Why two models fail differently

A frontier model is capable and bounded at the same time. Bounded because every architecture makes one trade in order to run at all, in how it handles the context you give it. Some compress that context to fit more in cheaply, and lose fine detail. Some select a subset to read at full fidelity, and miss what falls outside the selection. Some carry a running summary forward, and let the distant past fade. Some spread their attention evenly, and dilute it over a long input.

Each trade is reasonable. Each one also fixes, in advance, the kind of mistake the model will tend to make. The failure is not random. It is a property of the design.

The usual response is to ask several models and take the majority answer. It helps less than it looks. Models trained on similar data toward similar goals tend to be wrong in similar places, so their errors line up instead of cancelling, and a vote of correlated voices mostly multiplies the shared mistake. Past a handful of models, agreement stops buying correctness.

The opening is the opposite of agreement. If a model’s failure is a property of its design, then a second model built on a different trade fails somewhere else, and the first one’s blind spot is the second one’s clear view. The disagreement is the signal, and it is mechanical rather than lucky: a detail lost to compression is still there for full-fidelity selection; a fact that faded from a running summary is still present where it was selected exactly.

So the work is not to average models. It is to pair them across their failure mechanisms on purpose, to put each answer in front of the one perspective placed to catch what its author would miss, and then to settle any disagreement against something that cannot be voted with: a result you can run.

We do not publish which model plays which role, or how the pairings are chosen and tuned. That is the part that took the work. The idea is the easy thing to say and the hard thing to build, so we publish the idea and keep the build.