LLM sceptics have predicted the last 7 of 0 walls
RESEARCH
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Self-Orchestrating AI Agents: The Next Major Breakthrough in LLM Performance
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It’s happening: nitter.net/nousresearch/status/20… Matt Shumer (@mattshumer_) Agents that natively self-orchestrate, managing their own context, tools, and sub-agents, are the next big unlock in LLM performance. Right now, a skilled engineer building an optimized harness, with thoughtful data flow, separation of concerns, sub-agent management, etc., can make dramatic improvements over baseline for specific tasks. If a model could do this itself, that’d be a major step forward. You give it an objective and a set of tools, and it figures out the optimal way to orchestrate itself to do the task. For example, I’m building a very primitive AI scientist that I’ll open-source soon. Most of the work isn’t in the prompt, it’s in the harness… what the orchestrator sees, what sub‑agents see, what gets shared between them and when, where we summarize vs. pass raw data, and which tools each agent controls. Doing this allows me to dramatically improve what the model can do on its own. If a model can effectively design its own harness for a given problem, it’d be a huge step forward. My bet: self-orchestrating models… ones that manage their own context, tools, and sub-agents, will move the frontier almost as much as the jump from chatbot → reasoning did. Maybe more. — https://nitter.net/mattshumer_/status/1991942387145322715#m
→ View original post on X — @mattshumer_, 2026-03-19 21:48 UTC
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M²RNN: Non-Linear RNNs with Matrix-Valued States for Language Modeling
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Excited to share our latest work: M²RNN! We’ve revisited non-linear RNNs and found that expanding the hidden state to a matrix (Matrix-to-Matrix) significantly improves language modeling while the non-linear recurrence enables expressivity beyond TC⁰. Key highlights: – Efficient Scaling: Our expansion mechanism leverages. Tensor Cores for high-throughput training. – Better Long-Context Performance: Beats SOTA hybrid linear attention models by 8 points on LongBench. – Hybrid Models: Replacing just ONE layer in a hybrid stack gives massive gains with minimal overhead. This establishes non-linear RNNs as a primary building block for the next generation of LLMs. Mayank Mishra (@MayankMish98) Introducing M²RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling We bring back non-linear recurrence to language modeling and show it's been held back by small state sizes, not by non-linearity itself. 📄 Paper: arxiv.org/abs/2603.14360 💻 Code: github.com/open-lm-engine/lm… 🤗 Models: huggingface.co/collections/o… — https://nitter.net/MayankMish98/status/2034681226217595333#m
→ View original post on X — @berkeley_ai, 2026-03-19 21:41 UTC
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Biological Neurons vs ANNs: A Layered Network Analogy
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Not all do, eg. RBMs have 2. But more importantly, ANNs are not meant to be a model of neurons (with exceptions, in the neuromorphic modeling space). A biological neuron is probably more akin to a little 3 layer network with a dozen noisy perceptron nodes.
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50 ML Projects to Understand LLMs and Transformer Mechanisms
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50 ML projects to understand LLMs — Investigate transformer mechanisms through data analysis, visualization, and experimentation: http://
amzn.to/4aPfP7q
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#AI #GenAI #MachineLearning #DataScientist #DataScience -
Mathematical Methods in Data Science: Theory and Python Applications
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Mathematical Methods in Data Science — Bridging Theory and Applications with Python: http://
amzn.to/4b7ZYQ4
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#ML #MachineLearning #DataScientist #DataScience #Mathematics #Algorithms -
Analytical Skills for AI and Data Science Enterprise Development
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Analytical Skills for AI and Data Science — Building Skills for an AI-Driven Enterprise: https://
amzn.to/4b6fufu
…helps practitioners to create value from AI and data science using an analytical skillset — each chapter illustrates how each skill works across a collection of use -
Hands-On Mathematical Optimization with Python: Key Ingredients and Modeling Choices
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Hands-On Mathematical Optimization with Python: https://
amzn.to/4b3VADe “…presents the key ingredients of an optimization problem and the choices one needs to make when modeling a real-life problem mathematically. Topics covered range from linear and network optimization to -
AI Summit Operationalizing AI in Life Sciences
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Thomas Robinson is at the Flagship Pioneering AI Summit next week — where leaders from pharma, biotech, and AI are working through what it actually takes to operationalize AI in life sciences. https://
domino.buzz/4lpbP10 -
V-JEPA 2.1 Unlocking Dense Features Video Self-Supervised Learning
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Yann LeCun and his team dropped yet another paper! "V-JEPA 2.1: Unlocking Dense Features in Self-Supervised Learning" In this V-JEPA upgrade, they showed that if you make a video model predict every patch, not just the masked ones AND at multiple layers, they are able to