What Survives When You Compress a Recursive Reasoner for the Edge?
Recursive reasoning models solve hard structured tasks with a few million parameters by iterating a latent state, but deploying them on edge hardware means compressing them -- and quantization noise compounds across recursive cycles rather than accumulating over output tokens, so single-pass intuitions fail. Here, we ask what survives. Across a full precision sweep and three tasks, aggressive compression preserves local prediction but destroys global reasoning: pruning, distillation, and linear attention drive puzzle-exact accuracy to zero while cell accuracy holds. Naive INT4 is different: its damage is architectural, striking MLP-mixing recursion but not attention on the same task, and ...
| Authors | Pearse Jim, Steven Kolawole, Opegbemi Matthias Busoye et al. |
|---|---|
| Primary category | cs.LG |
| Published | 25 June 2026 |
| Last updated at source | 30 September 2026 |
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