arXiv subjects

Jinhui Liu

Publications and source records attributed to Jinhui Liu.

7 recordsLinked to original sources

arXiv2026

Agentic Data Environments

Autonomous agents promise substantial gains in speed, scale, and labor efficiency, but their failures can impose abrupt and often irreversible costs. The central challenge for agentic automation is therefore to increase the benefits of automation while bounding the consequences of failure. While databases remain central to modern computing, agents operate over a broader data environment spanning files, APIs, applications, and system state. In this talk, I will outline early work on Agentic Data Environments -- the execution substrate in which agents operate -- that both amplify agent capabilities and enforce safety guarantees. This perspective reframes data systems from passive stores of state into active substrates for safe, reliable execution.

cs.AI↗

arXiv2026

Business Logic-Driven Text-to-SQL Data Synthesis for Business Intelligence

Evaluating Text-to-SQL agents in private business intelligence (BI) settings is challenging due to the scarcity of realistic, domain-specific data. While synthetic evaluation data offers a scalable solution, existing generation methods fail to capture business realism--whether questions reflect realistic business logic and workflows. We propose a Business Logic-Driven Data Synthesis framework that generates data grounded in business personas, work scenarios, and workflows. In addition, we improve the data quality by imposing a business reasoning complexity control strategy that diversifies the analytical reasoning steps required to answer the questions. Experiments on a production-scale Salesforce database show that our synthesized data achieves high business realism (98.44%), substantially outperforming OmniSQL (+19.5%) and SQL-Factory (+54.7%), while maintaining strong question-SQL alignment (98.59%). Our synthetic data also reveals that state-of-the-art Text-to-SQL models still have significant performance gaps, achieving only 42.86% execution accuracy on the most complex business queries.

cs.CL↗

arXiv2025

Collaborative Scheduling of Time-dependent UAVs,Vehicles and Workers for Crowdsensing in Disaster Response

Frequent natural disasters cause significant losses to human society, and timely, efficient collection of post-disaster environmental information is the foundation for effective rescue operations. Due to the extreme complexity of post-disaster environments, existing sensing technologies such as mobile crowdsensing suffer from weak environmental adaptability, insufficient professional sensing capabilities, and poor practicality of sensing solutions. Therefore, this paper explores a heterogeneous multi-agent online collaborative scheduling algorithm, HoCs-MPQ, to achieve efficient collection of post-disaster environmental information. HoCs-MPQ models collaboration and conflict relationships among multiple elements through weighted undirected graph construction, and iteratively solves the maximum weight independent set based on multi-priority queues, ultimately achieving collaborative sensing scheduling of time-dependent UA Vs, vehicles, and workers. Specifically, (1) HoCs-MPQ constructs weighted undirected graph nodes based on collaborative relationships among multiple elements and quantifies their weights, then models the weighted undirected graph based on conflict relationships between nodes; (2) HoCs-MPQ solves the maximum weight independent set based on iterated local search, and accelerates the solution process using multi-priority queues. Finally, we conducted detailed experiments based on extensive real-world and simulated data. The experiments show that, compared to baseline methods (e.g., HoCs-GREEDY, HoCs-K-WTA, HoCs-MADL, and HoCs-MARL), HoCs-MPQ improves task completion rates by an average of 54.13%, 23.82%, 14.12%, and 12.89% respectively, with computation time for single online autonomous scheduling decisions not exceeding 3 seconds.

cs.MA↗

arXiv2025

HRM^2Avatar: High-Fidelity Real-Time Mobile Avatars from Monocular Phone Scans

We present HRM$^2$Avatar, a framework for creating high-fidelity avatars from monocular phone scans, which can be rendered and animated in real time on mobile devices. Monocular capture with smartphones provides a low-cost alternative to studio-grade multi-camera rigs, making avatar digitization accessible to non-expert users. Reconstructing high-fidelity avatars from single-view video sequences poses challenges due to limited visual and geometric data. To address these limitations, at the data level, our method leverages two types of data captured with smartphones: static pose sequences for texture reconstruction and dynamic motion sequences for learning pose-dependent deformations and lighting changes. At the representation level, we employ a lightweight yet expressive representation to reconstruct high-fidelity digital humans from sparse monocular data. We extract garment meshes from monocular data to model clothing deformations effectively, and attach illumination-aware Gaussians to the mesh surface, enabling high-fidelity rendering and capturing pose-dependent lighting. This representation efficiently learns high-resolution and dynamic information from monocular data, enabling the creation of detailed avatars. At the rendering level, real-time performance is critical for animating high-fidelity avatars in AR/VR, social gaming, and on-device creation. Our GPU-driven rendering pipeline delivers 120 FPS on mobile devices and 90 FPS on standalone VR devices at 2K resolution, over $2.7\times$ faster than representative mobile-engine baselines. Experiments show that HRM$^2$Avatar delivers superior visual realism and real-time interactivity, outperforming state-of-the-art monocular methods.

cs.GR↗

arXiv2024

Probing the pairing symmetry in kagome superconductors based on the single-particle spectrum

We investigate the single-particle spectra of recently discovered kagome superconductors. We examine nine distinct superconducting pairing symmetries, including the uniform $s$-wave pairing function, nearest-neighbor and next-nearest-neighbor $s$-wave pairing, $p+ip$ pairing, $d+id$ pairing, and $f$-wave pairing states. These pairing states can be classified into irreducible representations of the $C_{6v}$ point group. Our findings suggest that these pairing states can be differentiated by analyzing the energy bands, spectral function, and local density of states. Additionally, we explore the single impurity effect, which could potentially assist in further distinguishing these pairing symmetries.

cond-mat.supr-con↗

arXiv2023

LpiCT: A logic security analysis framework for protocols

The pi calculus is a basic theory of mobile communication based on the notion of interaction, which, aimed at analyzing and modelling the behaviors of communication process in communicating and mobile systems, is widely applied to the security analysis of cryptographic protocol's design and implementation. But the pi calculus does not provide perfect logic security analysis, so the logic flaws in the design and the implementation of a cryptographic protocol can not be discovered in time. The aim is to analyze whether there are logic flaws in the design and the implementation of a cryptographic protocol, so as to ensure the security of the cryptographic protocol when it is encoded into a software and implemented. This paper introduces logic rules and proofs, binary tree and the KMP algorithm, and proposes a new extension the pi calculus theory, a logic security analysis framework and an algorithm. This paper presents the logic security proof and analysis of TLS1.3 protocol's interactional implementation process. Empirical results show that the new extension theory, the logic security analysis framework and the algorithm can effectively analyze whether there are logic flaws in the design and the implementation of a cryptographic protocol. The security of cryptographic protocols depends not only on cryptographic primitives, but also on the coding of cryptographic protocols and the environment in which they are implemented. The security analysis framework of cryptographic protocol implementation proposed in this paper can ensure the security of protocol implementation.

cs.CR↗

arXiv2022

Adaptive Neural Network Backstepping Control Method for Aerial Manipulator Based on Variable Inertia Parameter Modeling

For the aerial manipulator that performs aerial work tasks, the actual operating environment it faces is very complex, and it is affected by internal and external multi-source disturbances. In this paper, to effectively improve the anti-disturbance control performance of the aerial manipulator, an adaptive neural network backstepping control method based on variable inertia parameter modeling is proposed. Firstly, for the intense internal coupling disturbance, we analyze and model it from the perspective of the generation mechanism of the coupling disturbance, and derive the dynamics model of the aerial manipulator system and the coupling disturbance model based on the variable inertia parameters. Through the proposed coupling disturbance model, we can compensate the strong coupling disturbance in a way of feedforward. Then, the adaptive neural network is proposed and applid to estimate and compensate the additional disturbances, and the closed-loop controller is designed based on the backstepping control method. Finally, we verify the correctness of the proposed coupling disturbance model through physical experiment under a large range motion of the manipulator. Two sets of comparative simulation results also prove the accurate estimation of the proposed adaptive neural network for additional disturbances and the effectiveness and superiority of the proposed control method.

cs.RO↗