Knowledge Graphs (KGs) provide structured and interpretable representations of real-world entities and relations. While dynamic KGs attempt to capture real-time changes, they typically treat updates as independent facts. This overlooks a critical challenge: a factual, localized update can contradict and invalidate previously correct knowledge, requiring revisions beyond the localized update to maintain KG consistency. Many of these inconsistencies arise from events whose effects propagate through relational dependencies, necessitating coordinated multi-hop reasoning rather than isolated changes. To address this, we introduce a model-agnostic framework for cascading KG update identification that leverages conformal prediction to provide reliable uncertainty guarantees over the cascade as a whole, accounting for dependencies among multi-hop update candidates. Building on this foundation, we further develop a graph-based KG update scoring framework that integrates large language models (LLMs) to enrich event representations with world knowledge. Experiments on two newly constructed real-world datasets, designed to reflect scenarios where events necessitate coordinated multi-hop updates, demonstrate that our framework establishes a strong baseline while offering calibrated confidence estimates, providing an effective solution for event-driven KG consistency restoration.

In recent times, there has been a shift towards adapting sequential recommendation to LLM paradigm to harness the capabilities of LLMs. These methods typically formulate recommendation data into natural language and train the model to forecast the subsequent item in an auto-regressive manner. Despite their notable success, the significant computational burden during inference poses a major challenge to their practical implementation. In this study, we aim to streamline current LLM-based recommendation models and introduce a straightforward yet highly effective model Lite-LLM4Rec. The primary objective of Lite-LLM4Rec is to ensure efficient inference for the sequential recommendation task. Lite-LLM4Rec circumvents the step-by-step beam search decoding by employing a direct item projection head to produce ranking scores in one step. This design arises from our empirical finding that beam search decoding is ultimately unnecessary for sequential recommendations. Additionally, Lite-LLM4Rec introduces a hierarchical LLM structure crafted to efficiently handle the extensive contextual information of items and redundant computation issue, thus diminishing computational overhead while enjoying the power of LLMs. Experiments on four publicly available datasets validate the efficacy of Lite-LLM4Rec in enhancing both performance and inference efficiency (notably 46.8% performance improvement and 99.48% efficiency improvement on ML-1m) compared to existing LLM-based methods. Our implementations are available at: https://github.com/HanbingWang2001/Lite-LLM4Rec-PyTorch.