Cognition begins with a loop, not a task.
Darwin Zero investigates how an artificial system can develop adaptive behavior through endogenous regulation, sensorimotor interaction, local prediction and persistent internal reorganization.
The goal is to examine the architecture that makes learning possible, rather than implement cognitive capabilities as individual features.
What must exist before a system can learn?
What structural conditions allow an artificial system to build persistent adaptive organization through its own continuous operation?
Darwin Zero starts without acquired knowledge of its particular environment. Its initial architecture provides the conditions for interaction, while its organization is modified through successive cycles.
Four interacting mechanisms.
Not four independent cognitive functions, but parts of a continuous process whose interactions are the object of research.
Internal state
Bounded homeostatic variables generate changing internal conditions and influence the selection of actions without an externally imposed task objective.
Sensorimotor interaction
Actions change the system’s relation to its environment. Observations are tied to movement and pose, not processed as detached information.
Local error signals
Partial processing units anticipate observations. Differences between expectation and input modulate attention and subsequent activity.
Persistent reorganization
Repeated interactions reinforce or weaken local relationships, changing later predictions and behavior without a central store of predefined concepts.
Learning is tested across cycles.
The key question is not whether a system produces an interesting behavior once, but whether experience leaves changes that influence later behavior.
A mechanism must survive scrutiny.
Darwin Zero uses controlled environments and observable traces to test whether the intended architecture is actually responsible for what is observed.
The research does not claim that its current prototypes demonstrate general intelligence or human-like understanding.
Does behavior change after exposure, and does a learned change persist when the system encounters related conditions again?
Do prediction and adaptation degrade when the movement or location signals required by the hypothesis are removed or distorted?
Can the observed outcome be traced to partial processing, error signals and persistent changes rather than an unexplained global decision?
Research, demonstrations and context.
Discuss the mechanisms, not just the claims.
Questions, scientific critique and collaboration around autonomous cognitive architectures are welcome.