Narrative-Aware Scientific Claim Verification Agent with Evidence Ranking for ClimateCheck - Wenhao Su; Siyu Chen; Ethan Qian (University of Science and Computer Technology, 2026)
Climate misinformation often combines a factual proposition with a recognizable narrative, such as denying observed warming, rejecting human causation, minimizing impacts, attacking mitigation, or casting doubt on climate science. This paper presents a lightweight narrative-aware scientific claim verification agent for the official ClimateCheck setting. The revised evaluation uses the official annotated ClimateCheck training data, the official publications corpus of 394,269 abstracts, and a claim-level validation split of the annotated data. The public ClimateCheck test file is treated as a blind claim list because its public fields do not contain verification or narrative labels. The system combines hashed BM25, TF-IDF retrieval, latent semantic analysis, narrative-family probabilities, and a logistic-regression verifier. Full-corpus retrieval shows that BM25 remains the strongest first-stage retriever, with Recall@10 = 0.466, while the narrative-aware hybrid obtains Recall@10 = 0.444. In the judged candidate reranking setting, the narrative-aware ranker obtains the highest Candidate Recall@1 = 0.789 and MAP = 0.848, compared with 0.759 and 0.843 for TF-IDF. End-to-end verification remains difficult: the BM25 top-1 pipeline reaches Macro-F1 = 0.408, while the narrative-aware pipeline reaches Macro-F1 = 0.355. Claim-level narrative evaluation no longer produces a perfect score; single-label top-family Macro-F1 is 0.422, and fine-grained multi-label CARDS-code Macro-F1 is 0.098. These results show that narrative information is useful for reranking already plausible evidence candidates, but it does not replace strong lexical retrieval and does not by itself solve claim verification.