LLM confidence and abstention policies

This technical brief treats llm confidence and abstention policies as a decision system: define its state, objective, feasible actions and evidence before comparing algorithms.

Practitioner lens

A model for llm confidence and abstention policies should state its objective and feasible set explicitly. Hidden penalty weights or relaxed constraints can make an impressive result operationally impossible.

First experiment

Report feasibility, tail outcomes, runtime and sensitivity—not only the best-case objective value.

This is an original educational brief. It is a starting point for discussion, not a claim that one technique fits every supply chain.