🤖 AI Summary
Current foundation models generate 3D content that, while visually plausible, lacks operability and thus struggles to support downstream tasks such as CAD or robotics. This work proposes Hylos, a novel system architecture that introduces contract-based design into spatial intelligence for the first time. Hylos employs a “spatial transaction” mechanism to maintain scene-level operational states, ensuring object recognizability, constraint satisfaction, and action feasibility. By integrating scene graph dependency tracking, constraint solving, and capability gap detection, the system guarantees verifiability and rollback capability for spatial modifications at transaction boundaries, while enabling upstream repairs grounded in causal dependencies. Experiments demonstrate that Hylos effectively identifies and corrects structural errors, transforming generated 3D content into a reliable foundation suitable for engineering applications and interactive world construction.
📝 Abstract
Foundation models can increasingly describe, reconstruct, and generate 3D objects, assemblies, scenes, and environments, but visually plausible spatial output is not yet operable 3D. A generated object or environment becomes useful to an agent only when the system can identify its entities, frames, surfaces, constraints, provenance, admissible actions, expected effects, and validation failures.
This paper introduces Hylos, a systems architecture for contract-bounded spatial intelligence. Hylos maintains scene-scale operability state over objects, assemblies, assets, surface anchors, assertions, action candidates, solver jobs, shared actuator invocations, capability gaps, and effect diffs. Durable spatial changes are routed through a SpatialTransaction: a commit boundary that resolves references, checks admissibility, protects invariants, projects effects, and returns commit, review, rollback, deferral, or capability-gap outcomes.
The paper is framed as a systems/position preprint with a focused artifact study rather than a broad benchmark. The study examines causal repair: a visible misalignment appears on a dependent component, while the supported repair lies upstream in the placement structure that controls it. The successful interaction traces the symptom through scene dependencies, selects a supported upstream interaction, and applies a validated change instead of directly editing visible geometry.
The broader claim is that spatial AI should be evaluated not only by visual quality, but by whether generated or edited 3D can become reliable substrate for CAD, robotics, simulation, inspection, manufacturing, and interactive world authoring.