We present Spoiler Court, a multi-agent adversarial framework for interpretable spoiler detection in movie reviews. Three LLM agents, acting as the Plaintiff, Defendant, and Judge, engage in a multi-round courtroom debate to identify spoiler spans, challenge them, and deliver a final judgement with an interpretable audit trail grounded in the movie’s plot summary. Unlike prior work that produces only binary labels, Spoiler Court outputs which sentences are spoilers, why (via plot fact citations), and how the decision was reached (via adversarial debate transcripts). To evaluate the effectiveness of our approach, we construct a multi-genre evaluation dataset spanning 4 movies with binary and character-level span annotations. Under controlled conditions, Spoiler Court achieves an average F1 of 0.733 across 4 movies, outperforming other baselines including a dedicated spoiler detection framework.

To reduce hallucinations in large language models (LLMs), researchers are increasingly investigating reasoning methods that integrate LLMs with external knowledge graphs (KGs). Existing approaches either map an LLM-generated query graph onto the KG or let the LLM traverse the entire graph; the former is fragile because noisy query graphs derail retrieval, whereas the latter is inefficient due to entity-level reasoning over large graphs. In order to tackle these problems, we propose SAFE (Schema-Driven Approximate Distance Join For Efficient Knowledge Graph Querying), a framework that leverages schema graphs for robust query graph generation and efficient KG retrieval. SAFE introduces two key ideas: (1) an Approximate Distance Join (ADJ) algorithm that refines LLM-generated pseudo query graphs by flexibly aligning them with the KG’s structure; and (2) exploiting a compact schema graph to perform ADJ efficiently, reducing overhead and improving retrieval accuracy. Extensive experiments on WebQSP, CWQ and GrailQA demonstrate that SAFE outperforms state-of-the-art methods in both accuracy and efficiency, providing a robust and scalable solution to overcome the inherent limitations of LLM-based knowledge retrieval.