Working notes on papers from OpenAI, Anthropic, Transformer Circuits, and Stanford CGRI, read for what they imply about deploying agents and rebuilding workflows in real organizations.
Anthropic's crosscoders research examines differences between models that output evaluations may miss.
What emotion-like patterns inside models could mean for their behavior in a workflow.
What OpenAI's internal coding-agent monitoring suggests about oversight in other organizations.
How to use an LLM to measure qualitative work, and where calibration matters.
What OpenAI's Graviton paper suggests about the cost of running agents in production.
Agents that run for hours need different checks and handoffs from agents that answer a single prompt.
What teams can learn from Anthropic's large-scale user interview research.
How trust affects the time and effort people spend coordinating work.