Revision on AIBussin — AI applications, systems and books

Critique, Revision, and Acceptance

Sat, 08 Aug 2026 16:23:00 +0100

Selection can only choose among the candidates it is given. A strong evaluator may recognize that every available candidate is poor, but it cannot select a correct answer that the generator never produced.

That limitation becomes practical because the candidates usually come from one model answering one prompt, and such samples correlate. When four candidates share the same misreading of the evidence, ranking them can still produce a confident winner while leaving the shared defect untouched.

The Thinking Agent

Mon, 01 Jan 0001 00:00:00 +0000

In the last chapter, you built a small agentic workflow in conversation. It had roles, visible state, a tool boundary, and a review loop.

Now we can strengthen the idea that made it useful.

generate
 ↓
critique
 ↓
revise
 ↓
compare
 ↓
accept, reject, or roll back

This is the architecture of a thinking agent: not because the system has private consciousness, and not because a model’s self-critique is automatically reliable, but because the system can externalize an attempt, inspect it, produce a candidate improvement, and decide whether the candidate deserves to replace the current version.

Why

Sat, 19 Sep 2026 05:00:07 +0100

Chapters 3 through 6 leave the system with a working pipeline and an uncomfortable surplus. Chapter 3 retrieves raw history. Chapter 4 derives a persistent graph from it. Chapter 5 propagates activation across that graph. Chapter 6 chooses between these mechanisms. A candidate memory reaches the reader, the reader writes an answer, and the answer contains claims about the project. This chapter asks the book’s third question — why did we decide or believe this? — in the form the pipeline forces:

Agents From First Principles 03: AI Agent Keeps Making the Same Mistake? Add a Critique-and-Revision Loop

Sat, 08 Aug 2026 16:23:00 +0100

An AI agent can fail in a particularly frustrating way: it produces an answer that is almost right, you ask it to improve the answer, and it produces another answer with the same underlying defect.

Sometimes the wording changes. Sometimes it adds more explanation. Sometimes it becomes longer and more confident. But the important mistake survives.

That usually means the system is doing this:

prompt
 ↓
model
 ↓
answer

or this:

What Does a Preference Know About the Future?

Fri, 10 Jul 2026 11:32:58 +0100

We Trained a Model on Editorial Choices to See Whether It Learned What Happened Next

Most preference-learning systems use a choice to change the future.

A model produces two responses. A human selects one. The chosen response becomes positive evidence, the rejected response becomes negative evidence, and training makes outputs resembling the chosen response more likely.

The preference acts as an instruction:

Produce more things like this.

I wanted to know whether the same choice could also function as evidence.

The Preference Was Only the Beginning

Tue, 07 Jul 2026 00:00:00 +0100

A preference is not only a label on what just happened. When the decision belongs to a continuing trajectory, it can also be evidence about what happens next.

Abstract

Most preference-learning systems stop at the choice.

A model produces two responses. A human selects one. The chosen response becomes positive evidence, the rejected response becomes negative evidence, and the training system moves on.

The work itself usually continues.

The selected answer may later be revised, partially retained, contradicted or abandoned. The rejected alternative may reveal a constraint that remains active long after the immediate decision. The preference is therefore not necessarily the outcome. It may be an event inside a longer trajectory.