Supervisionfrom

Supervisionfrom is a term used in discussions of machine learning and human-in-the-loop systems to describe a framework in which supervisory signals come from a designated supervisor or set of sources, with explicit provenance attached to each signal.

The concept emphasizes provenance, confidence, and traceability of supervision. Unlike traditional one-source labeling, supervisionfrom can aggregate feedback from multiple sources, including humans, automated evaluators, or oracle models, and records the origin and reliability of each signal.

In practice, systems implement supervisionfrom by attaching metadata: source identifier, timestamp, and confidence estimates; routing signals through an intermediate agent that resolves conflicts; and using the signals to update models via supervised learning, reinforcement learning with human feedback, or active learning.

Applications include safer AI alignment, better data governance, and composable learning where different tasks borrow signals from appropriate sources; for example, a language model might receive high-confidence edits from experts and lower-confidence corrections from crowdsourcing platforms.

Challenges include source reliability, conflict resolution, privacy, and scalability; standardization of provenance metadata and evaluation of aggregated signals are active research areas.