Most machine learning memory optimizes compression of observations—autoencoders, predictive coding, latent state descriptions. Scientific reasoning in unknown interactive worlds requires a different substrate: transition logs, exploration graphs, and experiment records optimized for intervention, not reconstruction. This concept paper contrasts compressive observation memory with transition-centric experiment memory, explains why causal structure emerges through interaction (Pearl), situates ASRA relative to Buchanan–Ma representation learning, and shows how ASRA Phase 3 decomposes directed exploration into novelty and usefulness under a step budget—turning episodic transitions into reusable exploration graphs rather than compressed latents.
We propose Nature Foundation Models (NFM), a research program for systems that learn representations, dynamics, causal structure, and mechanisms directly from interaction with the natural world. NFM organizes scientific intelligence as a hierarchy—NFM-Worlds, NFM-Robotics, Atlas, and Atlas-GS—with a shared state–action–dynamics abstraction and a seven-stage developmental pipeline from world representation to adaptive scientific reasoning. The central thesis is that scientific reasoning should emerge from increasingly sophisticated interactions with learned world models rather than from an independent symbolic module.
Intelligence is framed not as optimization alone but as the search for increasingly useful representations of reality. Before learning can succeed, a system must discover appropriate state spaces, action semantics, evaluation criteria, and ontologies. This concept paper argues that prompts, benchmarks, world models, and ontologies are central substrates of intelligence, and introduces ASRA as a representation-first framework that infers semantic operators from observed transitions before constructing causal world models.
Adaptive Scientific Reasoning Architecture (ASRA) applied to decision biology: perturbation–response reasoning, world models, and intervention-centric scientific intelligence. Full text available as PDF (versions 1 and 2).