment strategies: a multi-view feature fusion mechanism that leverages diffusion priors to resolve perspective ambiguity in image-3D alignment, while a text-3D projection module adaptively maps 3D features to text embedding space for better text-3D alignment. Extensive experiments on various datasets demonstrate the state-of-the-art performance of TIGaussian in multiple tasks.

cs.CV↗

arXiv2026

Brightest GRB flare observed in GRB 221009A: bridge the last gap between flare and prompt emission in GRB

Flares are usually observed during the afterglow phase of Gamma-Ray Bursts (GRBs) in soft X-ray, optical and radio bands, but rarely in gamma-ray band. Despite the extraordinary brightness, GECAM-C has accurately measured both the bright prompt emission and flare emission of GRB 221009A without instrumental effects, offering a good opportunity to study the relation between them. In this work, we present a comprehensive analysis of flare emission of GRB 221009A, which is composed of a series of flares. Among them, we identify an exceptionally bright flare with a record-breaking isotropic energy $E_{\rm iso} = 1.82 \times 10^{53}$ erg of GRB flares. It exhibits the highest peak energy ever detected in GRB flares, $E_{\rm peak} \sim 300$ keV, making it a genuine gamma-ray flare. It also shows rapid rise and decay timescales, significantly shorter than those of typical X-ray flares observed in soft X-ray or optical band, but comparable to those observed in prompt emissions. Despite these exceptional properties, the flare shares several common properties with typical GRB flares. We note that this is the first observation of a GRB flare in the keV-MeV band with sufficiently high temporal resolution and high statistics, which bridges the last gap between prompt emission and flare.

astro-ph.HE↗

arXiv2026

Error Exponents for Randomised List Decoding

This paper studies random-coding error exponents of randomised list decoding, in which the decoder randomly selects $L$ messages with probabilities proportional to the decoding metric of the codewords. The exponents (or bounds) are given for mismatched, and then particularised to matched and universal decoding metrics. Two regimes are studied: for fixed list size, we derive an ensemble-tight random-coding error exponent, and show that, for the matched metric, it does not improve the error exponent of ordinary decoding. For list sizes growing exponentially with the block-length, we provide a non-trivial lower bound to the error exponent that is tight at high rates under the matched metric.

cs.IT↗

arXiv2026

MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning

Traditional workflow-based agents exhibit limited intelligence when addressing real-world problems requiring tool invocation. Tool-integrated reasoning (TIR) agents capable of autonomous reasoning and tool invocation are rapidly emerging as a powerful approach for complex decision-making tasks involving multi-step interactions with external environments. In this work, we introduce MindWatcher, a TIR agent integrating interleaved thinking and multimodal chain-of-thought (CoT) reasoning. MindWatcher can autonomously decide whether and how to invoke diverse tools and coordinate their use, without relying on human prompts or workflows. The interleaved thinking paradigm enables the model to switch between thinking and tool calling at any intermediate stage, while its multimodal CoT capability allows manipulation of images during reasoning to yield more precise search results. We implement automated data auditing and evaluation pipelines, complemented by manually curated high-quality datasets for training, and we construct a benchmark, called MindWatcher-Evaluate Bench (MWE-Bench), to evaluate its performance. MindWatcher is equipped with a comprehensive suite of auxiliary reasoning tools, enabling it to address broad-domain multimodal problems. A large-scale, high-quality local image retrieval database, covering eight categories including cars, animals, and plants, endows model with robust object recognition despite its small size. Finally, we design a more efficient training infrastructure for MindWatcher, enhancing training speed and hardware utilization. Experiments not only demonstrate that MindWatcher matches or exceeds the performance of larger or more recent models through superior tool invocation, but also uncover critical insights for agent training, such as the genetic inheritance phenomenon in agentic RL.

cs.AI↗

arXiv2026

GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

We present GLM-4.1V-Thinking, GLM-4.5V, and GLM-4.6V, a family of vision-language models (VLMs) designed to advance general-purpose multimodal understanding and reasoning. In this report, we share our key findings in the development of the reasoning-centric training framework. We first develop a capable vision foundation model with significant potential through large-scale pre-training, which arguably sets the upper bound for the final performance. We then propose Reinforcement Learning with Curriculum Sampling (RLCS) to unlock the full potential of the model, leading to comprehensive capability enhancement across a diverse range of tasks, including STEM problem solving, video understanding, content recognition, coding, grounding, GUI-based agents, and long document interpretation. In a comprehensive evaluation across 42 public benchmarks, GLM-4.5V achieves state-of-the-art performance on nearly all tasks among open-source models of similar size, and demonstrates competitive or even superior results compared to closed-source models such as Gemini-2.5-Flash on challenging tasks including Coding and GUI Agents. Meanwhile, the smaller GLM-4.1V-9B-Thinking remains highly competitive-achieving superior results to the much larger Qwen2.5-VL-72B on 29 benchmarks. We open-source both GLM-4.1V-9B-Thinking and GLM-4.5V. We further introduce the GLM-4.6V series, open-source multimodal models with native tool use and a 128K context window. A brief overview is available at https://z.ai/blog/glm-4.6v. Code, models and more information are released at https://github.com/zai-org/GLM-V.

cs.CV↗

arXiv2025

GECAM Observations of the Galactic Magnetar SGR J1935+2154 during the 2021 and 2022 Burst Active Episodes. I. Burst Catalog

Magnetar is a neutron star with an ultrahigh magnetic field ($\sim 10^{14}-10^{15}$ G). The magnetar SGR J1935+2154 is not only one of the most active magnetars detected so far, but also the unique confirmed source of fast radio bursts (FRBs). Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is dedicated to monitor gamma-ray transients all over the sky, including magnetar short bursts. Here we report the GECAM observations of the burst activity of SGR J1935+2154 from January 2021 to December 2022, which results in a unique and valuable data set for this important magnetar. With a targeted search of GECAM data, 159 bursts from SGR J1935+2154 are detected by GECAM-B while 97 bursts by GECAM-C, including the X-ray burst associated with a bright radio burst. We find that both the burst duration and the waiting time between two successive bursts follow lognormal distributions. The period of burst activity is $134\pm20$ days, thus the burst activity could be generally divided into four active episodes over these two years. Interestingly, the hardness ratio of X-ray bursts tends to be softer during these two years, especially during the active episode with radio bursts detected.

astro-ph.HE↗