A-MBER: Affective Memory Benchmark for Emotion Recognition
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 support for testing whether a model can use remembered interaction history to interpret a user's present affective state. We introduce A-MBER, an Affective Memory Benchmark for Emotion Recognition, to evaluate this capability. A-MBER focuses on present aff...
| Authors | Deliang Wen, Ke Sun, Yu Wang |
|---|---|
| Primary category | cs.AI |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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