A-MBER: Affective Memory Benchmark for Emotion Recognition

ArXi:2604.07017v1 Announce Type: new AI assistants that interact with users over time need to interpret the user's current emotional state in order to respond appropriately and personally. However, this capability remains insufficiently evaluated. Existing emotion datasets mainly assess local or instantaneous affect, while long-term memory benchmarks focus largely on factual recall, temporal consistency, or knowledge updating. As a result, current resources provide limited for testing whether a model can use remembered interaction history to interpret a user's present affective state.