OpenAI released aggregated data linking consumer ChatGPT work-related use to 165 activity categories across nearly 100 countries, covering quarterly periods from early 2025 through the third quarter of 2026. Enterprise conversations and Codex use are excluded. The data describes sampled use, not productivity gains, job displacement or the whole AI market.
METTA combines within-episode neural memory with compact summaries across episodes, according to its preprint abstract. The authors report adaptation results on held-out reinforcement-learning tasks. This is research on meta-RL agents, rather than evidence that a general-purpose language-model assistant has acquired reliable long-term memory.
The authors propose choosing which partially generated solutions to continue by directly optimizing pass-at-k. Their abstract reports tests across open-weight reasoning models. The method tries to retain promising but varied reasoning paths, rather than optimizing quality and diversity separately; these are preprint claims, not independently reproduced results.
The authors compare trained classifiers and prompted language models under explicit costs for correct routing, human triage and mistakes. Their abstract reports that abstention policies can reduce routing cost, while better calibration alone need not improve a model's ability to distinguish correct answers. The findings are limited to the tested dataset, prompts and illustrative costs.