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https://github.com/meituan-longcat/LongCat-Flash-Prover LongCat-Flash-Prover: Native Formal Reasoning via Agentic ...

DSPy turns RAG tuning from guesswork into metric-driven optimization—learn a reproducible workflow to tune retrieval and ...

Check out with MiniMax: https://x.com/MiniMax_AI/status/2021980761210134808?s=20 Explore MaxClaw/MiniMax Agent: ...

https://mesuvash.github.io/blog/2026/rl_for_llm/ Intuition-First Guide to Reinforcement Learning for LLMs This guide explores the ...

OptiMind is a specialized language model that translates natural-language problem descriptions directly into solver-ready ...

This project demonstrates a wireframe renderer in C. It uses manual transformations and perspective projection without using ...

Meta just open-sourced HyperAgents — self-referential, self-improving agents that can optimize for any computable task.

This is a quick overview of modern LLMs like GPT-2 This video is inspired by 3b1b's videos and Andrej Karpathy's "Zero to Hero" ...

In this video, we put the Qwen 3.5 0.8B GGUF to the test. Despite its tiny parameter count, this model is a breakthrough in ...

https://rlhfbook.com/c/06-policy-gradients Reinforcement Learning | RLHF Book by Nathan Lambert This text provides a technical ...

Most people think Machine Learning is just importing libraries and hitting "run." But if you don't understand the underlying math, ...

Modern systems engineering has reached its limit with traditional tools. In this introductory video, we explore why SysML V2 is the ...

A deep dive into automatic differentiation, revealing how to compute model sensitivities accurately and efficiently in real world ...

MLP using 64 combinations of hyperparameters that trained a model to classify images into 20 superclasses.

Large language models (LLMs) exhibit sophisticated behavior, notably solving arithmetic with only a few in-context examples ...

https://www.alphaxiv.org/abs/1412.6980 The Architecture of Stochastic Optimization The provided text introduces a technical ...

In this AI Research Roundup episode, Alex discusses the paper: 'LLMs Can Learn to Reason Via Off-Policy RL'. Traditional ...

This chapter introduces the foundational concepts of computation models, exploring how the flexibility of an automaton's ...

This video walks through a hands-on mini-experiment with sequence models (RNN/LSTM). We train a small text classification ...

We empirically explore the design principles and performance optimization plans of the next-generation native multi-modal ...