Learning Systems and Innate Behavior

Most contemporary work on artificial agents — including the current generation of large language model agents — treats motivation as something to be specified at runtime, and treats learning as something that ends at deployment. We argue that what makes a creature feel alive is not the sophistication of its behavior but the presence of innate stakes: internal states it did not choose, cannot disable, and must work to keep within viable ranges. For humans, physical stress is a canonical example. And what enables a creature to grow into its own intelligence is not a finished pretrained model, but an architecture that keeps learning from its own innate experience, driven by those stakes. We ground the argument in a working 2019 reinforcement-learning prototype — with documentation framing the reward signal as "pain and pleasure" — and we argue that the transformer and recent LLM architectures are the first ones flexible enough to play the role of a generalized training mass at initialization. Combining these two observations gives a concrete research vision: smaller models that learn during their lifespan, in receptacles that have something at stake, rather than ever-larger scaling of models on internet data. As of mid-2026, this gap remains open. We sketch the implementation path, a four-direction research program, and the direction we intend to explore.