Most models behind agents don't learn while they're running. You train them, freeze the weights, and deploy them. Everything else gets built around them: prompts, tools, retrieval, memory, routing, gu

Most models behind agents don't learn while they're running. You train them, freeze the weights, and deploy them. Everything else gets built around them: prompts, tools, retrieval, memory, routing, guardrails. That works well for chat because every interaction has a clean boundary. The user asks a question, the model answers, and the loop ends. AdaJEPA updates the world model inside the control loop instead. The agent takes an action, sees what actually happened, nudges the latent model toward reality, then replans. No retraining run. No bigger context window. No memory trick. Just make the model a little less wrong before the next decision.

Most models behind agents don't learn while they're running. You train them, freeze the weights, and deploy them. Everything else gets built around them: prompts, tools, retrieval, memory, routing, gu

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