2 papers
cs.LG2026
An important Bottleneck in LLM Optimizers: Principle-Level Local Optima
Nian Ran, Tianhe Jia, Zhengyang Mu +6
Feedback-driven LLM optimizers are increasingly used for evolutionary search, Bayesian optimization, heuristic design, and scientific discovery. Yet rapid initial gains often give…
cs.CL2026
HTAM: Hierarchical Transition-Attended Memory for Operator Optimization
Yining Zhang, Mingyang Yi, Chen Wang +5
High-performance GPU kernels are essential for efficient LLM deployment, yet optimizing them remains expertise-intensive. Recent LLM-based code generation makes automatic GPU opera…