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ARLoop: Fully-Open Pre-trained Looped Transformers with Attention Residuals

Paper recorded by Signals 4 on 2026-11-03 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-03 on arXiv · recorded by Signals 4 on 2026-11-04

Category: cs.CL · 自然语言处理 · first seen 2026-11-04

Abstract

Looped transformers increase effective depth by repeatedly applying a shared set of transformer layers, offering a parameter-efficient way to scale computation. However, standard residual connections aggregate information across recurrent depth into a single stream, providing limited control over which intermediate representations from preceding layers should be reused. In this paper, we introduce

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#2 most recent of 628 cs.CL papers we have recorded · ↑ newer: MDSKILL: Learning to Internalize Skill Memory through Multi-View Disti · ↓ older: What Does the Model See? Representation Validity in Multimodal LLM-Bas
Cite this page: ARLoop: Fully-Open Pre-trained Looped Transformers with Attention Residuals: the #2 most recent of 628 cs.CL papers we have recorded (as of 2026-11-03). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/arloop-fully-open-pre-trained-looped-transformers-with-attention-residuals.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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