Modern IoT systems are transitioning from passive monitoring to intent-driven operation. However, a critical gap exists between high-level human intent (e.g., "Find my backpack lost between the library and the gym") and the precise physical sensor actions required to fulfill it.
Existing benchmarks often focus on pure QA or code generation, overlooking the embodied and spatial reasoning capabilities required for real-world cyber-physical systems.
TopoSense-Bench introduces the S³ challenge, requiring LLMs to:
- Reason Spatially: Understand complex topological relationships (connectivity, floor transitions) in a large-scale digital twin.
- Act Proactively: Select the optimal subset of sensors from a massive network (2,510 cameras) to satisfy a query, rather than just answering a text question.
- Ground in Reality: Map vague natural language to concrete sensor identifiers (e.g.,
teaching_building_1_camera_03).
By mastering this benchmark, LLMs demonstrate the capability to serve as the "brain" for large-scale smart city and smart campus infrastructures, moving beyond chatbots to actionable physical agents.