Redeo

Language models are thought generators locked into next-token prediction. They optimize for coherence, not correctness — the first thing that comes to mind, not the best thing. This isn't a flaw in the model. It's a limitation of how we query it. The knowledge is already in there, buried in a probability distribution trained on most of what humans have ever written. But a probability distribution, by definition, doesn't represent reality and its structures. It represents what words tend to come next. No amount of reinforcement learning or post-training fixes this — once a model is deployed, it stops learning. It's frozen.

This is why thinking models improve performance but still hallucinate. They give the model a scratchpad, but the model is still thinking inside its own frozen distribution. Sometimes it gets stuck solving a problem and loops on nonsense until you — a human — inject more context. Prompting works precisely because we have direct access to reality. We live in it. The model's entire world is text and token probabilities. The structures of reality have to come from us — as search strategies, as guided exploration, as new ways for the model to think that it could never arrive at on its own.

Redeo makes search strategies programmable. You describe how a model should explore its own knowledge — generate candidates, critique them, argue from different angles, check for consistency — and the platform handles execution. The model doesn't get smarter. It gets better directions. And the better the directions, the more of what it already knows actually surfaces.