In Internet of Things (IoT) scenarios, vision-language models (VLMs) are increasingly employed for visual perception and reasoning. However, their inherent tendency toward hallucinated and post-hoc rationalized reasoning often produces false positives (FPs), where the final answer is correct but supported by unreliable reasoning paths, typically manifested as self-inconsistent reasoning. Existing methods primarily focus on forward enhancement of reasoning ability, while neglecting reverse verification of reasoning consistency. To address this issue, we propose ViFP, a training-free framework for visual FP detection and correction. ViFP identifies FPs through consistency analysis between direct reasoning and structured multi-step reasoning, uncovering self-inconsistent reasoning without relying on superior supervisory models. Based on detected FPs, ViFP adaptively optimizes reasoning paths by refining question types and reasoning chain templates. Furthermore, we introduce a novel reliability metric, Value of Correction (VoC), which quantitatively measures the benefit of FP correction by jointly considering accuracy improvement, true negative rate, and FP reduction. VoC provides an interpretable indicator of reasoning reliability beyond conventional accuracy metrics. ViFP is designed as a cloud/edge-side reasoning reliability service for IoT systems and is compatible with leading closed-source VLMs. Experiments on A-OKVQA, OK-VQA, and FVQA demonstrate that ViFP significantly improves reasoning accuracy and reduces FPs, achieving up to 5.4% accuracy gain and surpassing previous state-of-the-art performance.

Temporal Knowledge Graph (TKG) reasoning focuses on predicting events through historical information within snapshots distributed on a timeline. Existing studies mainly concentrate on two perspectives of leveraging the history of TKGs, including capturing evolution of each recent snapshot or correlations among global historical facts. Despite the achieved significant accomplishments, these models still fall short of I) investigating the impact of multi-granular interactions across recent snapshots, and II) harnessing the expressive semantics of significant links accorded with queries throughout the entire history, particularly events exerting a profound impact on the future. These inadequacies restrict representation ability to reflect historical dependencies and future trends thoroughly. To overcome these drawbacks, we propose an innovative TKG reasoning approach towards Historically Relevant Events Structuring (HisRES). Concretely, HisRES comprises two distinctive modules excelling in structuring historically relevant events within TKGs, including a multi-granularity evolutionary encoder that captures structural and temporal dependencies of the most recent snapshots, and a global relevance encoder that concentrates on crucial correlations among events relevant to queries from the entire history. Furthermore, HisRES incorporates a self-gating mechanism for adaptively merging multi-granularity recent and historically relevant structuring representations. Extensive experiments on four event-based benchmarks demonstrate the state-of-the-art performance of HisRES and indicate the superiority and effectiveness of structuring historical relevance for TKG reasoning.

Blockchain-based Federated Learning (BFL) facilitates collaborative model training with guaranteed transparency and accountability across distributed participants. However, BFL encounters a critical security vulnerability: advanced free-riders can exploit the public on-chain model histories to synthesize fabricated gradients without performing actual local training, which is indistinguishable from legitimate contributions. Previous efforts to defense free-rider attack mostly focus on anomaly detection, but they struggle to detect some advanced free-riders. To address this fundamental challenge, we propose BFLGR (Blockchain-based Federated Learning with Gresham's Reversal), which is a novel framework integrating reverse auction mechanisms and a dynamic reputation system. BFLGR employs a dynamic selection algorithm that can eliminate advanced free-riders by exploiting their profit-seeking motives and parasitic dependency on honest participants' contributions. This framework ensures genuine contributions prevail over deceptive submissions, and achieves a paradigmatic shift in BFL security, transforming the transparency-exploitation vulnerability into a strategic advantage for detecting and eliminating malicious participants. Theoretical proofs and extensive experiments show that BFLGR performs well in advanced free-rider attack situation and outperforms our baselines.

Recent research in inductive reasoning has focused on predicting missing links between entities that are not observed during training. However, most approaches usually require that the relations are known at the inference time. In the real world, new entities and new relations usually emerge concurrently, which greatly challenges the model's generalization ability. In this paper, we propose a novel inductive knowledge graph embedding model that effectively handles unknown entities and relations by capturing their local structural features. Specifically, a relation graph is constructed to learn relation representations. In the relation graph, we employ a four-dimensional vector to represent the interaction patterns between nodes (relations), where each dimension corresponds to a specific type of interaction. For entity representations, our model dynamically initializes entity features using relation features and attentively aggregates neighboring features of entities to update entity features. By modeling interaction patterns between relations and incorporating structural information of entities, our model learns how to aggregate neighboring embeddings using attention mechanisms, thus generating high-quality embeddings for new entities and relations. Extensive experiments on benchmark datasets demonstrate that our model outperforms state-of-the-art methods, particularly in scenarios involving completely new relations.