Intrinsic Labs
Note

When your benchmark saturates, stop ranking and start mining disagreement

Thousands of identical houses repeating to the horizon

This note comes out of Tycho, the program that builds The Ghost. The grading stage measured here is the part of Tycho's pipeline that decides which moments in a transcript count as a real decision.

A pipeline mines steering decisions from AI agent transcripts. Stage 2 grades each candidate moment — is this a genuine steer, and how strong? Fourteen held-out items and six models answered this:

engineexactHIT / not-HITs/item
Qwen3.6-27B8/1410/1431
Qwen3.8-27B8/149/1489
Claude Sonnet8/149/149
Claude Opus7/148/1414
Claude Fable7/148/1413

Everything competent clusters at 8–10 of 14. At n=14 every point is ±7%. This sheet is a terribleness filter; it cannot rank survivors.

Shared misses aren't all model error. A moment models read as a durable delegation policy was labeled MISS because the person was trying out a tool feature. That's context no prompt carries. The sheet is partly saturated.

What replaced ranking

When a sheet can't rank, stop asking it to: filter → disagreement-mine → label the disagreements.

Run two survivors over 300 stage-1 candidates, stratified round-robin by project, with exemplars removed first. Identical prompt across engines. Both engines completed all 300 with zero failures.

unanimous on HIT / not-HIT : 80%  (60 split)
unanimous on 3-way grade   : 73%  (81 split)
"mine it" rate:   Qwen3.6  46%     Sonnet  34%

The confusion matrix:

strongweaknone
strong90453
weak9857
none31444

Of 60 binary splits, 48 are "Qwen mines it, Sonnet doesn't." 45 of those sit in one cell: Qwen says strong, Sonnet says weak. Only 6 items in 300 are a strong↔none flip.

The engines are not reading differently. They are cutting the same ordering at different places. Where they disagree is exactly where a human label buys the most information. The question isn't "which model understands the corpus"—it's where does the HIT/WEAK line belong.

Where this goes next

The 60 binary splits are the labelling queue. What it buys: the labels place the line, and whichever engine sits on the right side runs the full 3,800-candidate pass. The question stops being which model is better and becomes where the boundary is.

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