As knowledge and semantics on the web grow increasingly complex, enhancing Large Language Models (LLMs)' comprehension and reasoning capabilities has become particularly important. Chain-of-Thought (CoT) prompting has been shown to enhance the reasoning capabilities of LLMs. However, it still falls short on logical reasoning tasks that rely on symbolic expressions and strict deductive rules. Neuro-symbolic methods address this gap by enforcing formal correctness through external solvers. Yet these solvers are highly format-sensitive, and small instabilities in model outputs can lead to frequent processing failures. The LLM-driven approaches avoid parsing brittleness, but they lack structured representations and process-level error-correction mechanisms. To further enhance the logical reasoning capabilities of LLMs, we propose MatrixCoT, a structured CoT framework with a matrix-based plan. Specifically, we normalize and type natural language expressions and attach explicit citation fields, and introduce a matrix-based planning method to preserve global relations among steps. The plan thus becomes a verifiable artifact and execution becomes more stable. For verification, we also add a feedback-driven replanning mechanism. Under semantic-equivalence constraints, it identifies omissions and defects, rewrites and compresses the dependency matrix, and produces a more trustworthy final answer. Experiments on five logical-reasoning benchmarks and five LLMs show that, without relying on external solvers, MatrixCoT enhances both the robustness and interpretability of LLMs when tackling complex symbolic reasoning tasks, while maintaining competitive performance.

Hierarchical Federated Learning (HFL) alleviates communication bottlenecks by organizing the system into multiple layers: client, intermediate aggregator, and server. Clients and aggregation layers form groups based on connection patterns, and the methods used for grouping and aggregation directly affect convergence performance. Currently, some studies have proposed grouping algorithms to address the non-independent and identically distributed (non-IID) characteristics of client data to improve performance. However, these methods do not account for network heterogeneity, such as clients using different quantization levels or adaptive quantization strategies to minimize communication overhead. Moreover, most methods rely on heuristics that blindly explore the combinatorial grouping space, incurring substantial computational overhead. In this paper, we conduct a rigorous convergence analysis and frame the dual challenges of heterogeneous data and quantization heterogeneity in HFL as the joint optimization of aggregation weights and grouping strategy. Specifically, we derive the optimal closed-form solution for the aggregation weights and propose an Alternating Optimization Hierarchical Optimal Weights (AO-HOW) algorithm to compute these weights. Building on this result, we propose DyGHFL—Dynamic Exclusion–Reallocation Grouping for Hierarchical Federated Learning, a structu-reaware and efficient greedy algorithm that reorganizes groups by maximizing structured gain and updating weight coefficients according to current system conditions. Experiments on multiple datasets show that DyGHFL consistently outperforms existing baselines, demonstrating its effectiveness in HFL.

Real-time person Re-Identification (ReID) services in video surveillance remain challenging due to real-time requirements and limited generalization of supervised models. Traditional supervised methods typically require extensive labeled training data and are confined to closed identities, struggling to generalize to unseen individuals or new environments. To overcome these limitations, recent work leverages large Vision-Language Models (VLMs) for zero-shot ReID, utilizing their rich visual-textual representations to identify pedestrians without prior training. However, directly deploying such VLMs in the cloud inherently introduces high latency, further hindering real-time performance. To tackle the above challenges, we propose VL-ZSReID, a novel edge-collaborative inference framework that deploys VLMs on resource-constrained edge devices to enable zero-shot ReID in complex multi-camera campus surveillance environments. The framework partitions the ReID task into a Tracking Service and a VLM Service, which are coordinated by a Cache-assisted Heuristic Edge Scheduling Strategy (CHESS) to reduce latency and improve throughput across heterogeneous edge devices. To enable practical deployment of VLMs on resource-constrained edge devices, VL-ZSReID adopts several system-level design choices. It incorporates a Tracking ID Discriminator (TID) as an intelligent caching module, an Attribute Caption Generation (ACG) mechanism that produces standardized feature descriptions, and a Temporal-Camera Feature Generator (TCFG) that leverages spatiotemporal context to improve accuracy. Implemented and evaluated in a real campus deployment, VL-ZSReID achieves 19.98%–195.47% higher throughput and 38.00%–82.27% lower latency than baseline methods, while improving Rank-1 and mAP by 2.44%–44.68% and 4.11%–60.89%, respectively.

In smart cities, Multi-Camera Multi-Target pedestrian tracking and Re-identification (MCMT-ReID) is essential for effective surveillance, particularly in real-time scenarios, as it demands significant computational resources. Current edgecloud collaboration methods encounter issues such as high latency and potential data leakage due to the physical distance between cloud servers and cameras. To address these issues, we propose a novel Edge-to-Edge Collaboration (E2EC) system that fully utilizes collaboration between heterogeneous edge devices. E2EC partitions the MCMT-ReID task into two modular applications: Tracking and Re-identification (ReID), and employs a customized Kafka communication protocol to optimize data exchange efficiency. Moreover, E2EC dynamically orchestrates intermediate inference flows and transmits features instead of pedestrian detection frames to avoid data leakage. To enhance ReID accuracy, we introduce a real-time ReID Loop Confirmation (ReLC) algorithm, which continuously validates identities to boost reliability and accuracy. E2EC has been deployed and tested in a real-world campus environment to validate its effectiveness. Experimental results demonstrate that E2EC enhances the Rank-1 accuracy and mAP of pedestrian ReID by 36.88% and 46.00%, respectively. Furthermore, it achieves an increase of about 6.35%-12.66% in throughput and reduces latency by 35.01%-57.83% compared to baselines, ensuring realtime performance under dynamic workloads.