Most LLM interactions are context-free at the human level. The model knows the conversation history but has no signal about the user’s current cognitive or emotional state — stressed vs focused, fatigued vs sharp — which arguably affects what a good response looks like more than the prompt itself. Been thinking about this as a two-layer input problem: Layer 1 — User state: real-time signals from facial expression, posture, energy level via front camera Layer 2 — Environmental moment: ambient context from the physical environment via back camera Together these create what I’m calling Contextual Intelligence — response modulation based on who you are right now, not just what you typed. Curious if anyone is doing serious work in this space, or knows of research I should be reading. Affective computing is the closest field I’ve found but most of it stops at detection rather than response adaptation. submitted by /u/onasnowwhitedove
Originally posted by u/onasnowwhitedove on r/ArtificialInteligence