We ran a fresh evolution on ScienceBoard, 169 tasks in six scientific apps like ChimeraX and GRASS GIS. Learning from 48 synthetic tasks, the evolved repository lifts the same model to 58.6%, up from 41.4% for Qwen CUA with its fixed interface.

The evolved repository contains interfaces nobody specified. One sends requests from a logged-in browser tab and gets reused across email, banking and chat sites. Another writes a whole block of LibreOffice cells at once.

Each iteration, the meta-agent selects a parent repository, mutates it or crosses it with a second one, scores the result and adds the descendant back to the population. It learns only from synthetic tasks that carry no success labels and never sees a benchmark task.

It costs $1.10 per task, against $25.50 for GPT-5.5 and $72.40 for Opus 4.8 💸 The model and harness stay fixed the whole time. S4 runs a meta-agent that studies the task agent's trajectories and evolves the repository of interfaces it works through.

With the interfaces S4 evolved, Qwen3.8-27B completes 21.3% of the 108 tasks. That's the highest binary success of any open-source computer-use agent. And it's 3.8x the same model with fixed interfaces.

Is the model the main factor for a computer-use agent? We think the interface matters a lot more. Today, computer-use agents are fundamentally bottlenecked by fixed, human-designed interfaces: raw clicks, scrolls, typing, and basic CLIs built for human eyes and fingers, not autonomous agents.

Our agent scientist, Agent S4, just got an open 27B model to beat GPT-5.5 and Claude Opus 4.8 on OSWorld V2, with the model's weights left exactly as they were. With the interfaces S4 evolved, Qwen3.8-27B completes 21.3% of the 108 tasks, the highest binary success of any open-source computer-use agent and 3.8x the same model with fixed interfaces. It costs $1.10 per task, against $25.50 for GPT-5.5 and $72.40 for Opus 4.8 💸 Full write-up: simular.ai/articles/agent-s4…

At the @aiDotEngineer World’s Fair, our CEO @angli_ai explored a future where one person could have a fleet of autonomous computers. That's what we are building with @sai_borg. your robosecretary. It starts with work you can reliably delegate to one computer. Then you start imagining what you could do with 10. Then 100. And Sai manages them for you. Build the thing you keep saving for someday #simular

Ever wondered what goes into making @sai_borg more reliable? @Richard_simular from @SimularAI’s research team was invited to BayLearn 2026. His presentation explored how computer agents can reuse successful steps and handle repetitive tasks more consistently. For more on how agents learn reusable workflows, read the paper Richard coauthored: arxiv.org/abs/2609.36927 Thanks to Santa Clara University for hosting, and to San José State University, UC Berkeley, and UC Santa Cruz for co-organizing the event 👏 #BayLearn2026 #AIResearch

This is what our intern Qingni spent this summer solving with our head of research @xwang_lk The team proposes LEASH, a training-free framework that unleashes the strong agent only when the weak agent's latent uncertainty signals it is needed, and keeps it on a leash afterward. Read more in our blog: simular.ai/articles/put-the-…