{"passport":{"slug":"mculora","display_name":"MCULoRA","model_type":"multimodal","parameter_count":null,"context_window":null,"claimed_developer":"ArXiv AI","confirmed_developer":null,"developer_confirmed_at":null,"developer_source":null,"first_appeared_at":"2026-03-15T00:34:24.31915+00:00","first_appeared_on":"rss_pipeline","status":"confirmed","availability":null,"deployment_status":"not_deployed","availability_scope":null,"gate":1,"eu_ai_act":"GPAI","regulatory_status":{"eu_ai_act":"GPAI"},"open_source":null,"weights_available":null,"license":null,"provenance_chain":[],"parent_model":null,"model_family":null,"superseded_by":null,"supersedes":[],"recommended_replacement":null,"product_links":null},"events":[{"event_type":"first_appearance","event_date":"2026-03-15T00:34:24.31915+00:00","title":"First recorded in AI news","detail":"A novel multimodal emotion recognition framework called MCULoRA is proposed, which uses a modality combination aware low-rank adaptation (MCLA) module to decouple shared and distinct characteristics of individual modality combinations, and a dynamic parameter fine-tuning (DPFT) module to optimize learning efficiency across different modality combinations. The framework is shown to outperform previous incomplete multimodal learning approaches in downstream task accuracy.","source_url":"https://arxiv.org/abs/2507.11202","source_name":"ArXiv AI"}],"delta":{"days_unattributed":117,"claimed_developer":"ArXiv AI","confirmed_developer":null,"match":false}}