arXiv subjects

Zhiyang Liu

Publications and source records attributed to Zhiyang Liu.

At least 19 recordsLinked to original sources

arXiv2026

Burst-mode timing recovery based on fourth-power phase detector for passive optical networks

Driven by the ever-increasing capacity demands, 50G passive optical network (50G-PON) is ready for practical application. It is highly challenging to realize 50GHz burst-mode analog components; therefore, based on 25GHz burst-mode analog devices, burst-mode digital signal processing (DSP) is introduced to achieve the reception and processing of 50Gb/s on-off keying burst signals. To optimize the power consumption and area of the DSP chip, a one-sample-per-symbol (1-SPS) analog-to-digital converter has been applied in 50G-PON. One of the main challenges is implementing burst-mode timing recovery (BM-TR) for the 1-SPS burst signal in 50G-PON. In this paper, we first propose a BM-TR based on the fourth-power phase detector (4PPD) for 50G-PON. We mathematically verify that 4PPD can directly compute the timing phase offset (TPO) from the 1-SPS signal without using training sequences, allowing for immediate BM-TR initialization within 20 cycles to prevent long convergence times. After the initialization, the feedback loop structure tracks the TPO changes based on the sign of 4PPD, the loop filter, and the numerically controlled oscillator. In conclusion, training-sequence-free 4PPD-based BM-TR achieves low burst overhead via fast convergence and is particularly effective for handling burst signals in 50G-PON.

cs.NI↗

arXiv2026

Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey

Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditional rule-based methods fail to scale or generalize in unstructured, novel environments. In recent years, Vision-Language-Action (VLA) models, built upon Large Vision-Language Models (VLMs) pretrained on vast image-text datasets, have emerged as a transformative paradigm. This survey provides the first systematic, taxonomy-oriented review of large VLM-based VLA models for robotic manipulation. We begin by clearly defining large VLM-based VLA models and delineating two principal architectural paradigms: (1) monolithic models, encompassing single-system and dual-system designs with differing levels of integration; and (2) hierarchical models, which explicitly decouple planning from execution via interpretable intermediate representations. Building on this foundation, we present an in-depth examination of large VLM-based VLA models: (1) integration with advanced domains, including reinforcement learning, training-free optimization, learning from human videos, and world model integration; (2) synthesis of distinctive characteristics, consolidating architectural traits, operational strengths, and the datasets and benchmarks that support their development; (3) identification of promising directions, including memory mechanisms, 4D perception, efficient adaptation, multi-agent cooperation, and other emerging capabilities. This survey consolidates recent advances to resolve inconsistencies in existing taxonomies, mitigate research fragmentation, and fill a critical gap through the systematic integration of studies at the intersection of large VLMs and robotic manipulation. We provide a regularly updated project page to document ongoing progress: https://github.com/JiuTian-VL/Large-VLM-based-VLA-for-Robotic-Manipulation

cs.RO↗

arXiv2026

$q\bar{q}$ scattering phase shift in the $π^0$ channel and $π^0$ meson spectral function under external magnetic field and finite meson momentum

$q\bar{q}$ scattering phase shift in the $π^0$ channel $Φ_{π^0}(ω^2,\mathbf{k}_\perp^2,k^2_3)$ and $π^0$ meson spectral function $ρ_{π^0}(ω^2,\mathbf{k}_\perp^2,k^2_3)$ under external magnetic field $eB$ and finite meson momentum $\mathbf{k}_\perp^2,k^2_3$ are studied in the framework of a two-flavor Nambu-Jona-Lasinio (NJL) model. The $q\bar{q}$ scattering phase shift in the $π^0$ channel $Φ_{π^0}$ is closely related to $π^0$ spectral function $ρ_{π^0}$. We consider three situations, chiral broken phase ($T=μ=0$), chiral restoration phase ($T>T_{pc},\ μ=0$) and chiral restoration phase ($T=0,\ μ>μ_{pc}$). For $T=μ=0$ and $T>T_{pc},\ μ=0$ cases, $π^0$ meson spectral function $ρ_{π^0}$ shows a delta peak, several Breit-Wigner peaks and several non-Breit-Wigner peaks. The delta peak indicates the bound state of $π^0$ meson, and the Breit-Wigner peak means the resonant state of $π^0$ meson. For $T=0,\ μ>μ_{pc}$ case, Pauli blocking effect plays a role, which changes the inner structure of these Breit-Wigner peaks and non-Breit-Wigner peaks. Such multiple peak structure is caused by the external magnetic field. The $q\bar{q}$ scattering phase shift in the $π^0$ channel $Φ_{π^0}$ shows a jump from $0$ to $π$ when $π^0$ meson is in bound state. When $π^0$ meson is in resonant state, $Φ_{π^0}$ has the value $π/2$ and changes continuously. In large $ω$ region, at the starting and end points of wide peaks of spectral function, $Φ_{π^0}$ jumps abruptly (from $π$ to finite value or from finite value to $0$), and such jumps are caused by the external magnetic field. Finite momentum $\mathbf{k}_\perp^2$ or $k^2_3$ modifies the spectral function $ρ_{π^0}$ and scattering phase shift $Φ_{π^0}$, which demonstrates the anisotropy in the system induced by external magnetic field.

hep-ph↗

arXiv2026

Fine-Tuning Small Language Models for Reliable VASP INCAR Generation

Language models can prepare VASP INCAR files from natural-language requests, but so far only large proprietary cloud models come close to handling the tightly coupled, physics-sensitive settings reliably, a dependence that fits poorly with local, high-throughput materials workflows where privacy, cost, and offline deployment matter. We show that a small language model (SLM) can close this gap. The SLM is fine-tuned on reference VASP calculations and paired with VASPGuard, a deterministic post-processor that checks syntax, workflow, and material-dependent constraints; we call the combined model INCAR-SLM. On INCARBench, a benchmark for VASP INCAR generation, INCAR-SLM built on Qwen3-4B outperforms every general-purpose LLM evaluated, exceeding GPT-5.4 by 15.55 points on the 100-point INCAR Score. Most of this gain comes from fine-tuning, with VASPGuard correcting the errors that remain. We further find that model size matters less than expected: once fine-tuning and post-processing are applied, performance saturates at a few billion parameters, and Qwen3-4B outperforms larger models in the same family.

cond-mat.mtrl-sci↗

arXiv2026

$ρ$ mesons in finite magnetic field and finite temperature

The mass spectra of $ρ$ mesons ($ρ_{Q=\pm 1}^{s_z=0,\pm 1}$ and $ρ_{Q=0}^{s_z=0,\pm 1}$) at finite magnetic field and temperature are studied in frame of the two-flavor Nambu-Jona-Lasinio model. Fully considering the breaking of translational invariance induced by external magnetic field, the analytical form of $ρ$ meson propagators have been derived in the Ritus scheme and Schwinger scheme, which gives the same algebraic formula. When solving the pole equation of $ρ$ meson propagators, multiple solutions of the meson mass appear due to the dimension reduction of their constituent quarks in magnetic fields. At vanishing temperature, we focus on the $ρ$ meson masses $M_ρ$ corresponding to the lowest value solution of the pole equation. $M_{ρ^{-}_+}$, $M_{ρ^{0}_+}$ and $M_{ρ^{\pm}_0}$ increase with magnetic field. $M_{ρ^{+}_+}$ firstly decreases and then becomes saturated with increasing magnetic field. $M_{ρ^0_0}$ is not sensitive to magnetic field. These results are consistent with the available LQCD simulations. At finite temperature, we discuss the lowest four/five solutions of $ρ$ meson masses $M^{i=0,1,2,3,4}_ρ$. With fixed magnetic field, they decrease with temperature, and approach the mass sum of their constituent quarks at high temperature. The mass solution $M^{i}_ρ$ for different mesons $ρ_+^{0,\pm}$ and $ρ_0^{0,\pm}$ may become degenerate at finite magnetic field and temperature.

nucl-th↗

arXiv2026

INCARBench: A Benchmark for Scientific Configuration in VASP INCAR by Large Language Models

Large language models (LLMs) are increasingly being integrated into first-principles computational workflows, yet their ability to configure scientific calculations remains poorly understood. Here, we introduce INCARBench, a benchmark for evaluating LLMs on input configuration for the Vienna Ab initio Simulation Package (VASP) through both configuration generation and repair tasks. Evaluating 19 model configurations reveals substantial capability differences among current frontier models. While several models achieve high semantic and policy accuracy, task-critical correctness remains substantially lower, demonstrating that parameter-level correctness does not necessarily imply scientifically valid configurations. Failure analysis shows that errors concentrate in physically coupled settings involving DFT+$U$, magnetism, and correlated materials, where multiple constraints must be satisfied simultaneously. Repair evaluation further reveals that correcting incorrect settings and preserving already-valid configurations are distinct capabilities, with configuration preservation remaining a major challenge. These findings establish scientific configuration as a measurable capability of large language models and provide a foundation for developing more reliable AI systems for computational materials science.

cond-mat.mtrl-sci↗

arXiv2026

Rethinking 3D Shape Generation: Diffusion over Superquadrics

Diffusion models have advanced 3D shape generation, yet most methods still denoise in high-cardinality spaces (e.g., voxel/SDF grids, meshes, or point clouds), which is computationally and memory intensive and makes it difficult to scale in terms of both higher resolution and stronger controllability. We rethink the diffusion representation and propose to move diffusion from dense geometry to compact geometric primitives, representing each shape as a small set of superquadrics. Instead of operating on thousands to millions of geometric representation values, we leverage 7KB superquadric parameters (pose, size, and shape), drastically reducing diffusion-state dimensionality and per-step compute/memory. Our diffusion-over-superquadrics improves scalability by supporting broader capabilities (e.g., resolution-free point-cloud decoding, part-level editing, and constraint-based design) and achieving competitive surface-fidelity and distributional performance on standard benchmarks after point-cloud decoding, while enabling efficient generation within 0.6s per shape for most conditions.

cs.CV↗

arXiv2026

NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving

Recent perception-free end-to-end (E2E) autonomous driving methods bypass explicit perception outputs by compressing dense image patch tokens into compact scene tokens for downstream trajectory generation and scoring. While these scene tokens form a compact visual bottleneck for the planner, they receive supervision solely from the planning objective, providing limited constraints on the encoded visual information. To address this limitation, we introduce Neural Token Reconstruction (NTR), a representation learning framework to directly constrain the compact scene-token bottleneck in perception-free driving. NTR introduces a self-distillation masked latent reconstruction objective that reconstructs masked patch-level latent features using only compact scene tokens as reconstruction memory. This forces reconstruction gradients to pass exclusively through the scene-token bottleneck, encouraging scene tokens to preserve richer and less redundant visual representations for planning. We further introduce semantic priors derived from foundation-model annotations as a weak semantic interface biasing reconstruction targets toward driving-related structures without introducing explicit perception heads. All auxiliary reconstruction components are removed at inference time, leaving the deployed planner unchanged. NTR achieves state-of-the-art performance on three public autonomous driving benchmarks, including 8.0461 RFS on Waymo E2E and 94.1 PDMS / 90.9 EPDMS on NavSim1&2. The learned scene tokens exhibit lower pairwise redundancy and higher effective rank, indicating that effective bottleneck supervision improves both compact visual representation learning and planning performance.

cs.CV↗

arXiv2026

3D Reconstruction Techniques in the Manufacturing Domain: Applications, Research Opportunities and Use Cases

This comprehensive review examines the evolution and the current state of the art in three-dimensional (3D) reconstruction techniques in manufacturing applications. The analysis covers both traditional approaches and emerging deep learning methods, showing a critical research gap in unified 3d reconstruction frameworks. Through systematic review of 106 recent publications, we classify reconstruction techniques into three primary categories: data acquisition, point cloud generation, post-processing and applications. Non-contact methods, particularly structured light scanning and stereo vision, have shown significant adoption in manufacturing, with 47% of surveyed applications focusing on quality inspection. The integration of deep learning has enhanced reconstruction accuracy and processing speed, particularly in feature extraction and matching. Key applications span design and development (13%), machining (8%), process (17%), assembly (22%), and quality inspection (40%). While current technologies achieve sub-millimeter accuracy in controlled environments, challenges persist in handling reflective surfaces and dynamic environments. Our findings indicate a trend toward hybrid systems combining multiple sensor types and processing methods to overcome individual limitations. This survey provides a structured framework for understanding current capabilities and future directions in manufacturing-focused 3D reconstruction.

cs.CV↗

arXiv2026

Precise Aggressive Aerial Maneuvers with Sensorimotor Policies

Precise aggressive maneuvers with lightweight onboard sensors remains a key bottleneck in fully exploiting the maneuverability of drones. Such maneuvers are critical for expanding the systems' accessible area by navigating through narrow openings in the environment. Among the most relevant problems, a representative one is aggressive traversal through narrow gaps with quadrotors under SE(3) constraints, which require the quadrotors to leverage a momentary tilted attitude and the asymmetry of the airframe to navigate through gaps. In this paper, we achieve such maneuvers by developing sensorimotor policies directly mapping onboard vision and proprioception into low-level control commands. The policies are trained using reinforcement learning (RL) with end-to-end policy distillation in simulation. We mitigate the fundamental hardness of model-free RL's exploration on the restricted solution space with an initialization strategy leveraging trajectories generated by a model-based planner. Careful sim-to-real design allows the policy to control a quadrotor through narrow gaps with low clearances and high repeatability. For instance, the proposed method enables a quadrotor to navigate a rectangular gap at a 5 cm clearance, tilted at up to 90-degree orientation, without knowledge of the gap's position or orientation. Without training on dynamic gaps, the policy can reactively servo the quadrotor to traverse through a moving gap. The proposed method is also validated by training and deploying policies on challenging tracks of narrow gaps placed closely. The flexibility of the policy learning method is demonstrated by developing policies for geometrically diverse gaps, without relying on manually defined traversal poses and visual features.

cs.RO↗

arXiv2026

NavDreamer: Video Models as Zero-Shot 3D Navigators

Previous Vision-Language-Action models face critical limitations in navigation: scarce, diverse data from labor-intensive collection and static representations that fail to capture temporal dynamics and physical laws. We propose NavDreamer, a video-based framework for 3D navigation that leverages generative video models as a universal interface between language instructions and navigation trajectories. Our main hypothesis is that video's ability to encode spatiotemporal information and physical dynamics, combined with internet-scale availability, enables strong zero-shot generalization in navigation. To mitigate the stochasticity of generative predictions, we introduce a sampling-based optimization method that utilizes a VLM for trajectory scoring and selection. An inverse dynamics model is employed to decode executable waypoints from generated video plans for navigation. To systematically evaluate this paradigm in several video model backbones, we introduce a comprehensive benchmark covering object navigation, precise navigation, spatial grounding, language control, and scene reasoning. Extensive experiments demonstrate robust generalization across novel objects and unseen environments, with ablation studies revealing that navigation's high-level decision-making nature makes it particularly suited for video-based planning.

cs.RO↗

arXiv2026

USS-Nav: Unified Spatio-Semantic Scene Graph for Lightweight UAV Zero-Shot Object Navigation

Zero-Shot Object Navigation in unknown environments poses significant challenges for Unmanned Aerial Vehicles (UAVs) due to the conflict between high-level semantic reasoning requirements and limited onboard computational resources. To address this, we present USS-Nav, a lightweight framework that incrementally constructs a Unified Spatio-Semantic scene graph and enables efficient Large Language Model (LLM)-augmented Zero-Shot Object Navigation in unknown environments. Specifically, we introduce an incremental Spatial Connectivity Graph generation method utilizing polyhedral expansion to capture global geometric topology, which is dynamically partitioned into semantic regions via graph clustering. Concurrently, open-vocabulary object semantics are instantiated and anchored to this topology to form a hierarchical environmental representation. Leveraging this hierarchical structure, we present a coarse-to-fine exploration strategy: LLM grounded in the scene graph's semantics to determine global target regions, while a local planner optimizes frontier coverage based on information gain. Experimental results demonstrate that our framework outperforms state-of-the-art methods in terms of computational efficiency and real-time update frequency (15 Hz) on a resource-constrained platform. Furthermore, ablation studies confirm the effectiveness of our framework, showing substantial improvements in Success weighted by Path Length (SPL). The source code will be made publicly available to foster further research.

cs.RO↗

arXiv2026

Mass spectra and Mott transitions of neutral mesons at finite temperature and magnetic field in frame of three-flavor Polyakov-extended Nambu-Jona-Lasino model

Mass spectra and Mott transitions of neutral mesons $K_0,{\bar K}_0,π_0,η,η'$ at finite temperature and magnetic field are investigated in a three-flavor PNJL model. We focus on the effect of gluons, which is simulated by the Polyakov potential, and the inverse magnetic catalysis (IMC) effect, which is mimicked by using a magnetic field dependent parameter. Mass spectra show similar structure when introducing the gluon and IMC effect. The mass of $K_0\ ({\bar K}_0)$ meson $m_{K_0}=m_{{\bar K}_0}$ is controlled by chiral symmetry breaking and restoration. It increases with temperature in the low temperature region, and shows a mass jump at the Mott transition. Further increasing temperature, $m_{K_0}$ firstly decreases and then increases with temperature. $π_0$ meson is not only the pseudo-Goldstone boson of chiral symmetry breaking, but also influenced by the flavor mixing of $π_0-η-η'$. The behavior of $m_{π_0}$ is different from $m_{K_0}$ only at high temperature region, which decreases with temperature. $η,η'$ mesons are affected by both the $U_A(1)$ anomaly and the flavor mixing of $π_0-η-η'$. The mass of $η$ meson $m_η$ decreases with temperature in low temperature region and then shows a jump at its Mott transition. After that $m_η$ firstly decreases and later increases with temperature. $η'$ meson is a resonant state, and its mass $m_{η'}$ continuously decreases and then increases with temperature. The mass jumps of $K_0,{\bar K}_0,π_0,η$ mesons are caused by the dimension reduction of the constituent quarks under external magnetic field. In PNJL model, the Mott transition temperature of $K_0,{\bar K}_0,π_0$ mesons ($η$ meson) decreases (increases) with magnetic field. The IMC effect leads to no qualitative change to the meson Mott transition temperature but shifts them to the lower values.

hep-ph↗

arXiv2025

3D Affordance Keypoint Detection for Robotic Manipulation

This paper presents a novel approach for affordance-informed robotic manipulation by introducing 3D keypoints to enhance the understanding of object parts' functionality. The proposed approach provides direct information about what the potential use of objects is, as well as guidance on where and how a manipulator should engage, whereas conventional methods treat affordance detection as a semantic segmentation task, focusing solely on answering the what question. To address this gap, we propose a Fusion-based Affordance Keypoint Network (FAKP-Net) by introducing 3D keypoint quadruplet that harnesses the synergistic potential of RGB and Depth image to provide information on execution position, direction, and extent. Benchmark testing demonstrates that FAKP-Net outperforms existing models by significant margins in affordance segmentation task and keypoint detection task. Real-world experiments also showcase the reliability of our method in accomplishing manipulation tasks with previously unseen objects.

cs.RO↗

arXiv2025

Machine learning accelerates fuel cell life testing

Accelerated life testing (ALT) can significantly reduce the economic, time, and labor costs of life testing in the process of equipment, device, and material research and development (R&D), and improve R&D efficiency. This paper proposes a performance characterization data prediction (PCDP) method and a life prediction-driven ALT (LP-ALT) method to accelerate the life test of polymer electrolyte membrane fuel cells (PEMFCs). The PCDP method can accurately predict different PCD using only four impedances (real and imaginary) corresponding to a high frequency and a medium frequency, greatly shortening the measurement time of offline PCD and reducing the difficulty of life testing. The test results on an open source life test dataset containing 42 PEMFCs show that compared with the determination coefficient (R^2) results of predicted aging indicators, including limiting current, total mass transport resistance, electrochemically active surface area, and crossover current, obtained based on the measured PCD, the R^2 results of predicted aging indicators based on the predicted PCD is only reduced by 0.04, 0.01, 0.05, and 0.06, respectively. The LP-ALT method can shorten the life test time through early life prediction. Test results on the same open-source life test dataset of PEMFCs show that the acceleration ratio of the LP-ALT method can reach 30 times under the premise of ensuring that the minimum R^2 of the prediction results of different aging indicators, including limiting current, total mass transport resistance, and electrochemically active surface area, is not less than 0.89. Combining the different performance characterization data predicted by the PCDP method and the life prediction of the LP-ALT method, the diagnosis and prognosis of PEMFCs and their components can be achieved.

stat.AP↗

arXiv2025

SeLIP: Similarity Enhanced Contrastive Language Image Pretraining for Multi-modal Head MRI

Despite that deep learning (DL) methods have presented tremendous potential in many medical image analysis tasks, the practical applications of medical DL models are limited due to the lack of enough data samples with manual annotations. By noting that the clinical radiology examinations are associated with radiology reports that describe the images, we propose to develop a foundation model for multi-model head MRI by using contrastive learning on the images and the corresponding radiology findings. In particular, a contrastive learning framework is proposed, where a mixed syntax and semantic similarity matching metric is integrated to reduce the thirst of extreme large dataset in conventional contrastive learning framework. Our proposed similarity enhanced contrastive language image pretraining (SeLIP) is able to effectively extract more useful features. Experiments revealed that our proposed SeLIP performs well in many downstream tasks including image-text retrieval task, classification task, and image segmentation, which highlights the importance of considering the similarities among texts describing different images in developing medical image foundation models.

cs.CV↗

arXiv2024

DexGrasp-Diffusion: Diffusion-based Unified Functional Grasp Synthesis Method for Multi-Dexterous Robotic Hands

The versatility and adaptability of human grasping catalyze advancing dexterous robotic manipulation. While significant strides have been made in dexterous grasp generation, current research endeavors pivot towards optimizing object manipulation while ensuring functional integrity, emphasizing the synthesis of functional grasps following desired affordance instructions. This paper addresses the challenge of synthesizing functional grasps tailored to diverse dexterous robotic hands by proposing DexGrasp-Diffusion, an end-to-end modularized diffusion-based method. DexGrasp-Diffusion integrates MultiHandDiffuser, a novel unified data-driven diffusion model for multi-dexterous hands grasp estimation, with DexDiscriminator, which employs a Physics Discriminator and a Functional Discriminator with open-vocabulary setting to filter physically plausible functional grasps based on object affordances. The experimental evaluation conducted on the MultiDex dataset provides substantiating evidence supporting the superior performance of MultiHandDiffuser over the baseline model in terms of success rate, grasp diversity, and collision depth. Moreover, we demonstrate the capacity of DexGrasp-Diffusion to reliably generate functional grasps for household objects aligned with specific affordance instructions.

cs.RO↗

arXiv2024

Burst-Mode Digital Signal Processing for Coherent Optical Time-Division Multiple Access

As the 50G optical access gradually matures, it is time to discuss Beyond 50G optical access. According to the evolution rules of optical access standards, Beyond 50G optical access data rate may achieve 200Gb/s. Direct detection faces great challenges for Beyond 50G optical access, which makes coherent detection a potential solution. Similar to 50G optical timing-division-multiple access (TDMA), burst-mode digital signal processing (BM-DSP) is also required for Beyond 50G coherent optical TDMA (CO-TDMA). This paper proposes coherent BM-DSP (Co-BM-DSP) based on approximately 10ns designed preambles to process the burst signal for 200G CO-TDMA, which can fast estimate the state of polarization, frequency offset, sampling phase offset, synchronization position, and equalizer coefficients. Meanwhile, for obtaining the equalizer coefficients based on the designed preamble, the channel estimation based on the minimum-mean-square-error criterion is theoretically proven to have a unique solution for ensuring reliability. In conclusion, the proposed Co-BM-DSP based on the designed preambles paves the way for the applications of Beyond 50G CO-TDMA.

cs.NI↗