Core Insights - The article discusses the importance of wireless perception technology in the context of embodied intelligence and spatial intelligence, emphasizing its ability to overcome traditional sensory limitations and enhance human-machine interaction [1] Group 1: Wireless Perception Technology - Wireless perception is becoming a key technology that allows machines to "see" beyond physical barriers and detect subtle changes in the environment, thus reshaping human-machine interaction [1] - The technology captures the reflective characteristics of wireless signals, enabling the perception of movements and actions from several meters away [1] Group 2: Challenges in Data Acquisition - A significant challenge in developing large models that understand physical principles (like electromagnetism and acoustics) is the scarcity of relevant data, as existing models primarily learn from textual and visual data [2] - The reliance on real-world data collection is insufficient to support the vast data requirements of large models [2] Group 3: SynCheck Innovation - The SynCheck framework, developed by researchers from Peking University and the University of Pittsburgh, provides synthetic data that closely resembles real data quality, addressing the data scarcity issue [3] - The framework was recognized with the best paper award at the MobiSys 2025 conference [3] Group 4: Quality Metrics for Synthetic Data - The research introduces two innovative quality metrics for synthetic data: affinity (similarity to real data) and diversity (coverage of real data distribution) [5] - A theoretical framework for evaluating synthetic data quality was established, moving beyond previous methods that relied on visual cues or specific datasets [7] Group 5: Performance Improvements with SynCheck - SynCheck demonstrated significant performance improvements, achieving a 4.3% performance increase even in the worst-case scenario where traditional methods led to a 13.4% decline [13] - In optimal conditions, performance improvements reached up to 12.9%, with filtered synthetic data showing better affinity while maintaining diversity comparable to original data [13] Group 6: Future Directions - The research team aims to innovate training paradigms for wireless large models by diversifying data sources and exploring efficient pre-training task architectures [18] - The goal is to establish a universal pre-training framework for various wireless perception tasks, enhancing the integration of synthetic and diverse data sources to support embodied intelligence systems [18]