Genesis: A Large-Scale Benchmark for Multimodal Large Language Model in Emotional Causality Analysis

Artificial intelligence will not achieve genuine empathy until models can reason about the causes of human emotions rather than only label them. Current datasets fail to support this objective, as existing emotional causality datasets primarily focus on textual modalities, lack non-verbal information such as speech and facial expressions, feature relatively short dialogue lengths, and limit research on long-term emotional evolution. Existing annotations concentrate on stimulus-response patterns and lack cross-temporal emotional causal chain annotations, failing to reveal how early events accumulate and ultimately trigger emotional changes. In this work, we introduce Genesis, the first multimodal dialogue dataset supporting long-term emotional causality analysis, which Genesis contains 1,000 dialogues averaging 208 turns each, spanning debate, family, educational, and social scenarios. Through two-layer annotation system: proximal cause identification and long-term causal chain tracking, Genesis labels complex emotional phenomena including cross-modal inconsistencies and long-distance causal dependencies. Our evaluation of 20 mainstream multimodal models reveals limitations in current approaches for long-term emotional causality. We propose Empathica as an evaluation baseline, employing a Recognition-Memory-Attribution architecture that integrates dynamic sliding windows and event aggregation mechanisms to address multimodal emotional causality modeling challenges. Empathica outperforms text-based models GPT-o1, and multimodal model Gemini 1.5 Pro and GPT-4o across all evaluation metrics.