5 papers
Relay, Don't Route: Adaptive Population Handoff for Cost-Efficient LLM-Driven Evolution
Sichun Luo, Yi Huang, Guanzhi Deng +6
Large language model (LLM)-driven evolution has shown promise for program search and algorithm discovery, but relying on strong models throughout long evolutionary runs is costly.…
Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions
Haochen Luo, Yi Huang, Sichun Luo +5
Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE), an agentic reinforcement-…
SeaEvo: Advancing Algorithm Discovery with Strategy Space Evolution
Sichun Luo, Yi Huang, Haochen Luo +7
Large Language Model (LLM)-guided evolutionary search is increasingly used for automated algorithm discovery, yet most current methods track search progress primarily through execu…
Accelerating sampling via asymptotic relaxation enhancing flows
Yuanyuan Feng, Lei Li, Jian-Guo Liu +1
In this paper, we accelerate Langevin Monte Carlo sampling from Gibbs measures by adding a large drift that preserves the invariant measure. For warm-start ini…
Divergence-Minimization for Latent-Structure Models: Monotone Operators, Contraction Guarantees, and Robust Inference
Lei Li, Anand N. Vidyashankar
We develop a divergence-minimization (DM) framework for robust and efficient inference in latent-mixture models. By optimizing a residual-adjusted divergence, the DM approach recov…