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Competing Learning in Hybrid Architectures

Paper recorded by Signals 4 on 2026-10-19 in cs.CL. 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.CL · 自然语言处理 · first seen 2026-10-20

Abstract

Many modern language models have adopted hybrid architectures that incorporate both linear-time recurrence and softmax attention. Modern recurrence layers provide a highly expressive mixing mechanism while ensuring high throughput by compressing contextual information into a fixed-size state, but underperform softmax attention in contextual information retrieval. In this paper, we reveal a Competi

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#209 most recent of 660 cs.CL papers we have recorded · ↑ newer: Can It Actually Work? Synthesizing Functional CAD Mechanisms · ↓ older: KEEP-Bench: Evaluating Evidence Preservation in Context Compression
Cite this page: Competing Learning in Hybrid Architectures: the #209 most recent of 660 cs.CL papers we have recorded (as of 2026-10-19). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/competing-learning-in-hybrid-architectures.html
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