As a mom of two toddlers, Lauren uses ChatGPT to find time for herself through flexible workouts that fit into the unpredictable rhythms of her busy life.
@openai
-
ChatGPT Helps Millions Daily with Health Questions
By
–
Every day, millions of people ask ChatGPT about their health – from breaking down medical information, preparing questions for their doctor's appointments, to helping people manage their overall wellbeing.
-
Capability Overhang: Bridging Gap Between AI Models and User Adoption
By
–
Capability overhang means too many gaps today between what the models can do and what most people actually do with them. 2026 Prediction: Progress towards AGI will depend as much on helping people use AI well, in ways that directly benefit them as on progress in frontier models
-
ChatGPT Launches Your Year Feature Rollout
By
–
If you're in one of the countries above, check back throughout the day to see if "Your Year with ChatGPT" has rolled out to you. You can also try adding the "Your Year with ChatGPT" app by tapping the + sign and asking Chat, "show me my year with ChatGPT."
-
ChatGPT Now Lets Users Customize AI Personality Traits
By
–
You can now adjust specific characteristics in ChatGPT, like warmth, enthusiasm, and emoji use. Now available in your "Personalization" settings.
-
Chain-of-thought monitoring as complementary mechanistic interpretability approach
By
–
We view chain-of-thought monitoring as complementary to mechanistic interpretability, not as a replacement for it. Because we believe that chain-of-thought monitoring is incredibly useful as a window into a model’s brain and could be a loadbearing layer in a scalable control
-
Monitor Capability and Chain-of-Thought Reading Enhance AI Performance
By
–
What the monitor gets to read and the capability of the monitor matters. Stronger monitors that can read CoTs and use more test-time compute get much better fast. Also, post-hoc follow-ups (by asking the model to elaborate) often surface previously unspoken thoughts and boost
-
RL Reasoning Tradeoff: Monitorability vs Inference Compute
By
–
RL at today’s frontier doesn’t seem to wreck monitorability and can help early reasoning steps. But there’s a tradeoff: smaller models run with higher reasoning effort can be easier to monitor at similar capability — at the cost of extra inference compute (a “monitorability
-
Chain-of-Thought Monitoring for Better Model Oversight
By
–
Monitoring a model’s chain-of-thought is far more effective than watching only its actions or final answers. The more a model “thinks” (longer CoTs), the easier it is to spot issues.
-
Measuring Chain-of-Thought Monitorability in AI Models
By
–
To preserve chain-of-thought (CoT) monitorability, we must be able to measure it. We built a framework + evaluation suite to measure CoT monitorability — 13 evaluations across 24 environments — so that we can actually tell when models verbalize targeted aspects of their