Li Sun

This survey systematically reviews resource consumption threats in large language models (LLMs) to provide a unified view of the problem landscape, from threat induction to mitigation.

The paper introduces a unified framework to fairly evaluate LLM agentic capabilities by standardizing diverse benchmarks and separating the effects of the LLM model from the surrounding framework and environment.

The paper introduces SpikeWFM, a novel hybrid architecture combining spiking neural networks (SNNs) and transformers, which significantly improves the robustness and accuracy of wireless foundation models for channel prediction against noise and interference.

This paper proposes ConsisFormer, a compute-efficient Transformer design for wireless foundation models (WFMs) using short-term channel consistency and adaptive token aggregation.