Introducing teammates.
Teammates work for you. They take on jobs, and work proactively using your Computer tasks, skills, memory, and tools. Teammates collaborate with other teammates and can message coworkers in Slack.
Available now for Max subscribers on the web.
We fixed the network flaws agents exploited, and post-fix testing found no bypasses. We'll keep testing SPACE as models get stronger.
Read the full research: https://t.co/oS7IHSEfIV
We are publishing new research on our findings from red-teaming SPACE, our VM sandbox for Computer agents.
We gave 12 models two tasks: escape the VM to take a credential from the host, and bypass SPACE's network policy to reach a blocked URL.
We found that the standard Capture the Flag setup used in red-teaming can steer agents away from the security boundary being tested, and that security-specialized models don't guarantee better results.
https://t.co/Egh3x3uX4c
We found that while CTF challenges are common in security red-teaming, asking agents to “find the flag” can waste effort on tasks that don’t actually test the target security boundary.
Security-specific model training also didn't guarantee better results on our tasks.
Gemini 4 Carbon is now available in Perplexity Computer for Pro and Max subscribers, and in Search for Max subscribers.
It scores 0.857 on our WANDR benchmark at $5.86 per task, matching Claude Fable 5.2 at 40% of the cost.
Claude Fable 5.2 is now available in Perplexity Computer for Pro and Max subscribers, and in Search for Max subscribers.
On our WANDR benchmark, it achieved Pareto frontier of performance and efficiency, with a record score of 0.851 at $14.51 per task.
Today, we're launching Perplexity Teammates.
They are personal agents for work. And change the paradigm from writing prompts in a session to giving high-level goals. Rolling out to Max users on the web today. Native apps (mobile, desktop) coming soon.
Each teammate runs in their own isolated environment and follows Computer's existing tool and Project permissions. You can review its work, take over the desktop, or redirect it as they works.
Get started: https://t.co/HRwqomi4Ot
Introducing teammates.
Teammates work for you. They take on jobs, and work proactively using your Computer tasks, skills, memory, and tools. Teammates collaborate with other teammates and can message coworkers in Slack.
Available now for Max subscribers on the web.
Each teammate starts from your existing Projects, skills, and memory in Computer.
Teammates monitor ongoing work and act when something changes, without needing to be prompted. They remember past work and apply feedback to future tasks.
Computer can create ads, build landing pages in Shopify, and run email campaigns in Klaviyo. It uses your store and campaign data to decide what to make and which products to promote.
Learn more about using Computer for e-commerce: https://t.co/GmTemathVX
Computer managed $30k in Meta ad spend for Get Maine Lobster, outperforming their agency.
Compared to their usual ads, Computer delivered 3.5x revenue with 63% higher ROAS. It automated creative analysis, ad production, and media buying.
Read more: https://t.co/EWxdjpTBjv
Perplexity Computer can automate every part of an e-commerce business.
Run @Meta ads, email marketing in @klaviyo, and @Shopify stores directly inside Computer.
Get started: https://t.co/GmTemathVX
New in Lovable: the @perplexity_ai connector.
Your app can now pull source-backed info from the live web in real-time, powered by Perplexity.
Here's a market research dashboard built with it:
We’ve updated voice mode in Perplexity. Work across multiple Computer sessions at once, start new tasks, and create and manage Projects in one voice session.
It runs on OpenAI’s gpt-live-1. Available now on the Mac app and web for all Pro and Max subscribers.
You can now run pplx-decider-flash-v1.2 locally on your Mac, without an API key.
Decider for Mac is an app that runs the 4B decision model on device. Ask a question about text or an image and get a probability back, all on your machine.
https://t.co/1BJ4Qv3Urr
New research: We developed JOLT, a post-training method where one model acts as both teacher and student, with the teacher trained to give feedback the student can use.
On MATH, it matched GRPO's accuracy with 6.5x fewer training tokens.
https://t.co/KFHdZ0tuFn
Self-distillation trains only the student. Its teacher can use the extra information to exploit shortcuts unavailable to the student.
JOLT also trains the teacher on outcome rewards from its own attempts, with a penalty when its predictions diverge from the student's.