4 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
Divergence Decoding: Training-Free Capability Fusion
Yimi Wang, Hao Li, Shuo Yang +6
While large language models excel in reasoning, these generalists often lack knowledge for specialized scientific domains. Conversely, domain models~(specialists), while knowledgea…
Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data
Chengchun Liu, Zhiyuan Yan, Li Yuan +5
Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are spar…
ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
Bin Zhu, Yanhao Jia, Kexin Zhao +12
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. However, a critical ambiguity pe…
From Answers to States: Verifiable Process-Level Evaluation of Chemical Reasoning in Large Language Models
Hongyu Guo, Hao Li, He Cao +2
Large language models are increasingly used as chemistry assistants, yet most chemistry benchmarks still score only final answers. This masks a critical failure mode: a model may o…
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…
Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis
Hao Li, He Cao, Shenyao Peng +7
Language models are revolutionizing the biochemistry domain, assisting scientists in drug design and chemical synthesis with high efficiency. Yet current approaches struggle betwee…