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Ping Wang

Publications and source records attributed to Ping Wang.

At least 37 records · Page 2Linked to original sources
arXiv2026

JustDiag!: A Diagnostic Justification Engine for Accountable Root Cause Analysis

Large language models can produce fluent root cause analyses, but fluent final answers alone are insufficient evidence for accountability in high-stakes operations. In real incident response, engineers need to know what evidence supported a diagnosis, which alternatives were considered, where contradictions remained, and whether the system resolved the case or preserved uncertainty. We address this gap with JustDiag, a diagnostic justification engine for RCA that maintains an explicit process state over evidence, findings, competing hypotheses, conflicts, and next checks. We evaluated the system on 66 real-world incidents using a two-layer protocol that separately scores final-answer quality and process quality. Relative to a matched control without diagnostic justification, JustDiag achieved stronger outcome and process scores, while accepting slightly lower terminal completion due to more calibrated non-closure. These results suggest that accountable RCA requires explicit diagnostic justification artifacts and process-aware evaluation, not only fluent final answers.

cs.SE↗
arXiv2026

Deep Probabilistic Unfolding for Quantized Compressive Sensing

We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to enhance the reconstruction accuracy and efficiency. Unlike previous unfolding methods that apply L2 projection to measurements, we derive a closed-form, numerically stable likelihood gradient projection, which allows the model to respect the true quantization physics, turning the hard quantization constraint into a soft probabilistic guidance. Furthermore, an efficient, dual-domain Mamba module is specifically designed to dynamically capture and fuse the multi-scale local and global features, ensuring the interactions between the distant but correlated regions. Extensive experiments demonstrate the state-of-the-art performance of the proposed method over previous works, which is capable of promoting the application of quantized compressive sensing in real life.

cs.CV↗
arXiv2026

Hierarchical LLM-Driven Control for HAPS-Assisted UAV Networks: Joint Optimization of Flight and Connectivity

Uncrewed aerial vehicles (UAVs) are increasingly deployed in complex networked environments, yet the joint optimization of multi-UAV motion control and connectivity remains a fundamental challenge. In this paper, we study a multi-UAV system operating in an integrated terrestrial and non-terrestrial network (ITNTN) comprising terrestrial base stations and high-altitude platform stations (HAPS). We consider a three-dimensional (3D) aerial highway scenario where UAVs must adapt their motion to ensure collision avoidance, efficient traffic flow, and reliable communication under dynamic and partially observable conditions. We first model the problem as a hierarchical multi-objective partially observable Markov decision process (H-MO-POMDP), capturing the coupling between control and communication objectives. Based on this formulation, we propose a large language model (LLM)-driven hierarchical multi-rate control framework. At the global level, an LLM-based controller on the HAPS performs long-term planning for load balancing and handover decisions. At the local level, each UAV employs a hybrid controller that integrates a slow-timescale LLM for high-level spatial reasoning with a reinforcement learning agent for faster UAV-to-infrastructure (U2I) communication and motion control. We further develop a high-fidelity 3D simulation platform by integrating the gym-pybullet-drones environment with 3GPP-compliant RF/THz channel models. Numerical results demonstrate that the proposed framework significantly outperforms state-of-the-art baselines, achieving a 14% increase in transportation efficiency and a 25% improvement in telecommunication throughput. Additionally, it achieves a 23% reduction in physical collision rates, demonstrating strong handover stability and zero-shot generalization in dynamic scenarios.

cs.AI↗
arXiv2026

Finite-Size Gradient Transport in Large Language Model Pretraining: From Cascade Size to Intensive Transport Efficiency

We introduce a finite-size gradient-transport framework for real language-model training, based on five observables $(D,z,β,δ,v_{\mathrm{rel}})$ that separate cascade size, duration, absolute transport, and intensive transport efficiency. We analyze direct raw-gradient measurements from Pico-LM across four scales and 125 aligned steps, together with a five-scale Pythia companion dataset built from 153 aligned checkpoint-difference update fields. The same algebraic closure holds in both families, and both share a near-unity cascade-size backbone, but they occupy distinct transport regimes: Pico-LM shows positive duration scaling and negative intensive-efficiency scaling, whereas Pythia remains near the $D=1$ baseline with only weak positive efficiency scale dependence. Randomized-field controls give nearly matched null floors in the intensive and duration channels, indicating that the contrast reflects different real departures from a shared null skeleton rather than different null calibrations. The families also differ in stepwise power-law compressibility: Pico-LM retains clean duration and efficiency power laws, whereas Pythia preserves the size backbone but shows weaker one-slope compressibility in those channels. External performance associations are correspondingly channel-level, carried mainly by $v_{\mathrm{rel}}$ and normalized cascade duration, while $D(t)$ acts as a shared size backbone without a significant exponent-level performance association. These results support a reusable transport measurement framework without claiming a universal fixed point or a first-principles derivation of neural scaling laws.

cs.LG↗
arXiv2026

The trigger and localization system of SVOM-GRM

The Space multi-band Variable Object Monitor (SVOM) is an astronomical satellite jointly developed by China and France, primarily focused on the detection of gamma-ray bursts (GRBs) and transient sources. The SVOM satellite was launched on 22nd June, 2024 with four payloads installed onboard. As one of payload, GRM comprises 3 gamma-ray detectors (each detector has an effective area of approximately 200~cm$^{2}$) with distinct pointing directions, enabling the temporal and spectral measurements as well as localization of GRBs in the energy range of 15-5000 keV. This article firstly introduces the on-board localization algorithm design for GRM and presents preliminary test results. Then, leveraging abundant ground-based computational resources, a joint fitting method for spectral and localization analysis using Monte Carlo Markov Chain (MCMC) is implemented. In contrast to the on-board localization algorithm, the on-ground MCMC method comprehensively considers the influence of spectral characteristics, thereby mitigating systematic biases. Finally, a systematic analysis based on this method is provided, highlighting the localization and spectral measurement capabilities of GRM. The preliminary localization analysis result for the on-board detected GRB 240629A by both GRM and Fermi/GBM shows that the localization result (error$\sim$4.14$^{\circ}$) of GRM is consistent with the Fermi/GBM result.

astro-ph.IM↗
arXiv2026

Study on the detector energy response of SVOM/GRM

The SVOM mission is specifically designed to for the detection and localization of Gamma-Ray Bursts (GRBs) and subsequent follow-up observations. Among the four telescopes installed on the SVOM satellite, the Gamma-Ray Monitor (GRM) plays a crucial role in capturing the prompt emission of GRBs due to its wide field of view (FOV) and broad energy range. Accurate determination of the detector's energy response is vital for analyzing GRM data, particularly considering the significant impact of the atmospheric albedo effect on this response. This research focuses on deriving the detector's energy response and establishing a calibration database for the GRM, with particular emphasis on investigating the atmospheric albedo effect. The study shows that the contribution of albedo photons to the detector's effective area depends strongly on the orientation of the GRD line of sight (LoS) relative to Earth and on the incident direction of the GRB. When the GRD LoS is anti-Earth oriented, the albedo effect is minimal, with the highest proportion of albedo effective area accounting for approximately 10% of the total effective area. This occurs when the incident angle of the GRB is nearly perpendicular to the LoS. Conversely, if the GRD LoS is not pointing away from Earth and the GRB arrives from angles greater than about 90$^{\circ}$, the albedo component can become predominant, contributing up to around 100% of the total effective area. This is especially pronounced in the 8-20 keV range, where the direct effective area drops to zero due to the large GRB injection angle. Our results show that, it is necessary for GRM to consider the atmospheric albedo effects in detector response, otherwise the spectral and localization analyses will result in biased measurements.

astro-ph.HE↗
arXiv2026

The Gamma-Ray Monitor onboard the SVOM satellite

The Gamma-Ray Monitor (GRM) is a key scientific payload onboard the Space-based Multi-band Variable Object Monitor (SVOM) satellite, designed specifically for the detection and study of gamma-ray bursts (GRBs). Launched into a 625 km low-Earth orbit on 22 June 2024, GRM serves as a large-area, wide-field-of-view instrument capable of observing the hard X-ray and soft gamma-ray emissions in the energy range of 15 keV to 5 MeV. Its primary scientific objectives include: promptly triggering and localizing GRBs (with particular sensitivity to short-hard GRBs), measuring spectral and temporal properties of bursts, monitoring charged particle fluxes in orbit. GRM successfully detected its first GRB (GRB 240627B) on 27 June 2024, and has since maintained a detection rate of more than 100 GRBs per year. Cross-instrument comparisons with detectors such as GECAM and Fermi/GBM have validated the performance and data quality of GRM. This paper provides a comprehensive overview of GRM instrument design, reliability verification through ground testing, in-orbit triggering and localization algorithms, performance calibration, and preliminary in-orbit results, demonstrating its capability as a versatile gamma-ray all-sky monitor.

astro-ph.IM↗
arXiv2026

GRM Scientific Pipeline

The Gamma-Ray Monitor (GRM) is a key payload of the Space-based multiband astronomical Variable Objects Monitor (SVOM) mission, which is designed to detect gamma ray bursts (GRBs) within the energy range of 15 keV to 5 MeV. The GRM Instrument Center (GRM\_IC) features real-time data processing through the X-band, enabling rapid response of high-energy GRB events. The system employs an event-driven architecture and distributed design, achieving efficient processing and real-time monitoring of massive observational data. Through comprehensive data production processes and scientific data product management, the system achieves efficient production of scientific data products of the L1B / C level through the submission of jobs to the task scheduling system. Through modular architecture design and automated processing workflow, the GRM data processing system realizes precise conversion and scientific analysis of GRB detection data, providing robust technical support for future system upgrades and cross-platform collaboration.

astro-ph.IM↗
arXiv2026

RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding

Autoregressive decoding in Large Language Models (LLMs) generates one token per step, causing high inference latency. Speculative decoding (SD) mitigates this through a guess-and-verify strategy, but existing training-free variants face trade-offs: retrieval-based drafts break when no exact match exists, while logits-based drafts lack structural guidance. We propose $\textbf{RACER}$ ($\textbf{R}$etrieval-$\textbf{A}$ugmented $\textbf{C}$ont$\textbf{e}$xtual $\textbf{R}$apid Speculative Decoding), a lightweight and training-free method that integrates retrieved exact patterns with logit-driven future cues. This unification supplies both reliable anchors and flexible extrapolation, yielding richer speculative drafts. Experiments on Spec-Bench, HumanEval, and MGSM-ZH demonstrate that RACER consistently accelerates inference, achieving more than $2\times$ speedup over autoregressive decoding, and outperforms prior training-free methods, offering a scalable, plug-and-play solution for efficient LLM decoding. Our source code is available at $\href{https://github.com/hkr04/RACER}{https://github.com/hkr04/RACER}$.

cs.CL↗
arXiv2026

Endwall and leading-edge film cooling of turbine blades in a hydrogen-fueled rotating detonation combustor-turbine coupled system

This study performs a three-dimensional numerical simulation of the coupled flow field in a hydrogen-air rotating detonation combustor (RDC)-turbine system to evaluate the effectiveness of different film cooling strategies for the turbine blades. The results demonstrate that combining the endwall cooling with leading-edge film cooling effectively reduces blade surface temperatures while improving turbine flow field stability and blade protection. For endwall cooling, numerical simulations compare circular and slot hole configurations. Circular holes consume less cooling air than slot holes while maintaining comparable cooling performance, making them the preferred choice. For the leading-edge film cooling, both the vertical and the vertical-inclined schemes are examined. The vertical-inclined scheme demonstrates higher cooling efficiency and improved secondary flow attachment, ensuring greater stability under the oscillatory effects of the detonation flow. Additionally, the flow fields of film-cooled turbine blades with and without the propagation of the rotating detonation wave are compared, revealing that the upstream rotating detonation flow field facilitates the downstream diffusion of secondary film cooling jets.

physics.flu-dyn↗
arXiv2026

OAM modes characteristics analysis and low-loss transmission based on topological confinement

The topological confinement is a new mechanism that allows the transmission of cutoff orbital angular momentum (OAM) modes with negligible loss in ring-core fibers (RCFs) and provides a natural immunity against mode coupling. We investigate the influence of fiber design parameters and wavelength on the characteristics of topologically confined modes (TCMs) in step index ring-core fibers (SI-RCFs), and propose a type of graded index ring-core fibers (GI-RCF) with better characteristics. Furthermore, as TCMs occurs in structures with high refractive index difference and are often accompanied by relatively high scattering loss, we fabricate a type of low-loss SI-RCF and observe the stable existence of 24 low-loss TCMs in total. Subsequently, we use an analytical model to estimate the maximum signal-to-noise (SNR) and spectral efficiency (SE) of the fiber, demonstrating its strong capacity advantages.

physics.optics↗
arXiv2026

End-to-end Contrastive Language-Speech Pretraining Model For Long-form Spoken Question Answering

Significant progress has been made in spoken question answering (SQA) in recent years. However, many existing methods, including large audio language models, struggle with processing long audio. Follow the success of retrieval augmented generation, a speech-related retriever shows promising in help preprocessing long-form speech. But the performance of existing speech-related retrievers is lacking. To address this challenge, we propose CLSR, an end-to-end contrastive language-speech retriever that efficiently extracts question-relevant segments from long audio recordings for downstream SQA task. Unlike conventional speech-text contrastive models, CLSR incorporates an intermediate step that converts acoustic features into text-like representations prior to alignment, thereby more effectively bridging the gap between modalities. Experimental results across four cross-modal retrieval datasets demonstrate that CLSR surpasses both end-to-end speech related retrievers and pipeline approaches combining speech recognition with text retrieval, providing a robust foundation for advancing practical long-form SQA applications.

cs.SD↗
arXiv2026

MERMAID: Memory-Enhanced Retrieval and Reasoning with Multi-Agent Iterative Knowledge Grounding for Veracity Assessment

Assessing the veracity of online content has become increasingly critical. Large language models (LLMs) have recently enabled substantial progress in automated veracity assessment, including automated fact-checking and claim verification systems. Typical veracity assessment pipelines break down complex claims into sub-claims, retrieve external evidence, and then apply LLM reasoning to assess veracity. However, existing methods often treat evidence retrieval as a static, isolated step and do not effectively manage or reuse retrieved evidence across claims. In this work, we propose MERMAID, a memory-enhanced multi-agent veracity assessment framework that tightly couples the retrieval and reasoning processes. MERMAID integrates agent-driven search, structured knowledge representations, and a persistent memory module within a Reason-Action style iterative process, enabling dynamic evidence acquisition and cross-claim evidence reuse. By retaining retrieved evidence in an evidence memory, the framework reduces redundant searches and improves verification efficiency and consistency. We evaluate MERMAID on three fact-checking benchmarks and two claim-verification datasets using multiple LLMs, including GPT, LLaMA, and Qwen families. Experimental results show that MERMAID achieves state-of-the-art performance while improving the search efficiency, demonstrating the effectiveness of synergizing retrieval, reasoning, and memory for reliable veracity assessment.

cs.CL↗
arXiv2026

Grokking as Dimensional Phase Transition in Neural Networks

Neural network grokking -- the abrupt memorization-to-generalization transition -- challenges our understanding of learning dynamics. Through finite-size scaling of gradient avalanche dynamics across eight model scales, we find that grokking is a \textit{dimensional phase transition}: effective dimensionality~$D$ crosses from sub-diffusive (subcritical, $D < 1$) to super-diffusive (supercritical, $D > 1$) at generalization onset, exhibiting self-organized criticality (SOC). Crucially, $D$ reflects \textbf{gradient field geometry}, not network architecture: synthetic i.i.d.\ Gaussian gradients maintain $D \approx 1$ regardless of graph topology, while real training exhibits dimensional excess from backpropagation correlations. The grokking-localized $D(t)$ crossing -- robust across topologies -- offers new insight into the trainability of overparameterized networks.

cs.LG↗
arXiv2026

Dimensional Criticality at Grokking Across MLPs and Transformers

Abrupt transitions between distinct dynamical regimes are a hallmark of complex systems. Grokking in deep neural networks provides a striking example -- an abrupt transition from memorization to generalization long after training accuracy saturates -- yet robust macroscopic signatures of this transition remain elusive. Here we introduce \textbf{TDU--OFC} (Thresholded Diffusion Update--Olami-Feder-Christensen), an offline avalanche probe that converts gradient snapshots into cascade statistics and extracts a \emph{macroscopic observable} -- the time-resolved effective cascade dimension $D(t)$ -- via grokking-aligned finite-size scaling. Across Transformers trained on modular addition and MLPs trained on XOR, we discover a localized dynamical crossing of the Gaussian diffusion baseline $D=1$ precisely at the generalization transition. The crossing direction is task-dependent: modular addition descends through $D=1$ (approaching from $D>1$), while XOR ascends (from $D<1$). This opposite-direction convergence is consistent with attraction toward a candidate shared critical manifold, rather than trivial residence near $D \approx 1$. Negative controls confirm this picture: ungrokked runs remain supercritical ($D>1$) and never enter the post-transition regime. In addition, avalanche distributions exhibit heavy tails and finite-size scaling consistent with the dimensional exponent extracted from $D(t)$. Shadow-probe controls ($α_{\mathrm{train}}=0$) confirm that $D(t)$ is non-invasive, and grokked trajectories diverge from ungrokked ones in $D(t)$ some $100$--$200$ epochs before the behavioral transition.

cs.LG↗
arXiv2026

GECAM discovery of a peculiar magnetar X-ray burst (MXB 221120) from SGR J1935+2154 associated with a fast radio burst

Fast radio bursts (FRBs) are enigmatic cosmic transients of millisecond duration observed in the radio band. The identification of FRB-associated magnetar X-ray bursts (MXBs) from galactic magnetar SGR J1935+2154 suggests that at least a fraction of FRBs can be produced from magnetar activity. However, the sample size of FRB-associated MXBs is still very small. Here we report a bright and peculiar FRB-associated MXB from SGR J1935+2154 detected by GECAM on November 20, 2022, dubbed MXB 221120. We find that both temporal and spectral properties of MXB 221120 exhibit distinctive features. Its light curve could be generally described by a single FRED function with superposition of several narrow pulses. Interestingly, we identify a possible QPO feature with center frequency of ~18 Hz in this MXB. The time-integrated spectrum is best fitted by a blackbody model with temperature (kT ) of 18.6 keV, rendering it the first thermal spectrum FRB-associated MXB from SGR J1935+2154. Compared to other MXBs with single emission episode, MXB 221120 has longer duration and higher blackbody temperature, making it an outlier in the burst sample. These results indicate that MXB 221120 may be produced by a special mechanism with extreme physical conditions.

astro-ph.HE↗
arXiv2026

Comprehensive Measurement of Spectral Evolution in a GRB Flare: High Time-Resolution Insights into the "Double-Tracking" Phenomenon

The spectral evolution characteristics of the prompt emission in gamma-ray bursts (GRBs) have been extensively studied, but detailed investigations of spectral evolution in a GRB flare remain lacking. In this work, we present the first analysis of spectral parameter evolution in a GRB flare through high time-resolved spectral fitting of the Brightest Flare in GRB 221009A. We find that the $α$-Flux, $E_p$-Flux, and $E_p$-$α$ relationships during both the overall phase and the rise phase of flare can be well described by simple power-law model, showing positive correlations. Therefore, we conclude that Brightest Flare exhibits "Double-tracking" behavior. Since values of $α$ do not exceed the synchrotron "death line" (-2/3), we explain this phenomenon using a magnetic dissipation synchrotron radiation model. In the decay phase of flare, the $E_p$-Flux and $E_p$-$α$ correlations become notably flatter, with their power-law indices decreasing significantly compared to those in the rise phase. This may be due to the fact that the next flare begins to erupt before the Brightest Flare has completely ended, resulting in the combined effects of both two flares. Our study of spectral parameter relations of the Brightest Flare provides new insights into the radiation mechanisms of both GRB prompt emission and flares.

astro-ph.HE↗
arXiv2026

DRL-driven Online Optimization for Joint Traffic Reshaping and Channel Reconfiguration in RIS-assisted Semantic NOMA Communications

This paper explores a reconfigurable intelligent surface (RIS)-assisted and semantic-aware wireless network, where multiple semantic users (SUs) transmit semantic information to an access point (AP) using the non-orthogonal multiple access (NOMA) method. The RIS reconfigures channel conditions, while semantic extraction reshapes traffic demands, providing enhanced control flexibility for NOMA transmissions. To enable efficient long-term resource allocation, we propose a deferrable semantic extraction scheme that can distribute the semantic extraction tasks across multiple time slots. We formulate a long-term energy efficiency maximization problem by jointly optimizing the RIS's passive beamforming, the SUs' semantic extraction, and the NOMA decoding order. Note that this problem involves multiple and coupled control variables, which can incur significant computational overhead in time-varying network environments. To support low-complexity online optimization, a deep reinforcement learning (DRL)-driven online optimization framework is developed. Specifically, the DRL module facilitates the adaptive selection and optimization of the most suitable option from traffic reshaping, channel reconfiguration, or NOMA decoding order assignment based on the dynamic network status. Numerical results demonstrate that the deferrable semantic extraction scheme significantly improves the long-term energy efficiency. Meanwhile, the DRL-driven online optimization framework effectively reduces the running time while maintaining superior learning performance compared to state-of-the-art methods.

cs.NI↗