Julia on Guy Freeman

Three Types and a Funeral for Your Inference Library

Sun, 26 Apr 2026 00:00:00 +0000

This is Part 1 of a series on Bayesian decision-theoretic agents.

This post describes the Credence architecture as it stood in March 2026, when the system used the standard Kolmogorov definition of probability — measures over sample spaces. Since then, the foundation has been reconstructed around de Finetti’s definition, where expectation (the prevision) is the primitive and probability is derived from it. The three types described here were the right starting point; what came next is at the end.

What would it take to build an agent that genuinely learns and decides — not one that pattern-matches its way through tool calls, but one whose behaviour is derived from a few fundamentals the way physics is derived from conservation laws?

The Agent That Invents Its Own Rules

Tue, 28 Apr 2026 00:00:00 +0000

The previous post in this series described what I called Tier 1 of the Credence architecture: a DSL for Bayesian decision agents with three types, four axioms, and a constitution forbidding everything else. That post ended with a program the user had to write by hand — a short S-expression encoding a hypothesis about what the environment was like and how to act in it.

Hand-written programs have a well-known limitation: they are only as good as whoever wrote them.

The Loop Problem

Tue, 28 Apr 2026 00:00:00 +0000

This is Part 3 of a series. For the axiomatic foundation, see Part 1: Three Types and a Funeral. For the VOI-gated text adventure agent, see Part 2: Teaching Zork to a Bayesian.

Every reinforcement learning agent that has ever played a text adventure has, at some point, tried to take the lantern fifty times in a row.

Not because it’s stupid. Because its state representation makes “Shack with book” and “Shack with lantern” look like different states, so the learned futility of “take lantern” in one state doesn’t transfer to the other. The agent is doing exactly what its architecture tells it to do: each state-action pair is independent, and it hasn’t yet learned that this particular pair is useless. It will learn, eventually, after wasting 39 steps per episode on actions it has already tried.