Knowledge editing seeks to update language models without full retraining. While most prior work focuses on isolated factual or instance-level edits, we study a more structured setting: mathematical rules as a controlled stress test for abstract symbolic rule editing. We introduce RULE-EDIT, the first benchmark explicitly designed for editing and evaluating rule-level knowledge in LLMs. Our benchmark investigates two key questions: (1) whether edits to abstract rules propagate to derived instances, and (2) whether edits remain consistent across different representations of the same rule. To evaluate these aspects, we propose two new metrics: Instance Portability and Rule Understanding. Experiments on strong open-source LLMs with representative editing methods show that, although models can often overwrite formula-level knowledge, they struggle to transfer edits to rule-derived instances and to maintain consistency across symbolic and natural-language forms. For example, several methods achieve nearly 100% reliability on direct rule queries, while Instance Portability never exceeds 52% and Rule Understanding remains below 26%. Overall, RULE-EDIT provides a controlled benchmark for studying whether current knowledge editing approaches truly modify abstract symbolic rules in LLMs, rather than merely altering surface outputs.

The evolving realism of AI-generated Videos (AIGC-V) is rapidly rendering traditional artifact-centric detection insufficient, necessitating a paradigm shift from low-level inspection to high-level semantic verification. This paper presents a comprehensive survey of AIGC-V detection, reframing the task as Factual Fidelity Verification, which asks whether the events, entities, and physical processes depicted in a video are consistent with real-world facts. To systematize this rapidly evolving field, we propose a Vision–Language Dual-View taxonomy that organizes existing methods into a hierarchical, four-layer landscape, spanning intrinsic cue analysis, spatiotemporal consistency modeling, cross-modal consistency reasoning, and language-guided world-level reasoning. This dual-view framing highlights a fundamental transition from artifact matching to evidence-based semantic verification enabled by vision–language models and agentic reasoning pipelines. Based on a systematic review of 195 papers, we synthesize AIGC-V generation paradigms, survey the landscape of detection methods, and review evaluation metrics and benchmarks in line with proposed views. Finally, we discuss current challenges and identify promising directions toward robust, explainable, and trustworthy detection.