arXiv subjects

Qiang Li

Publications and source records attributed to Qiang Li.

At least 37 records · Page 2Linked to original sources

arXiv2026

Symbol-Level Precoding for Continuous-Aperture ISAC Systems

Continuous-aperture arrays (CAPAs) offer rich electromagnetic degrees of freedom for integrated sensing and communication (ISAC), but optimizing continuous current distributions leads to challenging infinite-dimensional problems. This paper investigates a CAPA-enabled downlink ISAC system with symbol-level precoding and receive polarization combining. The transmit current is optimized to maximize weighted target-illumination power while enforcing constructive-interference constraints for communication users. To address the resulting infinite-dimensional nonconvex problem, we establish that the optimal current distribution lies in a finite-dimensional subspace spanned by the communication and sensing electromagnetic responses. This result yields an exact, structure-preserving reformulation in terms of finite-dimensional coefficients. A penalty projected-gradient algorithm is then developed to jointly optimize the current coefficients and polarization combiners. Simulation results demonstrate that the proposed framework achieves higher sensing utility and improved communication reliability than conventional Fourier-basis CAPA and spatially discrete array baselines.

eess.SP↗

arXiv2026

M3-Tele: A Unified Multimodal Teleoperational Framework for Compliant Whole-Body Mobile Manipulation

Executing contact-rich tasks efficiently requires the seamless integration of whole-body coordination and physical compliance regulation. However, existing teleoperation and data-collection frameworks often overlook the joint consideration of multimodal perception and coordinated whole-body operation. This limitation can reduce the efficiency and quality of demonstration collection, thereby affecting the effectiveness of downstream policy learning. In this work, we present \textbf{M3-Tele}: A Unified \underline{M}ultimodal \underline{Tele}operational Framework for Compliant Whole-Body \underline{M}obile \underline{M}anipulation, enabling stable physical interaction and capturing aligned visual, tactile, force, and proprioceptive observations during task execution. Extensive experiments demonstrate that the proposed framework significantly improves contact-rich teleoperation performance. The proposed controller reduces the force tracking error from 4.132~N to 0.346~N, the contact loss from 2.46 to 0.02 events per trial and the tactile deformation error by 65\%. User studies across four mobile manipulation tasks also verify the reliability and usability of the proposed system. Furthermore, Diffusion Policy experiments highlight the value of joint tactile and force sensing.

cs.RO↗

arXiv2026

Dalitz decays of vector heavy quarkonia into $χ_{QJ}(1P)$ in the Bethe-Salpeter approach

We systematically investigate the Dalitz decays of vector heavy quarkonia into $χ_{QJ}(1P)\ell^+\ell^-$ ($Q=c,b$; $J=0,1,2$; $\ell=e,μ$) within the instantaneous Bethe--Salpeter framework. The study covers $ψ(2S)$, $ψ(1D)$, $Υ(2S)$, and the so-far unobserved $Υ(1D)$ states. For $ψ(2S)$ electron channels, our predictions are in excellent agreement with BESIII data. We further provide the first relativistic predictions for $ψ(1D)$ decays, with branching ratios for several electron channels reaching the $10^{-5}$ level, which is accessible at current BESIII statistics. For bottomonium, $Υ(2S)\toχ_{bJ}e^+e^-$ decays yield branching fractions of $\mathcal{O}(10^{-4})$, suggesting potential observability at Belle~II. Muonic channels are also discussed, with kinematic constraints carefully addressed. Our results establish a coherent theoretical basis for future experimental searches for heavy quarkonium Dalitz decays.

hep-ph↗

arXiv2026

Projected Sensitivity to Slow Muonphilic Dark Matter with Accelerator Muon Beams

The nature of dark matter (DM) remains one of the most enduring open questions in modern physics, and muonphilic DM has emerged as a promising scenario that complements traditional DM candidates. Following the recently established cosmic-ray muon scattering approach, we investigate the sensitivity for probing slow muonphilic DM with accelerator muon beams. A Geant4-based simulation framework is developed, incorporating the detector geometry from the PKMu muon tomography system and a dedicated elastic $μ$-DM scattering process. The projected sensitivity is found to be largely insensitive to both the beam energy and the transverse beam size when the beam is fully contained within the detector acceptance. For a benchmark beam intensity of $10^5/\rm{s}$, the simulated pure-muon beam surpasses the existing cosmic-ray limit of $1.61\times10^{-17}$ cm$^2$ at $m_{\rm DM}=1$ GeV within approximately 11 seconds. A realistic muon beam phase-space distribution based on simulations for the High Intensity heavy-ion Accelerator Facility (HIAF) is also implemented, yielding projected limits that improve upon the cosmic-ray results by nearly two orders of magnitude in a one-day exposure. These results demonstrate that a beam-muon scattering experiment offers a robust and promising route toward significantly improved sensitivity to slow muonphilic DM.

hep-ex↗

arXiv2026

High-Order Triadic Functional Connectivity in the Brain and Beyond

Here, we report high-order functional network connectivity as a promising way for studying the brain connectome. Traditional functional connectivity approaches capture only pairwise relationships between brain regions, overlooking complex multivariate dependencies that underlie cognition and behavior. First, we demonstrated that high-order interactions capture more information and can distinguish between resting-state and task-state brain activity. Second, we introduce a matrix-based entropy-functional method for estimating triadic interactions, which are statistical dependencies among triplets of brain regions, and apply it to large-scale functional brain networks. The resulting triadic networks revealed distinct community patterns that complement those observed in traditional pairwise functional connectivity analyses and simultaneously capture additional connection information. Despite the potential combinatorial explosion of triadic configurations, the networks exhibited constrained and hierarchical structures that allowed computation and interpretation. These findings position triadic connectivity as a promising next-step functional connectivity framework for probing brain network organization and high-order neural interactions, while also highlighting key biological and technical challenges that require careful consideration.

q-bio.NC↗

arXiv2026

From Language to Behavior: Scaling Sequence Transformers for Industrial Recommendation Ranking with Rec-Native Designs

Scaling Transformers has driven large gains in language modeling, but transplanting this to behavior-sequence modeling in production ranking is challenging: recommendation differs in signal quality, where behavior sequences are noisy, temporally irregular, and sparsely supervised, and in computation asymmetry, where each request scores many candidates against one shared user history under tight latency budgets. We propose ReST, a recommendation-native Transformer scaling framework. For signal quality, it introduces a sequence encoder with dual-gated attention, rotary positional and temporal embedding, stabilized residual normalization, and training-only auxiliary objectives. For computation asymmetry, it factorizes ranking into a heavy reusable encoder and a lightweight cross decoder with projection-free KV attention and token-specific parameterization, coupling user-level shared-prefix training with shared-prefix serving for compute-once, decode-many-times ranking. Across industrial and public benchmarks, ReST achieves higher accuracy and scales more consistently along sequence length, depth, and width, where LLM-style Transformer blocks saturate. A one-week online A/B test on a production advertising platform improves online AUC by 1.31% and lifts a core revenue metric by 11.93% within a 50 ms P99 budget; ReST has since been fully deployed in production, showing that behavior-sequence scaling remains a promising, under-exploited axis for production ranking.

cs.IR↗

arXiv2026

HybridWorldSim: A Scalable and Controllable High-fidelity Simulator for Autonomous Driving

Realistic and controllable simulation is critical for advancing end-to-end autonomous driving, yet existing approaches often struggle to support novel view synthesis under large viewpoint changes or to ensure geometric consistency. We introduce HybridWorldSim, a hybrid simulation framework that integrates multi-traversal neural reconstruction for static backgrounds with generative modeling for dynamic agents. This unified design addresses key limitations of previous methods, enabling the creation of diverse and high-fidelity driving scenarios with reliable visual and spatial consistency. To facilitate robust benchmarking, we further release a new multi-traversal dataset MIRROR that captures a wide range of routes and environmental conditions across different cities. Extensive experiments demonstrate that HybridWorldSim surpasses previous state-of-the-art methods, providing a practical and scalable solution for high-fidelity simulation and a valuable resource for research and development in autonomous driving.

cs.CV↗

arXiv2026

CANTON-$μ$ Proposal: A Next-Generation Muon $g-2$ Measurement at Sub-0.1 ppm Precision

We propose a next-generation precision measurement of the muon anomalous magnetic moment (muon g-2) at the High Intensity Heavy-Ion Accelerator Facility (HIAF) in Huizhou, China. We refer to this proposed experimental programme as CANTON-$μ$ (Coherent Anomalous magNetic momenT ObservatioN with muon). HIAF's intense, pulsed GeV-scale muon beams, particularly for negative muons, provide a promising basis for this programme. Building on two previously proposed storage-ring concepts, this work develops HIAF-specific experimental schemes that relax the conventional magic-momentum constraint and allow greater flexibility in the choice of beam momentum. We assess the expected muon intensity at HIAF and the corresponding statistical sensitivity. For each scheme, we identify its distinctive systematic effects, examine feasible control strategies, and propose quantitative systematic-uncertainty targets. Together, the statistical projections and systematic-uncertainty targets indicate prospective total precisions of 0.1 ppm in Phase-I, comparable to the current Fermilab precision, with the potential to reach the 0.05 ppm level in in Phase-II following the planned HIAF upgrade. At the ultimate projected precision, the measurement would provide a stringent test of the Standard Model and probe new physics at multi-TeV scales. A focus on negative-muon measurements would enable direct comparison with existing high-precision positive-muon results and strengthen tests of CPT symmetry in the muon sector within the Standard-Model Extension, with a projected sensitivity at the $10^{-24}$ GeV level, an order of magnitude beyond current limits.

hep-ex↗

arXiv2026

Industrial Practice of LLM-Based Test Case Carving and Assertion Generation (Experience Paper)

Enterprise regression testing for microservice systems is often constrained by incomplete or outdated documentation. In practice, QA engineers frequently rely on real execution traffic to reconstruct business scenarios; however, turning raw traffic into replayable regression tests with stable validation logic remains labor-intensive and error-prone. This paper presents NL2Test, an end-to-end approach and tool that generates executable API regression tests from (i) a natural-language scenario description and (ii) a traffic capture recorded while executing the scenario. NL2Test addresses two coupled tasks: test case carving, which extracts a minimal replayable request sequence and reconstructs data dependencies so that dynamic values are bound from their responses rather than hard-coded; and assertion generation, which produces assertions aligned with business intent while avoiding non-deterministic fields and hallucinated paths. To improve reliability, NL2Test uses LLMs for semantic interpretation and constrained code synthesis, and uses deterministic algorithms for request filtering, dependency confirmation via value consistency, and assertion-path validation. We evaluate NL2Test on 51 industrial regression scenarios extracted from a large consumer-facing Internet company. NL2Test achieves an exact-match rate of 82.4% (42/51), and produces a functionally usable draft in 98.0% (50/51) of scenarios when allowing minor post-edits. In a 9-month production deployment starting in March 2025, NL2Test generated 3,196 test cases with an overall code adoption rate of 85.4%. These results indicate that traffic-grounded generation with deterministic guardrails can substantially reduce manual effort while improving regression automation in complex microservice environments.

cs.SE↗

arXiv2026

The muon Moonshot: Moon subsurface tomography with upward-going muons

We propose a novel muon Moonshot concept for lunar subsurface tomography based on upward-going muons originated from the lunar regolith. Unlike the Earth, the Moon lacks an atmosphere, leaving a dense regolith below and a near-vacuum environment above. Consequently, while most downward-going hadrons are absorbed before decaying, upward-going hadrons escaping the regolith can decay in flight, producing a significant source of lunar muons. These muons are detectable by instruments on the lunar surface or in near-lunar orbit. We perform Monte Carlo simulations to investigate their energy spectra, angular distributions, and integrated fluxes under various theoretical and detector configurations. The results indicate that the lunar muon flux is sensitive to detector altitude under a flat-terrain assumption, demonstrating its potential as a novel non-invasive probe of shallow subsurface voids. We also present case studies on the detection of underground cavities and water resources, with cavity-induced flux variations observable in less than two minutes and weaker water signals distinguishable after about 36 minutes of data collection with a $1~\mathrm{m^2}$ detector, and discuss potential implementations in future lunar missions.

astro-ph.EP↗

arXiv2026

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action prediction. However, robotic manipulation is composed of many frequent closed-loop spatial adjustments, for which excessive abstraction may waste computation and weaken low-level geometric cues essential for precise control. Existing early-exit strategies attempt to reduce computation by stopping at predefined layers or applying heuristic rules such as action consistency, but they do not directly answer when a representation is actually sufficient for action. In this paper, we present LoopVLA, a recurrent VLA architecture that jointly learns representation refinement, action prediction, and sufficiency estimation. LoopVLA iteratively applies a shared Transformer block to refine multimodal tokens, and at each iteration produces both a candidate action and a sufficiency score that estimates whether further refinement is necessary. By sharing parameters across iterations, LoopVLA decouples refinement from absolute layer indices and grounds sufficiency estimation in the evolving representation itself. Since sufficiency has no direct supervision, we introduce a self-supervised distribution alignment objective, where intermediate confidence scores are trained to match the relative action quality across refinement steps, thereby linking sufficiency learning to policy optimization signals. Experiments on LIBERO, LIBERO-Plus, and VLA-Arena show that LoopVLA pushes the efficiency-performance frontier of VLA policies, reducing parameters by 45% and improving inference throughput by up to 1.7 times while matching or outperforming strong baselines in task success.

cs.AI↗

arXiv2026

Marionette: Predicting World States, Rendering Geometry, Painting Appearance

Interactive game world models typically autoregress visual observations directly in pixel or latent space, forcing structured properties such as pose, geometry, and occlusion to be implicitly maintained by the same generative sequence. Over long horizons, errors in these latent world properties accumulate, making consistency and controllability fragile. We explicitly model the evolving world state, delegate exact geometric computation to a fixed, zero-parameter renderer, and leave the neural model to synthesize appearance. We instantiate this idea as Marionette, a world model for interactive games with articulated characters. First, a two-stage autoregressive dynamics model predicts an explicit and interpretable 276-dimensional 3D world state comprising multi-entity articulated skeletons, metric root trajectories, and rotations. Second, a zero-parameter graphics bridge converts the predicted state into pose-control videos, computing world-space geometry and occlusion in closed form. Third, a control-conditioned video-diffusion observation model synthesizes photorealistic RGB observations from the resulting structured controls. Our experiments establish two properties of Marionette. First, the predicted world state is directly controllable. Forcing a mismatched action stream changes root-aligned joint error by 31% across 48 held-out segments. Second, long-horizon behaviour is determined in the state, and can be repaired there. Left free, the two generated characters drift to 21.2 m apart (recorded sessions stay near 5 m) and a third of frames show ground penetration. Two rules imposed on the explicit state, a terrain collider and a separation cap, cut penetration by 66% and keep the pair engaged, with no change to the observation model. Routing appearance through the predicted state costs no fidelity we can detect, at an FVD of 831 against 799 for recorded pose.

cs.CV↗

arXiv2026

HiCo-GS: Hierarchical Context Aggregation and Geometric Consistency for Octree Gaussian Splatting

Octree-based anchor Gaussian Splatting has emerged as a scalable representation for city-scale novel view synthesis, where multi-level anchors adaptively capture scene content from coarse building structures to fine architectural details. However, we identify a fundamental limitation in existing methods: cross-level feature isolation, where each level's anchor features are optimized independently with no inter-level communication, causing color drift on building facades and over-smoothing in textured regions. We present HiCo-GS, a high-fidelity reconstruction framework with two complementary modules. Cross-Level Context Aggregation (CLCA) enables bidirectional hierarchical prior injection by leveraging the octree's spatial containment structure to aggregate per-level context vectors into parent-self-child triplets, fused via a lightweight MLP with residual connection. Coarse-level structural priors flow down to inform fine-level anchors, while fine-level detail statistics feed back to prevent over-smoothing, at negligible computational overhead. Depth-Normal Geometric Consistency (DNGC) regularization enforces agreement between rendered normals and depth-derived normals through an alpha-weighted consistency loss, complemented by edge-aware smoothness losses with progressive warmup that exploit the strong planar priors ubiquitous in urban geometry to suppress floating artifacts. We further introduce the China-Pagoda dataset comprising 8 ancient Chinese pagodas with over 1,200 images each, featuring dense ornamental carvings, curved multi-layer eaves, and repetitive fine-grained textures. Extensive experiments on Mill19, UrbanScene3D, MatrixCity, and China-Pagoda demonstrate that HiCo-GS achieves state-of-the-art rendering quality and substantially cleaner geometry across real-world and synthetic urban benchmarks.Code: https://github.com/WZ-CS/HiCo-GS.

cs.CV↗

arXiv2026

Linguistically-Aligned and Visually-Grounded Preference Optimization for Clinically-Augmented Medical Report Generation

Despite significant advances in Medical Report Generation (MRG), the reliability remains constrained by the prevalence of factual errors. While Direct Preference Optimization (DPO) has emerged as a promising post-training paradigm to enhance the performance of Supervised Fine-Tuned (SFT) MRG models, existing DPO-based MRG methods typically adopt a naive preference construction that directly pairs model-generated reports with ground truth reports. This strategy inadvertently entangles critical clinical findings with clinically irrelevant linguistic characteristics, and fundamentally lacks explicit vision-language alignment. To address these challenges, we propose DPO-Clin, a novel post-training framework that focuses preference optimization on clinical findings and cross-modal alignment. First, we introduce the Entity-level Clinical Diagnostic (ECD) module to perform a precise entity-level factual diagnosis. ECD guides the generation of linguistically-aligned report preference pairs, isolating clinical discrepancies from linguistic variations. Second, to achieve fine-grained cross-modal alignment, we develop M2DPO, a retrieval-augmented multi-modal DPO variant that enforces textual preference inversion triggered by visual context switches. Third, we locate correct yet highly uncertain predicted entities and apply counterfactual modifications to construct targeted preference data for latent risk mitigation, thereby further enhancing the model reliability. Extensive experiments on two public chest X-ray datasets (MIMIC-CXR and IU X-Ray) and an in-house endoscopy dataset demonstrate that DPO-Clin significantly improves the SFT baselines on clinical-aware metrics. Furthermore, it achieves superior performance over existing DPO-based MRG methods, exhibiting robust generalizability across distinct baseline architectures and diverse medical imaging modalities.

cs.CV↗

arXiv2026

Terahertz Channel Measurement and Modeling for Short-Range Indoor Environments

Accurate channel modeling is essential for realizing the potential of terahertz (THz) communications in 6G indoor networks, where existing models struggle with severe frequency selectivity and multipath effects. We propose a physically grounded Rician fading channel model that jointly incorporates deterministic line-of-sight (LOS) and stochastic non-line-of-sight (NLOS) components, enhanced by frequency-dependent attenuation characterized by optimized exponents alpha and beta. Unlike conventional approaches, our model integrates a two-ray reflection framework to capture standing wave phenomena and employs wideband spectral averaging to mitigate frequency selectivity over bandwidths up to 15 GHz. Empirical measurements at a 208 GHz carrier, spanning 0.1-0.9 m, demonstrate that our model achieves root mean square errors (RMSE) as low as 2.54 dB, outperforming free-space path loss (FSPL) by up to 14.2% and reducing RMSE by 73.3% as bandwidth increases. These findings underscore the importance of bandwidth in suppressing oscillatory artifacts and improving modeling accuracy. Our approach provides a robust foundation for THz system design, supporting reliable indoor wireless personal area networks (WPANs), device-to-device (D2D) communications, and precise localization in future 6G applications.

eess.SP↗

arXiv2026

Ultralow p-type contact resistance for ultra-nanoscaled 2D-materials transistors

High contact resistance is one of the main bottlenecks for practical two-dimensional (2D) materials transistors, especially for p-type transistors and future 2D ultra-nanoscaled (sub-10 nm) FETs (PMOS + CMOS). We develop self-consistent contact resistance models for metal-2D semiconductor-metal devices to capture the essential interface physics for both vertical and edge configurations. Our calculations have been verified with various recent experiments of p-type and n-type contacts. For a given set of materials, the model determines the scaling of contact resistance over a wide range of device parameters including channel length (100s nm down to sub-10 nm), doping and mobility of the 2D materials, contact length of the electrodes, and applied voltages. These results identify the key factors in order to reduce the contact resistance for p-type 2D semiconductor WSe$_2$ towards the sub-10 nm channel length scale that are readily to be realized by future experiments. It is found that the effect of source-limited current saturation is the key challenge for down scaling 2D FET to sub-10 nm channel length. Two topological semi-metals as potential electrodes are proposed for 2D p-type semiconducting WSe$_2$ with our predicted contact resistance $R_c<$ 100 $Ω\; {\rm μm}$ approaching the quantum limit. Our model is also verified with the computational expensive full quantum atomistic model that is currently limited to a few nm scale.

cond-mat.mes-hall↗

arXiv2026

Projection-shifted particle-flow imaging with cosmic-ray muons

Cosmic-ray muons are natural probes for non-destructive imaging, but reaching sub-millimetre resolution with realistic exposure times has long been hindered by a fundamental limitation: the stochastic nature of multiple Coulomb scattering defies deterministic reconstruction of particle trajectories inside matter. Here we introduce Projection-shifted MUon transMission tomogrAphy (P$μ$MA). Rather than localizing individual scattering points, P$μ$MA records how material perturbations statistically shift the projected transmission tracks of a muon flux onto a virtual imaging plane. Near density boundaries, asymmetric projection-shift statistics naturally produce steep undershoot-overshoot profiles, sharpening edges without additional filtering. Crucially, the method operates robustly even with only two detector planes, a configuration where conventional scattering tomography cannot be applied. Cosmic-ray simulations with a lead knife-edge target yield edge widths as narrow as 1.196 mm, while monoenergetic-beam simulations reach 48 $μ$m. Using a prototype system, we resolve 2-mm copper letters within two days---a feat unattainable by standard approaches under equivalent conditions. The projection-shift concept can be extended to accelerator- or laser-driven muon beams and to other ions, establishing a generalisable strategy for high-resolution particle-flow imaging.

physics.ins-det↗

arXiv2026

$S-P-D$ Mixing in Vector Quarkonia from the Salpeter Equation with Optimized Wave Function Representations

This paper proposes a novel mechanism based on the instantaneous Bethe-Salpeter (Salpeter) equation for investigating wave function mixing in vector mesons such as $ψ(3770)$. Conventional theories typically treat $ψ(3770)$ as a $2S-1D$ mixed state; however, considering only tensor forces or relativistic corrections alone often leads to mixing angles that are too small and inconsistent with experimental data. Phenomenological $2S-1D$ mixing requires experimental data as input to determine the mixing angles, resulting in limited theoretical studies on states like $Υ(1D, 2D)$ in the absence of experimental data. To more accurately describe $S-D$ mixing and its relativistic effects, this paper systematically compares four relativistic wave function representations ($φ_1$, $φ_2$, $φ_3$, and $φ_4$) by solving the Salpeter equation and calculates the mass spectra and dileptonic decay widths of charmonium and bottomonium. The study finds that the wave function representation $φ_2$ can simultaneously reproduce the experimental data of both charmonium and bottomonium well. Further analysis reveals that, in addition to $S-D$ mixing, the wave functions of vector mesons contain a non-negligible $P$-wave component, meaning they are $S-P-D$ mixed states. We predict the mixing angles for bottomonium $Υ(1D)$ and $Υ(2D)$ to be $(1.78^{+0.32}_{-0.25})^\circ$ and $(5.44^{+1.10}_{-0.76})^\circ$, with dileptonic decay widths of $2.29^{+0.86}_{-0.69}$ eV and $10.5^{+4.2}_{-3.1}$ eV, respectively.

hep-ph↗