4 citations · 4 across the 3 of their papers we have counts for
7 papers
LabEvolver: Training-Free Experience Evolution for Safe and Grounded Wet-Lab Agents
Jingya Wang, Yuyang Gao, Liuzhenghao Lv +2
We introduce LabEvolver, a training-free framework that equips safe and grounded wet-lab agents with episodic memory from execution experience. LabEvolver couples a state-grounded…
MoleCode unlocks structural intelligence in large language models
Zhiyuan Yan, Chen Liu, Boxuan Zhao +8
Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular representation, SMILES, compresses…
BioProAgent: Neuro-Symbolic Grounding for Constrained Scientific Planning
Yuyang Liu, Jingya Wang, Liuzhenghao Lv +1
Large language models (LLMs) have demonstrated significant reasoning capabilities in scientific discovery but struggle to bridge the gap to physical execution in wet-labs. In these…
BioProBench: A Corpus and Benchmark for Biological Protocol Reasoning in Autonomous Science
Yuyang Liu, Liuzhenghao Lv, Xiancheng Zhang +2
The realization of autonomous scientific experimentation is currently limited by LLMs' struggle to grasp the strict procedural logic and accuracy required by biological protocols.…
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
Hao Li, Liuzhenghao Lv, He Cao +6
Large language models are increasingly used in scientific domains, especially for molecular understanding and analysis. However, existing models are affected by hallucination issue…
Navigating Chemical-Linguistic Sharing Space with Heterogeneous Molecular Encoding
Liuzhenghao Lv, Hao Li, Yu Wang +5
Chemical language models (CLMs) are prominent for their effectiveness in exploring chemical space and enabling molecular engineering. However, while exploring chemical-linguistic s…