# Personalized Emotional Intelligence in Generative AI through Symbolic Affective Reasoning

arXiv: https://arxiv.org/abs/2607.10678 · HTML: https://mlsanity.com/paper/2607.10678/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 97 references that are in the map.
- First posted on arXiv 12 Jul 2026.

## Builds on (most cited first, 14 of 14)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
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- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [Seeing Sound, Hearing Sight: Uncovering Modality Bias and Conflict of AI models in Sound Localization](https://mlsanity.com/paper/2505.11217/) (2025)
- [Do LLMs "Feel"? Emotion Circuits Discovery and Control](https://mlsanity.com/paper/2510.11328/) (2025)
- [Efficient Zero-shot Visual Search via Target and Context-aware Transformer](https://mlsanity.com/paper/2211.13470/) (2022)
- [Adaptive Visual Scene Understanding: Incremental Scene Graph Generation](https://mlsanity.com/paper/2310.01636/) (2023)
- [EmotionHallucer: Evaluating Emotion Hallucinations in Multimodal Large Language Models](https://mlsanity.com/paper/2505.11405/) (2025)
- [Pose Prior Learner: Unsupervised Categorical Prior Learning for Pose Estimation](https://mlsanity.com/paper/2410.03858/) (2024)
- [EmoKGEdit: Training-free Affective Injection via Visual Cue Transformation](https://mlsanity.com/paper/2601.12326/) (2026)
- [Learning to See Through a Baby's Eyes: Early Visual Diets Enable Robust Visual Intelligence in Humans and Machines](https://mlsanity.com/paper/2511.14440/) (2025)
- [Learning to See the Elephant in the Room: Self-Supervised Context Reasoning in Humans and AI](https://mlsanity.com/paper/2211.12817/) (2022)

## Abstract (arXiv)

Emotional intelligence enables humans to recognize emotions, infer their causes, reason about interventions, and modify their environment to achieve desired affective states. Despite recent advances in artificial intelligence (AI), current models remain largely limited to generating realistic content or performing semantic reasoning, with little capacity for understanding, predicting, and personalizing human emotional responses. Here we introduce Emotion-augmented geneRatiOn System (EROS), a hybrid AI framework that integrates symbolic reasoning with deep learning to enable personalized emotion augmentation through visual content. Leveraging large-scale image-emotion datasets, EROS discovers generalizable affective rules, identifies emotion-relevant image regions, and predicts context-aware visual modifications that preserve scene semantics while steering emotional responses toward desired targets. To account for individual variability, EROS incorporates an expandable memory bank that supports inference-time personalization without model fine-tuning, yielding interpretable emotional profiles and rapid adaptation to new users. Across extensive human psychophysics experiments, EROS elicits target emotional responses more effectively than state-of-the-art large multimodal models while adapting to individual affective preferences. Beyond affective computing, EROS provides a foundation for AI systems that can understand, reason about, and augment human cognitive states, with potential applications in mental health, adaptive media, education, and human-computer interaction.