6 related articles

A self-study roadmap from dynamical systems, causal inference, and state space models to world models—breaking down the core math needed to understand Dreamer, JEPA, and other frontier AI systems.

AI aces reasoning tests but may reason incorrectly. This article analyzes fake reasoning behind correct answers in LLMs, covering data contamination, memory effects, and methods like process supervision and counterfactual testing.

An open-source GitHub repo curates 30+ legally free AI/ML classic books covering deep learning, RL, NLP, computer vision & more, with automated link checking.

Awesome Free AI Books is an open-source repo with 30+ legally free AI & ML classic textbooks covering deep learning, reinforcement learning, NLP, LLMs, and more — all linking to official sources with weekly automated link checks.
Causal Theory Cracks Open the LLM Blac…
How can we solve the LLM black box problem? This article explores how causal theory powers mechanistic interpretability research — from causal intervention and activation patching to circuit discovery — and its implications for AI safety and alignment.

Are large language models truly intelligent? This article analyzes core AI limitations — pattern matching, hallucinations, reasoning deficits — and explores next-gen directions like inference-time compute, neuro-symbolic AI, and embodied intelligence.