jev-ultrafast indexes visible browser controls so Jev can pick an action + target in one request. A small LLM writes only when text input is needed. The repo includes a measured demo and its limits: https://t.co/hs8Z1H0TQB #BrowserUse#AIAgents
Browser agents rarely fail because they cannot click. They fail when the page changes and they keep clicking anyway. We tested Jev Ultrafast, a deliberately narrow next-step chooser. Here is what one controlled run actually showed. Thread:
The useful question is not how many buttons an agent can click. It is: can we see its next choice, bound its actions, and take over when needed? Source and limitations: https://t.co/hs8Z1H0TQB
A very interesting idea from Dream-RSI:
Maybe the next step of Agent self-improvement isn't making the model smarter.
It's making the **search process smarter**.
Instead of treating past agent trajectories as logs, Dream-RSI turns them into a replayable world.
The agent can โdreamโ inside this historical world, test different exploration policies, select a better one, and then deploy it back into the real environment.
So the loop becomes:
Agent โ Experience โ Better Search Policy โ New Experience โ ...
The key question shifts from:
โHow do we make the model smarter?โ
to:
**โHow do we make the agent better at deciding what to try next?โ**
That feels like a much more interesting direction for recursive self-improvement.
Maybe the future is not just **self-training**.
It is **self-search**.
#AI #AIAgents #LLM #SelfImprovement #AIResearch