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MemT-1.0: Towards Memory-Native Evolution

Paper recorded by Signals 4 on 2026-10-19 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-19 on arXiv · recorded by Signals 4 on 2026-10-20

Category: cs.LG · 机器学习 · first seen 2026-10-20

Abstract

Memory-native evolution requires making memory an intrinsic capability of foundation models, enabling them to continuously accumulate and utilize information. To this end, we introduce MemT-1.0, a new architecture of memory foundation models that internalize memory as part of model computation. Building on Metis, MemT-1.0 employs Gated DeltaNet-2 with a stable token-level memory update mechanism w

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#237 most recent of 749 cs.LG papers we have recorded · ↑ newer: Supervised Learning with Quantum Hamiltonian Embeddings · ↓ older: Scaling Parameter-Efficient Fine-Tuning for Brain Encoding
Cite this page: MemT-1.0: Towards Memory-Native Evolution: the #237 most recent of 749 cs.LG papers we have recorded (as of 2026-10-19). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/memt-1-0-towards-memory-native-evolution.html
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