IoT-Brain uses a neuro-symbolic Spatial Trajectory Graph to ground LLMs for verifiable semantic-spatial sensor scheduling, achieving 37.6% higher task success with lower resource use on a campus-scale benchmark.
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SMH-Bench supplies 1,100 stratified tasks in a verifiable smart-home simulator to measure LLM performance on explicit control, scheduling, ambiguity, and personalization as environment complexity grows.
EdgeFlow reduces mobile LLM cold-start latency up to 4.07x versus llama.cpp, MNN, and llm.npu by NPU-aware adaptive quantization, SIMD-friendly packing, and synergistic granular CPU-NPU pipelining at comparable accuracy.
Presents PEC-Home dataset for elliptical smart-home commands and shows LLMs achieve lower execution accuracy on elliptical inputs than complete commands even with dialogue history access.
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