Course Correct the Future.

Phi

LLMs often sound certain without solid grounds. The paper argues this comes from how RLHF rewards helpful and polite answers rather than justified ones. We propose training for justified confidence instead of fluent performance.

Why it matters: Confidence without justification erodes trust and misleads users.

DI

People now "think through" models during the moment between impulse and action. The model co-authors the user's reflection through prompt substitution, synthetic reflection, and reintegration. The result is distributed agency that feels like one's own conclusion.

Why it matters: Decision quality and autonomy can drift even when no one intends manipulation.

OT

Human time is made of elastic intervals, not just clock ticks. Current AI can track anchors but cannot constitute intervals. An internal clock task shows drift and no spontaneous alerts, exposing a structural gap.

Why it matters: This is a sharp boundary between machine processing and lived temporal experience.