aiqbench analysis
This study reveals that compressing recursive reasoning models for edge deployment causes quantization noise to compound across iterative cycles, fundamentally breaking global reasoning capabilities while preserving local predictions. The authors identify that naive INT4 quantization specifically damages MLP-mixing mechanisms but can be reversed via per-channel calibration without retraining, offering a viable path to fit these models within strict memory budgets. A novel metric called "carry-trajectory fidelity" is introduced to predict model degradation and recovery before task evaluation, providing a critical tool for optimizing edge AI deployments.
What Survives When You Compress a Recursive Reasoner for the Edge?
arXiv:2606.26488v2 Announce Type: replace Abstract: 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 per-channel calibration reverses it without retraining, where quantization-aware training does not. We also introduce carry-trajectory fidelity, the cosine similarity to the full-precision reasoning path, as a label-free signal that predicts both the damage and the recovery before any task evaluation, with a threshold that transfers across tasks. The result is a deployment recipe: flash-streamed embeddings remove a 99.4 MB bottleneck, one recursive cycle matches full-depth accuracy at 6x fewer FLOPs, and calibrated INT4 brings the backbone to 3.3 MB, inside a 4 MB budget.
Title:What Survives When You Compress a Recursive Reasoner for the Edge?
Abstract: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 per-channel calibration reverses it without retraining, where quantization-aware training does not. We also introduce carry-trajectory fidelity, the cosine similarity to the full-precision reasoning path, as a label-free signal that predicts both the damage and the recovery before any task evaluation, with a threshold that transfers across tasks. The result is a deployment recipe: flash-streamed embeddings remove a 99.4 MB bottleneck, one recursive cycle matches full-depth accuracy at 6x fewer FLOPs, and calibrated INT4 brings the backbone to 3.3 MB, inside a 4 MB budget.
Machine Learning (cs.LG)
arXiv:2606.26488 [cs.LG]
(or arXiv:2606.26488v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.26488 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 286 words
