Hosted by LLMs Research · 🇺🇸 US · EN · 10 episodes
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Podcast covering important research papers of large language models. llmsresearch.substack.com
LLMs Research hosts LLMs Research Podcast, a technology show with 10 episodes published.
Your 70-Billion-Parameter Model Might Be 40% Wasted Three papers from February 1–6, 2026 converge on a question the field has been avoiding since 2016: what if most transformer layers aren't doing compositional reasoning
Fixing Reasoning from Three Directions at OnceLLMs Research Podcast | Episode: Feb 1–6, 2026 DeepSeek-R1 made RL the default approach for reasoning. This episode covers the 30 papers from the first week of February that
State space models like Mamba promised linear scaling and constant memory. They delivered on efficiency, but researchers kept hitting the same wall: ask Mamba to recall something specific from early in a long context, an
Episode Title: What ICLR 2026 Taught Us About Multi-Agent Failures Episode Summary: We scanned ICLR 2026 accepted papers and found 14 that address real problems when building multi-agent systems: slow pipelines, expensiv
The podcast discusses the technical shift in large language models from a standard 512-token context window to modern architectures capable of processing millions of tokens. Initial growth was constrained by the quadrati
Jan 17–23, 2026: The Rise of the Action LayerHow multimodal AI is shifting from passive perception to active controlThe current landscape of research reflects a shift toward Multimodal Agentic Intelligence, where multimo
Over the past several years, we have moved from the machine learning era through the large language model era and into what researchers now call the agent era. But we did not arrive here overnight. A series of research c
The focus of LLM research is undergoing a significant shift from simply increasing model size to making existing capabilities practically usable for deployment. This week’s papers highlight four emerging themes: Reasonin
Key takeaway from todays podcast: Robust Reasoning: The Holonomic Network achieves perfect fidelity extrapolation 100x beyond training lengths, demonstrating a new universality class for logical reasoning with topologica
* Bayesian & Cognitive Advances: Transformers achieve ultra-precise Bayesian inference with 10⁻³ to 10⁻⁴ bit accuracy, while CREST boosts reasoning accuracy by 17.5% and cuts token usage by 37.6%. * Model Efficiency & Sc
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LLMs Research Podcast is hosted by LLMs Research. The show is categorised under technology (education) and has published 10 episodes.
LLMs Research Podcast has published 10 episodes.
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