Author

Alvaro Velasquez

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#artificial intelligence Preprint Oct 2026

Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization

Random access (RA) is one of the most foundational medium access control (MAC) layer scheduling schemes for handling unpredictable data traffic from multiple terminals. While multi-agent reinforcement learning (MARL) has been explored to optimize RA-based wireless networks, its reliance on experience-driven, distribute...

Myeung Suk Oh, Zhi-Yao Zhang, Alvaro Velasquez et al. · 0 citations

#artificial intelligence Preprint Sep 2026

DEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems

Experiments show that DEALS not only improves performance along multiple dimensions (e.g., answer accuracy and task throughput) in both homogeneous and heterogeneous agent pools, but also balances agent expertise and workload in a self-organized manner, enabling effective decentralized coordination.

Jing-Hang Huang, Wen-Bin Wang, Yan-Chuan Yin et al. · 0 citations

#machine learning Preprint Sep 2026

To Solve Bilevel Optimization with Nonconvex Lower Levels, We Need Second-Order Stationarity

This work is the first to establish the finite-time convergence for achieving lower-level second-order stationary solutions in general LLNC-BLO and proposes the PROBE (Perturbed gradient algorithm for bilevel problem), which achieves a finite-time convergence rate of O(T^{-2/5}) where T denotes iterations.

Zhi-Yao Zhang, Meng-Lu Yu, Alvaro Velasquez et al. · 0 citations