arXiv subjects

Huafu Li

Publications and source records attributed to Huafu Li.

5 recordsLinked to original sources

arXiv2026

When Agentic Trust Crosses Organizational Boundaries: Structural Externalization and a Reference Model for Trust Evidence

Agentic systems increasingly invoke tools, services, data, and other agents across organizational boundaries, yet a relying party cannot assess a delegated action solely from producing-domain controls and records. This paper develops Trustworthiness as a Service (TaaS) through a synthesis of trustworthy-AI governance, agent security, distributed trust management, identity, provenance, assurance, and control-plane research. The analytical unit is a cross-domain reliance proposition that names the issuer, subject and action, relying party, administrative boundary, evidence dependencies, adverse condition, and required verification or adjudication semantics. The three-condition structural-externalization diagnostic identifies propositions that depend on multiple domains, require producer-independent reliance, and must remain reviewable after revocation, failure, conflicting records, or dispute. For such propositions, the paper specifies a trust-evidence envelope: an immutable workflow manifest linked to append-only, issuer-attributed attestations for task-scoped authority, policy and execution decisions, provenance, validity, disclosure, status, challenge, and recovery. A topology-neutral logical reference model assigns these functions to explicit roles and trust domains. Three analytical scenarios and the TaaS-Eval protocol proposal define manifests, independent consumers, hard gates, adversarial evidence tests, metrics, and reproducible artifact reporting. By composing established identity, authorization, provenance, assurance, and governance mechanisms around a bounded delegated action, TaaS provides a reusable profile for cross-domain reliance. It makes evidence dependencies, independent verification, challenge, and recovery explicit, supporting interoperable governance and future evaluation without treating producer assertions as ground truth.

cs.CR↗

arXiv2026

Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models

Visual information extraction (VIE) from visually rich documents remains challenging due to high layout variability and real-world impairments. Existing methods typically rely on sequential OCR pipelines or end-to-end models requiring extensive labeled data and layout-specific training, limiting their scalability.We propose a classification-guided large vision-language model (LVLM) framework for multi-type VIE that achieves high accuracy with minimal supervision. The approach decouples document-type classification from content extraction and employs in-context learning (ICL)-based dynamic prompt engineering to inject task-specific knowledge, enabling robust zero-shot inference across diverse layouts. From a theoretical perspective, the proposed method can be viewed as a form of conditional computation that reduces task uncertainty and improves information efficiency during prompt-based inference. Evaluated on a real-world bidding dataset with 16 certificate types, our zero-shot method (based on Qwen2.5-VL-7B) outperforms a strong supervised baseline by 18.35 percentage points in F1-score (86.43\% vs. 68.08\%) and 0.23 in normalized edit distance (0.90 vs. 0.67). Optional domain-specific fine-tuning further improves performance to 93.65\% F1 and 0.93 NED, demonstrating superior robustness against seals, watermarks, and low contrast. The framework offers an efficient, scalable solution for complex document understanding in office automation. Code is available at https://github.com/FairmeHIT/Multi-VIE, and fine-tuned models at https://huggingface.co/fairme/Qwen2.5-VL-7B-SFT.

cs.CV↗

arXiv2025

A Robust Cooperative Vehicle Coordination Framework for Intersection Crossing

Cooperative vehicle coordination at unsignalized intersections has garnered significant interest from both academia and industry in recent years, highlighting its notable advantages in improving traffic throughput and fuel efficiency. However, most existing studies oversimplify the coordination system, assuming accurate vehicle state information and ideal state update process. The oversights pose driving risks in the presence of state uncertainty and communication constraint. To address this gap, we propose a robust and comprehensive intersection coordination framework consisting of a robust cooperative trajectory planner and a context-aware status update scheduler. The trajectory planner directly controls the evolution of the trajectory distributions during frequent vehicle interactions, thereby offering probabilistic safety guarantees. To further align with coordination safety in practical bandwidth-limited conditions, we propose a context-aware status update scheduler that dynamically prioritizes the state updating order of vehicles based on their driving urgency. Simulation results validate the robustness and effectiveness of the proposed coordination framework, showing that the collision probability can be significantly reduced while maintaining comparable coordination efficiency to state-of-theart strategies. Moreover, our proposed framework demonstrates superior effectiveness in utilizing wireless resources in practical uncertain and bandwidth-limited conditions.

eess.SY↗

arXiv2024

Caching Content Placement and Beamforming Co-design for IRS-Aided MIMO Systems with Imperfect CSI

When offloading links encounter deep fading and obstruction, edge caching cannot fully enhance wireless network performance and improve the QoS of edge nodes, as it fails to effectively reduce backhaul burden. The emerging technology of intelligent reflecting surfaces (IRS) compensates for this disadvantage by creating a smart and reconfigurable wireless environment. Subsequently, we jointly design content placement and active/passive beamforming to minimize network costs under imperfect channel state information (CSI) in the IRS-oriented edge caching system. This minimization problem is decomposed into two subproblems. The content placement subproblem is addressed by applying KKT optimality conditions. We then develop the alternating optimization method to resolve precoder and reflection beamforming. Specifically, we reduce transmission power by first fixing the phase shift, reducing the problem to a convex one relative to the precoder, which is solved through convex optimization. Next, we fix the precoder and resolve the resulting reflection beamforming problem using the penalty convex-concave procedure (CCP) method. Results demonstrate that our proposed method outperforms uniform caching and random phase approaches in reducing transmission power and saving network costs. Eventually, the proposed approach offers potential improvements in the caching optimization and transmission robustness of wireless communication with imperfect CSI.

eess.SP↗

arXiv2022

Aging Channel Modeling and Transmission Block Size Optimization for Massive MIMO Vehicular Networks in Non-Isotropic Scattering Environment

We investigate the effect of channel aging on multi-cell massive multiple-input multiple-output (MIMO) vehicular networks in a generic non-isotropic scattering environment. Based on the single cluster scattering assumption and the von Mises distribution assumptions of the scatterers' angles, an aging channel model is established to capture the joint effect of spatial and temporal correlations resulting from different angular spread conditions in various application scenarios. Expressions of the user uplink transmission spectral efficiency (SE) are derived for maximum ratio (MR) and minimum mean square error (MMSE) combining. Through numerical studies, the area spectral efficiency (ASE) performance of the network is evaluated in freeway and urban Manhattan road grid scenarios, and easy-to-use empirical models for the optimal transmission block size for ASE maximization are obtained for the evaluated scenarios.

cs.IT↗