most citedAgentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis

2 citations · 2 across the 2 of their papers we have counts for

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cs.AI2026

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…

cs.AI2026

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…

cs.AI2025

Beyond Chemical QA: Evaluating LLM's Chemical Reasoning with Modular Chemical Operations

Hao Li, He Cao, Bin Feng +6

While large language models (LLMs) with Chain-of-Thought (CoT) reasoning excel in mathematics and coding, their potential for systematic reasoning in chemistry, a domain demanding…