The foundational behavior of adaptive interfaces. Information doesn't appear and disappear—it rises and settles. Like a living surface responding to touch, the interface elevates relevant content when attention demands detail, then gradually returns to a settled state when confidence is high.

The interface breathes with human attention, creating a living surface that adapts rather than overwhelms.

This creates a fundamentally different relationship with information. Instead of managing visibility through explicit controls, users influence density through their natural behavior. The system learns what "intent" looks like and responds accordingly.

Multiple density layers exist simultaneously, each responding to different aspects of user context. Spatial proximity, temporal urgency, semantic relevance, and interaction history all contribute to what surfaces and what remains dormant.

The system maintains awareness across all layers while presenting only what serves the current moment. A calendar event gains density as the meeting approaches. Location-relevant information rises when you arrive somewhere new. These layers interweave, creating a rich topology of potential information.

Human on the loop, not in the loop.

Bayesian inference continuously models user intent from multi-domain signals. The system predicts needs before explicit requests, surfacing relevant information at the moment of thought. Confidence bands expand and contract as sensor fusion refines understanding.

This isn't about reading minds—it's about reducing the gap between intention and action. The interface becomes anticipatory rather than reactive, but always maintains human agency. Users can always override, correct, or ignore suggestions.

The death of discrete applications. From rigid app containers to fluid, context-aware surfaces. Information reorganizes itself around intent rather than requiring humans to navigate predetermined structures.

The current model forces users to think in terms of apps: "Which app has this?" The adaptive model inverts this: the system presents what's relevant, regardless of which app it comes from. Boundaries dissolve. Context flows.

The interface becomes ambient—always present but never demanding, surfacing exactly what's needed in the moment of need.

Multi-domain sensor fusion creates a living topology of information density. Spatiotemporal patterns, behavioral models, and contextual signals merge into a unified inference engine. The system maintains continuous awareness, dynamically allocating interface real estate based on probabilistic intent modeling.

This is the full picture—thousands of data points forming a coherent model of user state. Not surveillance, but awareness. The system knows where you are, what you're doing, what you've done before in similar contexts, and what you might need next.

The probabilistic world model that powers intent inference. Beliefs propagate through a network of observations, updating continuously as new signals arrive. Each data point shifts probability distributions, refining the system's understanding of user state.

This isn't a simple if-then rule system. It's a living model that handles uncertainty, conflicting signals, and novel situations. Confidence levels vary across different aspects of the model, and the interface reflects this uncertainty in its density responses.

Parallel hypothesis evaluation funnels converging to conclusions. Multiple potential interpretations of user intent compete simultaneously, with evidence flowing through each pathway. The winning hypothesis determines what surfaces, but runners-up remain available if the primary interpretation proves wrong.

This architecture enables graceful handling of ambiguity. Rather than committing to a single interpretation, the system maintains multiple possibilities until confidence threshold is reached. The result is an interface that rarely feels wrong, because alternatives are always just below the surface.