Agents & Reasoning

One of the clearest public anchors for scalable oversight and alignment research in the frontier-model era.

A strong person to follow for how Anthropic moved from assistant training into more explicit evaluation work around model behavior, red-teaming, and chain-of-thought faithfulness.

A useful researcher to follow if you care about the bridge between safety evaluation, human data, and how frontier models are turned into practical tools and benchmarks.

A useful person to follow if you care about the bridge between embodied-agent research and modern open-weight language-model systems, rather than treating those worlds as separate.

A strong person to know for the Mistral line of open-weight models, especially if you care about the arc from compact performant base models into mixture-of-experts, multimodal systems, and reasoning models.

A useful person to study if you care about alignment proposals that try to make superhuman systems legible enough for humans to supervise in practice.

A high-signal researcher for understanding how DeepMind approaches generality, especially in areas where reinforcement learning, multimodality, and large-scale systems meet.

A long-running builder of ML intuition whose influence spans Bayesian methods, reinforcement learning, and recent work on generalist and generative environments.

An important systems page because he is one of the named authors on speculative decoding, a technique that became part of the mainstream conversation about making large-model inference materially faster without changing outputs.