2 citations · 5 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
LabOPBench: A Multimodal Benchmark for Evidence-Grounded Laboratory Decision Making
Jingyuan Li, Yujie Liu, Yijie Dai +18
Laboratory experiments involve more than following predefined protocols; they require adaptive decision-making as the experimental state changes. Existing scientific benchmarks rar…
WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
Zhongzheng Li, Qingsong Ran, Shikun Feng +5
Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suffer from poor sample efficienc…
CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials
Yanjie Li, Jian Xu, Xu-Yao Zhang +4
Property prediction and inverse structural design of catalytic materials are typically modeled as two independent tasks: the former predicts target properties from given structures…