Affective-Computing

TL;DR for operators Conversation history helps emotion recognition, but this paper shows that having the whole conversation available is not the same as extracting the affective signal that persists across it. Using the same frozen RoBERTa utterance representations, a structured atmosphere prior scores 71.29 versus 67.86 on IEMOCAP and 69.22 versus 63.63 on MELD; it also improves EmoryNLP and DailyDialog. AtmosERC then reuses that prior inside a lightweight classifier, while a prompt-level variant converts it into a textual cue for LLMs. The lightweight model leads reported baselines on three of four datasets, and the LLM cue improves all three tested general-purpose LLMs on both evaluated datasets. ...

TL;DR for operators A system observing a changing biological signal has a basic design choice: how much history should it examine at once? A short window may preserve transient changes that disappear when averaged across a longer recording; a long window may retain sustained patterns that short segments miss. Processing both sounds attractive, but every additional view consumes computation and does not necessarily add useful information. ...

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. ...