Category: LLMs & Foundation Models
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Week 28: LLMs & Foundation Models – Frontier AI Research Brief
This week in large language model research, the field continues its explosive expansion across every dimension — from new architectures and training techniques to evaluation benchmarks and safety considerations. With over 200 papers touching on LLMs, W28 of 2026 shows that foundation models are not just growing larger but more capable, more efficient, and more…
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Week 26: LLMs & Foundation Models – Frontier AI Research Brief
A focused look at this week’s most significant advances in llms & foundation models — 83 papers surveyed from arXiv and leading AI labs. — This week in foundation models, researchers pushed further into understanding how these systems learn, what they represent internally, and how far we can scale them. The papers span training dynamics,…
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Week 22, 2026 — LLM Training & Scaling Laws
This week brought transformative advances in understanding how large language models scale and train — from a unified theory of scaling failures to practical recipes for MoE hyperparameter transfer and data mixture auditing. Shannon Scaling Law Unifies Training Phenomena Xu Ouyang and colleagues proposed the Shannon Scaling Law, treating LLM training as information transmission over…
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Why the LLM Scaling Era Is Over — and What Comes Next
The year LLMs stopped getting bigger and started getting smarter. In May 2025, the AI research community was still buzzing about ever-larger models, ever-bigger training runs, and the seemingly inexorable march toward AGI fueled by GPU clusters the size of data centers. By May 2026, the conversation had fundamentally shifted. Not because scaling stopped working…