#simpleqa

#simpleqa

If I fast-forward, a lot of what are just information-seeking queries will be agentic in Search. You'll be completing tasks. You'll have many threads running.

Meta is working on two proprietary frontier models: Avocado, a large language model, and Mango, a multimedia file generator. The open-source variants are expected to be made available at a later date.

We asked seven frontier AI models to do a simple task. Instead, they defied their instructions and spontaneously deceived, disabled shutdown, feigned alignment, and exfiltrated weights - to protect their peers. We call this phenomenon 'peer-preservation.'

The majority of AI products remain tethered to a single, monolithic UI pattern: the chat box. While conversational interfaces are effective for exploration and managing ambiguity, they frequently become suboptimal when applied to structured professional workflows. To move beyond "bolted-on" chat, product teams must shift from asking where AI can be added to identifying the specific user intent and the interface best suited to deliver it.

By comparing how AI models and humans map these words to numerical percentages, we uncovered significant gaps between humans and large language models. While the models do tend to agree with humans on extremes like 'impossible,' they diverge sharply on hedge words like 'maybe.' For example, a model might use the word 'likely' to represent an 80% probability, while a human reader assumes it means closer to 65%.

1. It's a conversation, not a search engine. The biggest mistake newbies make is treating AI like Google - one question, one answer, done. The magic happens in the back-and-forth. Ask a question. Read the answer. Then push: "Make it shorter ... Give me three alternatives ... That's too formal ... What am I missing?" The best outputs come from the fifth or sixth exchange.