New Paper 📄: LMs just want to explain themselves! When we SFT an LM on explanations of its own behaviors, do they learn to actually introspect, or do they merely imitate the original training distribution? We find evidence for the former. Despite training on a static set of explanations from a base model, the SFT-ed model explains its own current behaviors better than the base model’s behaviors, tracking behavioral drift even when we don’t explicitly train it to. We call this introspective coupling: self-explanations track a model’s own behavior as that behavior changes, and it shows promise in making introspection training a part of scalable post-training pipelines. 🧵

New @fulcrum_inc research: Inverse Rubric Optimization (IRO), a testbed for agent science. Long-horizon tasks are often noisy, making them hard to study. In IRO, an agent learns a hidden judge's preferences under a label budget. We observe rich agent behavior and smooth scaling.

How can we tell whether a brain region causally represents a visual concept, rather than merely correlating with it? Introducing BrainCause, a framework combining generative and brain models to create controlled stimuli and causally test neural representations. More below 🧠👇

Come check out our work on better utilizing agentic coding benchmark evaluation data, presented at #ICLR2026 Agents in the Wild Workshop!

🚨 We're open-sourcing Druids, a library for coordinating and deploying coding agents across machines. Our beta users have used Druids to work on open math problems, conduct ML "autoresearch," and make software faster.