I think this is a valuable direction for research to take. I suspect human emotion is represented in the training data and that there is some underlying model representing human emotional systems present within current LLMs. If we can understand how the model affects response given certain qualities in provided stimuli, then we can effectively use this embedded model to improve our understanding of both LLMs and Humans and also get more productive output from LLMs.
Thanks — I think you're right that emotional dynamics are already latent in training data. The question is whether we can make that implicit model explicit and architectural, so it's auditable and controllable rather than emergent and opaque.
That's what HEART attempts: rather than hoping the LLM has internalized useful emotional patterns, we create an explicit 18-dimensional state that actively modulates retrieval and reasoning. The tradeoff is added complexity, but the gain is transparency — you can trace exactly how emotional state influenced a decision.
Curious whether you think the interpretability benefits outweigh the engineering overhead, or if there's a lighter-weight approach that could get similar results.