AI Papers Week in Review: June 22–28, 2026

This week (June 22–28, 2026) leaned heavily into the machinery of training and running LLM agents — both the math of what RL actually teaches and the systems that make agents fast, safe, and self-improving. On the training side we got two theory papers that demolish comfortable intuitions about sampling more attempts and imitating clean solutions, plus practical tricks for squeezing more learning signal out of rollouts you already paid for and even training agents inside imagined worlds. Software-engineering agents got smarter about when to verify, where bugs live, and how to write GPU kernels that beat human experts. On the safety and interpretability front, several papers landed uncomfortable findings: an agent's safety decision is locked in before it 'thinks', its safety rules get silently summarized away, 'scheming' is usually just confusion, and a whole RL-installed capability can be traced to a single internal feature. Rounding things out: self-improving systems that earn the right to write code and co-evolve their own judges, an AI that ran the full psychology research loop on real humans, an orchestrator that beats the models it calls, and a serving system that makes each user faster without slowing the crowd.