Jun Lin

The paper proposes GUIDE, a physics-guided deep unfolding framework that enables practical, real-time cross-band channel prediction for AI-RAN by embedding wireless channel physics, significantly improving beamforming gain while maintaining high inference speed.

The paper proposes a novel nonparametric mutual information estimator to robustly quantify dependence between heterogeneous temporal data, specifically continuous time series and discrete event sequences.

The paper introduces the concept of Search-Time Contamination (STC), demonstrating that deep research agents can leak information from public benchmarks via web search, leading to an overestimation of their true reasoning ability.

This paper introduces a controlled, two-player extension of the Alternate Uses Test (AUT) for comparing human-human and human-AI co-creation under matched conditions, demonstrating equivalent originality with a GPT-4 partner and human partner, and identifying factors influencing performance.