#llms

A new analysis shows that large language models excel at language tasks but struggle with seemingly simple visual reasoning—like reading analog clocks.

Mass-produced studies citing legitimate datasets are overwhelming journal editors, creating a crisis that worsens as AI improves at mimicking competent research.

A new architecture called SubQ targets 12 million token context windows while sidestepping the quadratic compute scaling that limits standard transformers.

Software engineer Bhavya Gupta argues that LLM document extractors are missing fixed-point iteration, a classical CS convergence technique that could make extraction far more reliable.

A new open-source repository walks developers through building a modern large language model from scratch, with every line of code annotated and explained in plain language.

A new Hacker News-featured tool promises AI analysis stripped of flattery, targeting the approval-seeking behavior researchers have flagged in mainstream models.

David Silver, who built AlphaGo at DeepMind, argues large language models are fundamentally capped by human data and has founded Ineffable Intelligence to pursue reinforcement learning instead.