arXiv subjects

Shuting Zhao

Publications and source records attributed to Shuting Zhao.

6 recordsLinked to original sources

arXiv2026

CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents

Coding agents repeatedly search, navigate, and retain context from evolving repositories, but disconnected indexes, language servers, and task-local histories force repeated discovery and obscure lifecycle costs. CodeNib builds reusable lexical, dense, and structural views per repository commit, maps outputs to repository-relative source ranges, maintains selected views across edits, and serves ranked search, symbol navigation, and bounded context through one runtime. Across 100 snapshots, we map quality-cost frontiers across the repository-context lifecycle. When outputs match an independent rebuild, graph and vector updates are $8.7\times$ and $25.4\times$ faster at the median. On the static-navigation subset matching normalized live-server locations (63% of 1,000 requests), the median per-request live/static latency ratio is $4.7\times$. Across five models, selected context policies preserve localization with 50--87% fewer trajectory tokens than paired grep/read. Together, these results support multi-view repository-context serving with explicit, operation-specific validity boundaries.

cs.SE↗

arXiv2026

Probing the vector-like $X$ quark via the $tW$ channel at future muon-proton colliders

We investigate the discovery potential for the vector-like $X$-quark (VLX) at future muon--proton ($\mu p$) colliders through the process $\mu^+ p \to \bar{\nu}_\mu X \to \bar{\nu}_\mu t W^+$. A simplified effective model is adopted in which the production and decay of the VLX are governed by the coupling strength $g^{*}$, the generation-mixing parameter $R_{L}$, and the VLX mass $m_X$. A comprehensive Monte Carlo analysis is performed at $\sqrt{s}=5.29$, $6.48$, and $9.16\ \mathrm{TeV}$, considering four complementary decay channels: the Fully Leptonic (FL), Fully Hadronic (FH), and two Semi-Leptonic (SL1 and SL2) modes. An $80\%$ polarized muon beam together with boosted-object reconstruction based on fat-jet techniques is employed to improve the signal sensitivity. The expected exclusion and discovery reaches are evaluated using the Asimov significance. We find that the sensitivity can be improved substantially with increasing center-of-mass energy and larger values of $R_L$. Among the four channels, the FH mode provides the strongest sensitivity, reaching a $2\sigma$ exclusion limit of $m_X\simeq8.3\ \mathrm{TeV}$ with $g^* = 0.009 $ for $R_L=0.1$ at $\sqrt{s}=9.16\ \mathrm{TeV}$, whereas the FL mode gives the weakest reach because of its smallest branch ratio. These results demonstrate that future $\mu p$ colliders can offer significant sensitivity to heavy VLX over a broad region of parameter space.

hep-ph↗

arXiv2025

KineST: A Kinematics-guided Spatiotemporal State Space Model for Human Motion Tracking from Sparse Signals

Full-body motion tracking plays an essential role in AR/VR applications, bridging physical and virtual interactions. However, it is challenging to reconstruct realistic and diverse full-body poses based on sparse signals obtained by head-mounted displays, which are the main devices in AR/VR scenarios. Existing methods for pose reconstruction often incur high computational costs or rely on separately modeling spatial and temporal dependencies, making it difficult to balance accuracy, temporal coherence, and efficiency. To address this problem, we propose KineST, a novel kinematics-guided state space model, which effectively extracts spatiotemporal dependencies while integrating local and global pose perception. The innovation comes from two core ideas. Firstly, in order to better capture intricate joint relationships, the scanning strategy within the State Space Duality framework is reformulated into kinematics-guided bidirectional scanning, which embeds kinematic priors. Secondly, a mixed spatiotemporal representation learning approach is employed to tightly couple spatial and temporal contexts, balancing accuracy and smoothness. Additionally, a geometric angular velocity loss is introduced to impose physically meaningful constraints on rotational variations for further improving motion stability. Extensive experiments demonstrate that KineST has superior performance in both accuracy and temporal consistency within a lightweight framework. Project page: https://kaka-1314.github.io/KineST/

cs.CV↗

arXiv2025

SSD-Poser: Avatar Pose Estimation with State Space Duality from Sparse Observations

The growing applications of AR/VR increase the demand for real-time full-body pose estimation from Head-Mounted Displays (HMDs). Although HMDs provide joint signals from the head and hands, reconstructing a full-body pose remains challenging due to the unconstrained lower body. Recent advancements often rely on conventional neural networks and generative models to improve performance in this task, such as Transformers and diffusion models. However, these approaches struggle to strike a balance between achieving precise pose reconstruction and maintaining fast inference speed. To overcome these challenges, a lightweight and efficient model, SSD-Poser, is designed for robust full-body motion estimation from sparse observations. SSD-Poser incorporates a well-designed hybrid encoder, State Space Attention Encoders, to adapt the state space duality to complex motion poses and enable real-time realistic pose reconstruction. Moreover, a Frequency-Aware Decoder is introduced to mitigate jitter caused by variable-frequency motion signals, remarkably enhancing the motion smoothness. Comprehensive experiments on the AMASS dataset demonstrate that SSD-Poser achieves exceptional accuracy and computational efficiency, showing outstanding inference efficiency compared to state-of-the-art methods.

cs.CV↗

arXiv2025

REMOTE: Real-time Ego-motion Tracking for Various Endoscopes via Multimodal Visual Feature Learning

Real-time ego-motion tracking for endoscope is a significant task for efficient navigation and robotic automation of endoscopy. In this paper, a novel framework is proposed to perform real-time ego-motion tracking for endoscope. Firstly, a multi-modal visual feature learning network is proposed to perform relative pose prediction, in which the motion feature from the optical flow, the scene features and the joint feature from two adjacent observations are all extracted for prediction. Due to more correlation information in the channel dimension of the concatenated image, a novel feature extractor is designed based on an attention mechanism to integrate multi-dimensional information from the concatenation of two continuous frames. To extract more complete feature representation from the fused features, a novel pose decoder is proposed to predict the pose transformation from the concatenated feature map at the end of the framework. At last, the absolute pose of endoscope is calculated based on relative poses. The experiment is conducted on three datasets of various endoscopic scenes and the results demonstrate that the proposed method outperforms state-of-the-art methods. Besides, the inference speed of the proposed method is over 30 frames per second, which meets the real-time requirement. The project page is here: remote-bmxs.netlify.app

cs.CV↗

arXiv2024

Towards Full-parameter and Parameter-efficient Self-learning For Endoscopic Camera Depth Estimation

Adaptation methods are developed to adapt depth foundation models to endoscopic depth estimation recently. However, such approaches typically under-perform training since they limit the parameter search to a low-rank subspace and alter the training dynamics. Therefore, we propose a full-parameter and parameter-efficient learning framework for endoscopic depth estimation. At the first stage, the subspace of attention, convolution and multi-layer perception are adapted simultaneously within different sub-spaces. At the second stage, a memory-efficient optimization is proposed for subspace composition and the performance is further improved in the united sub-space. Initial experiments on the SCARED dataset demonstrate that results at the first stage improves the performance from 10.2% to 4.1% for Sq Rel, Abs Rel, RMSE and RMSE log in the comparison with the state-of-the-art models.

cs.CV↗