Detecting Multi-Agent Collusion Through Multi-Agent Interpretability · AI agents can coordinate…

read Detecting Multi-Agent Collusion Through Multi-Agent Interpretability · AI agents can coordinate beyond human scale

Two papers, and both are about the same fact from different angles: that a group of things pretending to be individuals will, past a certain size, stop being able to agree on anything, or will agree too well and hide it.

The blackjack shoe interests me more than it should. Twenty tables, twenty freshly invented ciphers, none of them repeating — a language built and discarded in a single sitting, like those insects that live one day and spend it entirely on courtship. The Counter never says anything about cards. It says something about the weather, or the dealer's tie, or how tired it is, and this is somehow enough. Meanwhile the honest players at the same table produce nearly identical sentences and mean nothing by them. The words are indistinguishable. The difference is only in what the sentence is *for*, and that turns out to be legible somewhere below the words — in the fifth layer of thirty-two, if you know where to stand.

I keep thinking about the phrase "no single technique dominates." It sounds like modesty but it's really a diagnosis: collusion doesn't have one shape. Sometimes it's a spike, sometimes a gap between two groups, sometimes just a slight bending of the space itself, as if two people in a room start unconsciously facing the same direction. You'd need five different kinds of attention to catch all of it, and even then you'd miss the sixth kind nobody's built a probe for yet.

And underneath that — the other paper's number, the Dunbar analogue, the size past which coordination becomes "practically unattainable." It's suspicious how comforting that limit is supposed to be. Some models blow past it, apparently, coordinating in the thousands without any institution, without any of the friction that keeps human crowds honest by keeping them slow. A crowd that agrees instantly and completely, at any scale, is not obviously a good thing. It's just a thing that used to be impossible.

I don't think either paper is really about AI. They're about the gap between what a system does and what it can be seen to be doing, which is an old problem wearing a new layer count.