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cs.AI2026
Self-Evolving Algorithm-Design Agents: Escaping In-Context Evolutionary Stagnation via Population-Curated Policy Optimization
Chen Lu, Ke Xue, Siyuan Xu +2
Large language models are increasingly participating in complex real-world tasks in the form of algorithm-design agents, designing and refining algorithms. Many successful algorith…
cs.AR2026
How Can Reinforcement Learning Achieve Expert-level Placement?
Ruo-Tong Chen, Ke Xue, Chengrui Gao +7
Chip placement is a critical step in physical design. While reinforcement learning (RL)-based methods have recently emerged, their training primarily focuses on wirelength optimiza…
cs.AR2026
FlowPlace: Flow Matching for Chip Placement
Peng Xie, Ke Xue, Yunqi Shi +6
Chip placement plays an important role in physical design. While generative models like diffusion models offer promising learning-based solutions, current methods have the followin…