AgnesHarness 连接物理世界：让每个可编程设备成为硅基生命

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We want ai to help people get things done in the real world. That means going beyond a conversation. It means working with tools, responding to feedback, and interacting with the physical world.

We hope to push beyond the limits of data work and expand the boundaries of ai potential. At Agnes, we are bringing all these capabilities together.

Our vision is one connected world where intelligence, artificial beings can communicate with each other through natural languages.

Traditionally model training involves collecting customer traces, thin size training data, and updating the model parameters.

This process can take considerable time and involve a lot of human supervision. With agnes embedded test time training, we are exploring a faster approach.

Through our dual loop, harness model dual system, we bring data collection, training, and inference into a unified task cycle.

In our experimental setting, an improvement cycle can take as little as 30 minutes. This brings learning much closer to real time involvement.

We are testing this approach across games, robotic manipulation, and scientific research. In honor of kings, a large language models works alongside the decision model to distill the skills of state of the art models.

Into an agent system with continuous online learning. The goal is to retain advanced capabilities while delivering more stable performance at a lower cost.

But a model that learns faster is only the beginning. Real world tasks demand, reliable execution, not just intelligent responses.

That's why we built agnes agent hollis or agnes hollis. It connects models with tools manages long horizon tasks and makes execution traceable and reliable.

By combining acts harness with test time training, we're working towards dual evolution models that learn from experience and agents that continuously improve how they work with model.

How one standard we expand this intelligence into the physical world. It gives agents a common way to connect with different devices and let programmable devices speak to each other.

While keeping precise motion and safety under hardware control, from robots to industrial equipments, language models can help coordinate complex physical tasks.

Intentions can now go beyond understanding instructions to helping carry them out. How can we turn a model's understanding and reasoning into precise physical actions?

Through natural language interaction, we're exploring how robots can understand human instructions, identify targets, and carry out complex operations in the real world environments.

we are also working with industry partners to bring a i into the physical world, including applications in electrical power operations.

Agnes provides visual understanding and task reasoning, while our partners contribute robot hardware and motion control.

Together, we are putting these capabilities to the test. In real world operational environments, from continuous learning to reliable execution, from digital intelligence into physical action, we are bringing these capabilities together.

Our vision is to build intelligence that learns, acts, and evolves in the real world, and shared intelligence that connects everything.

Starting with agnes today.