Transition-Centric Memory and Directed Exploration: Beyond Compressive Observation Memory in ASRA
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.