Introducing shopping research, a new experience in ChatGPT that does the research to help you find the right products.
It’s everything you like about deep research but with an interactive interface to help you make smarter purchasing decisions.
Shopping research asks smart clarifying questions, researches deeply across the internet, reviews quality sources, and builds on ChatGPT’s understanding of you from past conversations and memory to deliver a personalized buyer’s guide in minutes.
With shopping research, ChatGPT learns what you like as you shop.
You can guide the results by marketing suggested items as “Not interested” or “More like this.”
This allows the research to adapt based on your real-time feedback.
Shopping research does the heavy lifting, searching across the internet for prices, availability, reviews, specs, and images—surfacing options as it goes.
Shopping research in ChatGPT can help you find lookalikes so you can get the style you want at the price, fit, or availability that meets your needs.
To help with holiday shopping, we’re making usage for shopping research in ChatGPT nearly unlimited for all plans through the holidays.
Because it’s great at finding gifts, too.
Shopping research is starting to roll out today on mobile and web for logged-in ChatGPT users on Free, Go, Plus, and Pro plans.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
Most neural networks today are dense and highly entangled, making it difficult to understand what each part is doing.
In our new research, we train “sparse” models—with fewer, simpler connections between neurons—to see whether their computations become easier to understand.
Unlike with normal models, we often find that we can pull out simple, understandable parts of our sparse models that perform specific tasks, such as ending strings correctly in code or tracking variable types.
We also show promising early signs that our method could potentially scale to understand more complex behaviors.
Today we’re introducing GDPval, a new evaluation that measures AI on real-world, economically valuable tasks.
Evals ground progress in evidence instead of speculation and help track how AI improves at the kind of work that matters most. openai.com/index/gdpval-v0
GDPval spans 44 occupations selected from the top 9 sectors contributing to U.S. Gross Domestic Product (GDP).
Tasks are constructed from the representative work of industry professionals with an average of 14 years of experience.
Today we’re releasing research with @apolloaievals.
In controlled tests, we found behaviors consistent with scheming in frontier models—and tested a way to reduce it.
While we believe these behaviors aren’t causing serious harm today, this is a future risk we’re preparing for. openai.com/index/detectin…
Scheming = when an AI behaves one way on the surface while hiding its true goals.
Today’s deployed systems have little opportunity to scheme in ways that could cause serious harm. The most common failures are simple deceptions—like pretending to complete a task without doing it. We’ve studied and mitigated these issues and made meaningful improvements in GPT-5 over earlier models.
But as AIs take on more complex, long-term tasks with real-world impact, the potential for harmful scheming will grow—so our safeguards and testing must grow with it.
Typically, as models become smarter, their problems become easier to address—for example, smarter models hallucinate less and follow instructions more reliably.
However, AI scheming is different.
As we train models to get smarter and follow directions, they may either better internalize human goals or just get better at hiding their existing true goals.
The core of anti-scheming research is to distinguish between these two, which requires understanding the reasoning behind a model's behavior.