activity
20182026
most citedChemLLM: A Chemical Large Language Model

46 citations · 143 across the 154 of their papers we have counts for

collaborators
Showing cs.CLShow all

30 papers · 1 filter

cs.CL2026

When LLM Meets Tree Search: A Systematic View of Inference as Search in Large Language Models

Jiaqi Wei, Xiang Zhang, Yuejin Yang +10

As pretraining scaling laws approach saturation, Test-Time Scaling (TTS) has emerged as an important direction for improving reasoning by allocating inference-time compute to a fix…

cs.CL2026

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

Chen Tang, Yizhou Wang, Jianyu Wu +26

Structure-property relationships are foundational to biology, chemistry and materials science, where function, reactivity and physical response emerge from spatial, chemical and pe…

cs.CL2026

SciOrch: Learning to Orchestrate Expert LLMs for Solving Frontier Multimodal Scientific Reasoning Tasks

Jingru Guo, Xiangyuan Xue, Lian Zhang +6

Frontier scientific reasoning remains a major challenge for large language models (LLMs), where even the strongest commercial systems fall short of expert-level performance. A clos…

cs.CL2026

StraTA: Incentivizing Agentic Reinforcement Learning with Strategic Trajectory Abstraction

Xiangyuan Xue, Yifan Zhou, Zidong Wang +5

Large language models (LLMs) are increasingly used as interactive agents, but optimizing them for long-horizon decision making remains difficult because current methods are largely…

cs.CL2026

When to Think, When to Speak: Learning Disclosure Policies for LLM Reasoning

Jiaqi Wei, Xuehang Guo, Pengfei Yu +5

In single-stream autoregressive interfaces, the same tokens both update the model state and constitute an irreversible public commitment. This coupling creates a silence tax: addit…

cs.CL2025

Reflection Pretraining Enables Token-Level Self-Correction in Biological Sequence Models

Xiang Zhang, Jiaqi Wei, Yuejin Yang +8

Chain-of-Thought (CoT) prompting has significantly advanced task-solving capabilities in natural language processing with large language models. Unlike standard prompting, CoT enco…