{"total":1,"items":[{"citing_arxiv_id":"2607.21496","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models","primary_cat":"eess.SP","submitted_at":"2026-07-23T16:43:12+00:00","verdict":"REJECT","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A multimodal LLM pipeline (Qwen audio + Qwen text embeddings, concatenated and classified) reaches 92.4% accuracy on a combined ADReSS20/ADReSSo21 test set, but the evaluation does not justify state-of-the-art or cross-dataset generalization claims.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}