Affective-Computing

Customer support is where emotional AI often goes to embarrass itself. A user says, “Fine, whatever.” The system detects a neutral sentence. A human hears irritation, resignation, and possibly the final five seconds before churn. The difference is not vocabulary. It is context, tone, facial expression, timing, and the reason behind the emotion. Unfortunately, many “emotion AI” systems still behave as if the job is to pick a label from a menu: happy, sad, angry, neutral. Very scientific. Also very convenient, because menus are easier than people. ...

Affective Inertia: Teaching LLM Agents to Remember Who They Are A chatbot does not need to forget your name to become strange. Sometimes the stranger failure is tonal. The assistant is patient for ten turns, defensive on the eleventh, apologetic on the twelfth, and oddly cheerful on the thirteenth. Nothing in the user’s goal changed. Nothing in the product specification said “please behave like an emotionally unstable intern with excellent grammar.” Yet the agent flips. ...

TL;DR for operators Emotion-like AI does not have to mean artificial suffering, digital joy, or a chatbot saying “I’m sad” with the theatrical subtlety of a bad intern. The useful idea in this paper is narrower: affect can be treated as a control layer that helps an agent decide what to do under uncertainty. ...