Looped/recurrent-depth transformer architectures can improve model quality at a fixed compute budget when the model is large enough.

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Our reading — they may agree, disagree or merely touch the same thing. Closest first: a shared subject counts for most, then how near the wording is.

• Test-time scaling has gained a third axis of latent space reasoning iterations in looped transformers. FCFrançois Chollet5 Sept 2026similar wordingCreator of the Keras deep-learning library and the ARC-AGI benchmark
• Scaling up language models greatly improves task-agnostic few-shot performance, sometimes matching prior fine-tuning approaches. DADario Amodei28 May 2020similar wordingCo-founder and CEO of Anthropic
• Residual networks are easier to optimize than plain ones and keep gaining accuracy as depth increases. KHKaiming He10 Dec 2015similar wordingAssociate professor at MIT and first author of the residual networks paper
• Sequence transduction can use a simple architecture based solely on attention, without recurrence or convolutions. AVAshish Vaswani12 Jun 2017similar wordingOne of the eight authors of the 2017 transformer paper…
• Larger language models already have enough capacity that extra per-layer embedding tricks add little benefit to them. SRSebastian Raschka16 May 2026similar wordingAI research engineer working on large language models

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