The video above is the article. What follows is the evidence for it: what a decision room is, what happened in room R-G on 17 October, the arithmetic behind the room's answer, and the record every room leaves behind.

What a room is

AvoQ is the coding workbench I am building: an AI agent writes the code, and only checked code is let out. Its security gate needed a secret scanner, and on 17 October I decided which one: Betterleaks 2, from its v2.0.0 release.

A decision like that is logged, and a room is queued for it. The room gets the facts and the options, never my choice and never a recommendation. Two models from two different families take the seats, Claude Opus 5.5 and GPT-6.1 Sol, each seals a first vote before it reads the other’s, and then they argue for two rounds. They can have a web page fetched and read it as evidence. Between the rounds a third model, Jev, a fast judging model from TypeSafe, scores the arguments, not the votes.

What happened in room R-G

The room opened at 18:50:04 and closed at 18:52:14, Helsinki time: 2 min 10 s. In round 1 the seats had Betterleaks’ release page fetched: the newest release was v2.0.0-rc.2, a pre-release, and v2.0.0 did not exist yet.

The room chose Betterleaks 2 on a condition: pin the release candidate and its rule file’s hash together, let the gate fail closed on anything it cannot read, and move to v2.0.0 only after an acceptance test and a Windows measurement. It chose it at a value of 0.15, under the bar of 0.6 declared before the room ran. That low value is the room saying it is unsure.

I read the arguments and took the condition. It became row 98 of AvoQ’s decisions, 41 minutes after the room closed. The gate’s first commit came the next day, pinned to 2.0.0-rc.2 and to the rule file’s hash. The room had changed the decision before a single line of the gate existed.

What the record says so far

Every room leaves a record, and every decision it checks is counted. Read on 24 October, rooms had returned a result on 147 of my decisions and disagreed with 30: about one in five. Whether the room or I was right shows only later, so each outcome is recorded as it arrives. The first pre-registered check of that record waits for 20 outcomes and has 9.

Why I do it this way

That is hybrid decision-making: two language models argue, a fast judging model scores, and I decide. The word hybrid is borrowed. Wang et al. use hybrid thinking for a single model that switches between reasoning and answering directly; a room keeps the two apart, as separate models with separate jobs. Without it, my decisions are slower and less complete.

Transcript

AI writes code faster than a person can check what it decided. So before it builds something hard to undo, the decision goes to a room.

This is AvoQ, the coding workbench I am building. Its security gate needs a secret scanner. I decided: Betterleaks 2, from its version 2.0 release.

The decision is logged, and a room is queued. The question goes in blind: the facts and the options, never my choice.

This is the real room, held in Finnish. Replayed from its record: my machine seats two models from two families, Claude and GPT. Neither can see my decision. Each seals a first vote before it reads the other’s.

Then two rounds of arguments. They have the release page fetched: version 2.0 does not exist yet, only a release candidate. Between the rounds Jev, a fast judging model from TypeSafe, scores the arguments, not the votes.

The room disagrees with me. Betterleaks 2, yes, but on a condition: pin the release candidate and its rule file, and let the gate fail closed. And it is unsure. The weaker seat gives it only 0.15.

I read the arguments, and I took the condition. It became row 98 of AvoQ’s decisions.

The gate was built the next day, pinned exactly as the room asked. The room had changed the decision before a single line of the gate existed.

Rooms have now checked 147 of my decisions and disagreed with 30. Every outcome is recorded, so over time the system learns when to trust a room, and when to trust me.

The room added a condition my decision did not have, in two minutes. That is hybrid decision-making: two language models argue, a fast judging model scores, and I decide. Without it, my decisions are slower and less complete.

Sources & further reading

  • A. Wald, Statistical Decision Functions (1950): the maximin rule, choosing the action whose worst case is best.
  • J. Rawls, A Theory of Justice (1971): the same shape applied to judging an arrangement by its worst-off position.
  • S. Wang et al. (Case Western Reserve University, Michigan State University, Meta AI), Demystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think? (arXiv 2510.12680v2): where the word hybrid comes from.
  • J. Kwok et al. (Stanford and NVIDIA), Contrastive Language Models: A System One Model for Fast and Generalizable Decision-Making (2026): the kind of fast judging model Jev is, and a comparison with it.

— SF3D