From 7ea798de80816ff7ec1fa953b5138dc3d804840f Mon Sep 17 00:00:00 2001
From: osmarks <osmarks@mycorrhiza>
Date: Sun, 8 Feb 2026 21:02:40 +0000
Subject: [PATCH] =?UTF-8?q?Edit=20=E2=80=98autogollark=E2=80=99?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 autogollark.myco | 1 +
 1 file changed, 1 insertion(+)

diff --git a/autogollark.myco b/autogollark.myco
index 0c1a4d7..70ba389 100644
--- a/autogollark.myco
+++ b/autogollark.myco
@@ -36,6 +36,7 @@ Autogollark currently comprises the dataset, the search API server and the [[htt
 * ~~Pending:~~ Resource now available: [[XEROGRAPHIC BIFROST]] phase 3.
 * https://arxiv.org/abs/2507.07101
 * https://arxiv.org/abs/2507.01335
+* https://arxiv.org/abs/2510.14901
 * https://github.com/d0rc/egg.c and https://eshyperscale.github.io/. Does this actually work (at scale)? Why? Would be really nice for using AMX units.
 * Maybe compute grants are available for training.
 * Substantial bandwidth bottleneck on CPU (230GB/s nominal; 200GB/s benchmarked; 100GB/s per NUMA node, which llama.cpp handles awfully). Specdec/MTP would be useful.