Beyond Articulation: Intrinsic Operational Logic For Interactive 3D Assets

Simulating everyday object use requires operational behavior: rules governing how objects respond to manipulation over time. Authoring these rules is costly, and simulator-specific implementations limit reuse. We introduce Intrinsic Operational Logic (IOL), a declarative representation for portable behavior execution and reliable authoring by large language model (LLM) agents. An LLM specifies state predicates, temporal rules, and motion effects; automatic synthesis constructs the controller, and lightweight adapters connect the controller to different simulators. The controller also guides interaction-trace-based test creation; execution is compared with the LLM's expectations to diagnose and repair errors. On AuthorBench, our benchmark of 50 objects, IOL improves behavioral accuracy over procedural code; trace-based repair further improves over standard agentic self-testing. IOL-equipped objects also enable OperateBench, our benchmark of 25 manipulation tasks. GPT-6 Astra solves 92% of tasks in a privileged-state workflow and 56% in a separate workflow using simulated RGB-D observations, highlighting the challenge of complex object operations from visual observations.