Implicit Ambient Binding (IAB): Structural Compression of Co-Occurring Environmental Signals

This paper introduces Implicit Ambient Binding (IAB), a learning principle addressing the unsupervised acquisition of structural knowledge from the natural co-occurrence of heterogeneous environmental signals visual, acoustic, and kinematic. IAB posits that such signals constitute simultaneous projections of a single latent structural state G, defined as the most compressed relational description jointly consistent with all observed modalities, thereby recasting learning as recovery of G rather than estimation of cross-modal statistical association. The principle is formally situated within the Implicit Thinking family of the AI Implicit paradigm, grounding representation in implicit rather than explicit definition, and is distinguished from Pearlian causal inference on epistemic rather than terminological grounds: IAB poses a Kolmogorov-theoretic compression problem, not an interventionist one, and requires neither directed acyclic graphs nor do-calculus. IAB is formalized through three binding pressures Reconstruction, Compression, and Cross-Modal Prediction from which a Binding Efficiency metric is derived and proven to constitute a strictly stronger condition than cross-modal predictive accuracy. Five falsifiable predictions are stated, positioning IAB as the knowledge-origin theory within the AI Implicit programme and resolving a structural gap left open by Bold Learning, The Implicit Tension, and the ECI framework concerning the emergence of structurally rich knowledge from unsupervised, ambient, multimodal experience.