Research

A reasoning model carries two kinds of momentum. One is worth keeping — the capability encoded in its weights by training. The other is the reason it can argue itself out of the right answer: the pull to stay consistent with whatever it said first. Chain-of-thought struggles to interrupt the second, because the check is written by the same running generation. A separate call can reduce it — and that, we argue, is most of why IRG is a different thing than a prompt.

VibeThinker-3B scores 94.3 on AIME26 with three billion parameters, matching models orders of magnitude larger. The claim underneath the benchmark is the interesting part: reasoning compresses aggressively while knowledge does not — meaning reasoning is a separable artifact. That has consequences for how production systems should be built.

For years the industry pulled three levers: more parameters, more data, more test-time compute. Memory stayed entangled in the weights. DeepSeek’s Engram pulls a fourth lever — explicit, conditional memory — and the early numbers suggest it was underexplored for no good reason. The deeper signal is architectural: the monolith is unbundling.