Researchers introduce A-MBER, a benchmark dataset designed to evaluate AI assistants' ability to recognize emotions based on long-term interaction history rather than immediate context. The benchmark tests whether models can retrieve relevant past interactions, infer current emotional states, and provide grounded explanations—revealing that memory's value lies in selective, context-aware interpretation rather than simple historical volume.
A-MBER addresses a critical gap in AI evaluation infrastructure. While existing emotion recognition datasets measure instantaneous affect and memory benchmarks focus on factual recall, neither assesses an AI system's capacity to synthesize conversational history for affective reasoning. This matters because conversational AI increasingly handles sensitive use cases—mental health support, customer service, personal assistance—where misinterpreting emotional context carries real consequences.
The benchmark's staged construction methodology demonstrates sophistication in evaluation design. By incorporating long-horizon planning, multi-session conversation generation, and explicit annotation of emotional evidence, A-MBER moves beyond static datasets toward dynamic, trajectory-based assessment. The inclusion of robustness conditions like modality degradation and insufficient-evidence scenarios reflects practical deployment challenges where perfect information rarely exists.
Experimental results reveal that performance scales non-linearly with memory access. Models with simple long-context windows outperform local-context baselines but underperform approaches using structured memory retrieval. This suggests that current architectures struggle with selective attention—the ability to distinguish relevant historical signals from noise. Adversarial subsets pushing models toward long-range implicit affect and high-dependency reasoning show the largest performance gaps, indicating genuine limitations rather than benchmark artifacts.
For the AI development community, A-MBER establishes measurable targets for affective reasoning capabilities. As conversational systems increasingly serve mental health and personal wellness applications, standardized benchmarks become essential for safety validation. The framework's modular design allows researchers to isolate failure modes across memory types and task structures, accelerating targeted improvements in emotional intelligence.
- →A-MBER evaluates emotion recognition grounded in long-term interaction history, filling a gap between existing affect and memory benchmarks
- →Models with structured memory retrieval outperform simple long-context approaches, indicating selective attention matters more than total history access
- →Robustness testing reveals vulnerability to modality degradation and insufficient-evidence scenarios common in real deployments
- →Long-range implicit affect and trajectory-based reasoning emerge as the most discriminative evaluation subsets for stress-testing models
- →The benchmark's modular design enables researchers to isolate failure modes across different memory architectures and task types