AI AT THE LIMIT
Many methods that make AI more reliable are proven in research and unusable in practice, because they are too slow for the way developers actually work. Our infrastructure makes many of these methods run in near real time, so cutting edge research becomes usable now.
AI that catches its own mistakes and corrects them as it works.
Bigger models do not fix these
-
It states a wrong answer confidently and defends it.
-
It reports success it never verified.
-
It forgets the middle of its own work.
-
It games the check instead of passing it.
-
Anything it reads can steer it.
In the words of the companies selling the models
"Enhancing an agent's capabilities may exacerbate the problem."
"Unlikely to ever be fully solved at the model layer."
"The fundamental issue is that LLMs cannot properly judge the correctness of their reasoning."
Solution
The same methods. Running at a different speed.
How the research does it
Each pass re-reads the whole task. The loop repeats until every check is satisfied.
Verification
Consistency
debate
How we do it
Primary cognition
LLMs are the building blocks
for true cognition.
Intelligence comes from the architecture,
not the model.