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Candidate Generation and Selection

Sat, 08 Aug 2026 15:54:00 +0100

The previous chapter built a boundary that stops arbitrary model output from acquiring execution authority without explicit checks. It made one class of failure inspectable and enforceable, and it is silent about another.

Suppose the model is asked to solve a coding problem. One run produces the right patch. The next produces a plausible but incomplete one. A third produces something better again. Nothing is malformed, nothing violates the action schema, and every one of them would pass the boundary we just built. Validity and quality are different properties. A proposal can be completely valid and still be a poor choice, which relocates the uncertainty rather than removing it:

Reasoning Is More Than Architecture — Where Extra Computation Lives

Tue, 01 Sep 2026 10:41:00 +0100

Reasoning Is More Than Architecture — Where Extra Computation Lives

So far in Models From First Principles, we have changed several different things and called all of them model design.

We changed what a model predicts.

MR.Q produced one learned quality score.

EBT added Q, V, policy, and advantage.

SICQL turned those outputs into explicit model components.

Then we changed how computation unfolds.

HRM introduced recurrent state operating at different timescales.

Agents From First Principles 08: AI Agent Picks the First Solution? Add Search Instead of One-Shot Generation

Sat, 08 Aug 2026 17:26:00 +0100

An AI agent often fails for a surprisingly ordinary reason:

it commits too early.

It finds one plausible next action, follows it, and then spends the rest of the run trying to make that first choice work.

That can look intelligent because the agent keeps reasoning, calling tools, revising plans, and explaining itself.

But underneath, the trajectory may be almost completely determined by an early mistake.

A coding agent chooses the wrong implementation strategy and spends twenty tool calls repairing it.

Agents From First Principles 02: AI Agent Gives Inconsistent Answers? Generate Multiple Candidates and Rank Them

Sat, 08 Aug 2026 15:54:00 +0100

One of the first things you notice when you build anything around a large language model is that the same prompt does not always produce the same quality of answer.

Sometimes the first response is excellent.

Sometimes it is merely acceptable.

Sometimes it misses the point entirely.

That creates a very common agent-engineering question:

If the model is inconsistent, should the agent trust the first answer it gets?

Often, no.