Tianze Cao

Jiazhen Lei, Tianze Cao, Yuxin Sha et al.

Translating user intents into physical radio signals represents the critical yet notoriously tedious final step in wireless prototyping, as it requires intricate knowledge of physical layer details and presents immense implementation challenges. Large Language Models (LLMs) and multi-agent systems have revolutionized conventional software engineering, raising the compelling question of whether they can resolve these formidable difficulties. However, our investigations reveal that current models experience significant limitations and fail to accomplish this task when applied to radio signal generation. This performance degradation primarily stems from severe domain ignorance and a fundamental insensitivity to physical hardware constraints. To bridge this gap, we introduce RadioMaster, a fully autonomous multi-agent framework designed to seamlessly translate user input into real-world wireless emissions. RadioMaster operates on three synergistic pillars: RadioWiki for domain-specific knowledge retrieval, RadioAgent for collaborative I/Q sample generation alongside hardware configuration, and RadioEmulator for closed-loop physical layer verification. Furthermore, we construct RadioBench, the first comprehensive benchmark tailored specifically for the radio signal generation domain. Extensive real-world evaluations demonstrate that RadioMaster significantly outperforms state-of-the-art (SOTA) baselines regarding configuration viability and signal fidelity.

Chunliang Li, Tianze Cao, Sanyuan Zhao

Visual Autoregressive (VAR) modeling inefficiently applies a fixed computational depth to each position when generating high-resolution images. While existing methods accelerate inference by pruning tokens using frequency maps, their binary hard-pruning approach is fundamentally limited and fails to improve quality even with better frequency estimation. Observing that VAR models possess significant depth redundancy, we propose a paradigm shift from pruning entire tokens to adaptively allocating per-token computational depth. To this end, we introduce DepthVAR, a training-free framework that dynamically allocates computation. It integrates an adaptive depth scheduler, which assigns computational depth via a cyclic rotated schedule for balanced, non-static refinement, with a dynamic inference process that translates these depths into layer-major masks, selectively applies transformer blocks, and blends the resulting codes to ensure each token's influence is proportional to its processing depth. Extensive experiments show that DepthVAR achieves 2.3$\times$-3.1$\times$ acceleration with minimal quality loss, offering a competitive compute-performance trade-off compared to existing hard-pruning approaches. Code is available at https://github.com/STOVAGtz/DepthVAR

Jiazhen Lei, Fengyuan Zhu, Tianze Cao et al.

The emergence of Ambient IoT signals a paradigm shift toward massive batteryless networking. However, the absence of an agile physical layer substrate remains a fundamental barrier to research and standardization. Current testbeds are hindered by decoupled radio paths, high static power, and cumbersome control methods, which stifle rapid protocol prototyping. In this paper, we present Janus, the first hybrid active-passive configurable radio architected for agile Ambient IoT networking. Janus introduces a parameterized architecture that unifies passive and active transmission into a single RF front end, abstracting complex physical layer behaviors into concise parameters. This design enables a system-level control plane for dynamic mode transitions and an energy management plane for fine-grained harvesting across multiple sources. We implement a compact PCB prototype and evaluate its performance across diverse protocol landscapes, including 3GPP A-IoT, IEEE 802.11 AMP, and Bluetooth SIG. Our experimental results demonstrate that Janus achieves communication performance on par with dedicated radios while significantly reducing configuration overhead. Ultimately, Janus serves as a versatile enabler for validating emerging protocols and accelerating the standardization of next-generation low-power networks.