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Low-Bit GDN Recurrent-State Quantization: Global Gradient-aware Allocation and Pure-Write Sliding Windows

Paper recorded by Signals 4 on 2026-10-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.LG · 机器学习 · first seen 2026-10-30

Abstract

Linear attention stores historical information in a recurrent state of fixed size, providing a compact memory representation for long-context inference. Reducing state precision further lowers per-request storage requirements, but quantized writeback errors enter subsequent recurrences, degrade prediction quality on long-reasoning tasks, and can be accompanied by repetitive generation and excessiv

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#125 most recent of 737 cs.LG papers we have recorded · ↑ newer: H$^2$R: Hierarchical Hint-Based Reward for Mathematical Reasoning via · ↓ older: Learning Calibrated Confidence for Multi-Agent Collaboration
Cite this page: Low-Bit GDN Recurrent-State Quantization: Global Gradient-aware Allocation and Pure-Write Sliding Windows: the #125 most recent of 737 cs.LG papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/low-bit-gdn-recurrent-state-quantization-global-gradient-aware-allocation-and-pu.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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