Most AI progress today is driven by scaling more parameters, more data, more compute. Elyana explores a different hypothesis: that intelligence can be enhanced through memory, causal reasoning, abstraction, mental simulation, concept formation, and cross-domain transfer working together as a unified cognitive system.
We're especially interested in feedback around:
How users think Elyana should balance reasoning vs speed.
Whether long-term memory makes AI more useful in real-world workflows.
What types of problems require causal understanding rather than simple pattern matching.
Where current AI systems fail because they lack a world model or common-sense reasoning.
If you've used Elyana, we'd love to know where it surprised you, where it failed, and what cognitive capability you believe AI is still missing today.
The world modeling piece is a really thoughtful detail, it sets Elyana apart from the usual "predict the next token" framing. Curious how the autonomous experimentation loop handles ambiguity in real time.
How does the "autonomous experimentation" piece actually work in practice - does it spin up its own sandboxed environment to test things, or is that happening more abstractly in its internal world model?