3 citations · 8 across the 23 of their papers we have counts for
24 papers
Molecular LLM Agents: From Architectural Design to Scientific Autonomy
Jiatong Li, Wengyu Zhang, Weida Wang +8
Molecular science represents an important frontier for LLM-based agents. Unlike general agents that mainly operate over natural language, code, or web environments, molecular LLM a…
Intern-S2-Mobius: Foundation Model with Decoupled Knowledge and Reasoning
Kai Chen, Jifeng Ding, Ning Ding +44
We introduce Mobius-v0, an architecture that comprises a globally shared Memory (FFN) that stores knowledge vectors and multiple Reasoners (Self-Attn) that iteratively achieve comp…
Chemical Chain-of-Thought Functions as a Hallucination-Prone Molecular Scratchpad
Jiatong Li, Yuxuan Ren, Weida Wang +2
Chemical reasoning language models are expected to derive molecular answers through faithful chain-of-thought (CoT). However, across four reasoning model families and twelve chemis…
AMRM-Pure: Semantic-Preserving Adversarial Purification
Zhihao Dou, Zhiqiang Gao, Dongfei Cui +6
Adversarial purification is a defense technique that employs generative models to remove adversarial perturbations. Current methods often rely on powerful generators, typically dif…
Do LLMs Truly Generalize in the Molecular Domain? A Perturbation-Based Analysis
Jiatong Li, Weida Wang, Changmeng Zheng +4
Large Language Models (LLMs) have recently shown promise in molecular discovery, yet a gap remains between their probabilistic nature over discrete sequential tokens and the rigid…
Deep Research in Physical Sciences: A Multi-Agent Framework and Comprehensive Benchmark
Yigeng Jiang, Tengchao Yang, Taoyong Cui +25
Deep research agents are Large Language Model (LLM)-based systems designed for autonomous, multi-step scientific reasoning, and they hold immense potential for accelerating researc…
OmniMatBench: A Human-Calibrated Multimodal Reasoning Benchmark Across 19 Materials Science Subfields
Wanhao Liu, Jiaqing Xie, Qian Tan +10
As multimodal language models play an increasingly important role in scientific research, materials science offers a critical testbed due to its interdisciplinary, multimodal, and…
-mem: Efficient Online Memory for Large Language Models
Jingdi Lei, Di Zhang, Junxian Li +7
Large language models increasingly need to accumulate and reuse historical information in long-term assistants and agent systems. Simply expanding the context window is costly and…
MolViBench: Evaluating LLMs on Molecular Vibe Coding
Jiatong Li, Yuxuan Ren, Weida Wang +4
Molecular Vibe Coding, a paradigm where chemists interact with LLMs to generate executable programs for molecular tasks, has emerged as a flexible alternative to chemical agents wi…
Step-GRPO: Internalizing Dynamic Early Exit for Efficient Reasoning
Benteng Chen, Weida Wang, Shufei Zhang +2
Large reasoning models that use long chain-of-thought excel at problem-solving yet waste compute on redundant checks. Curbing this overthinking is hard: training-time length penalt…
PolyReal: A Benchmark for Real-World Polymer Science Workflows
Wanhao Liu, Weida Wang, Jiaqing Xie +12
Multimodal Large Language Models (MLLMs) excel in general domains but struggle with complex, real-world science. We posit that polymer science, an interdisciplinary field spanning…
InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery
Shiyang Feng, Runmin Ma, Xiangchao Yan +53
We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery across computational and empirical domains. The system is built on a structured architec…
SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence
Yiheng Wang, Yixin Chen, Shuo Li +33
We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike gene…
Chem-R: Learning to Reason as a Chemist
Weida Wang, Benteng Chen, Di Zhang +14
Although large language models (LLMs) have significant potential to advance chemical discovery, current LLMs lack core chemical knowledge, produce unreliable reasoning trajectories…
Plan Then Action:High-Level Planning Guidance Reinforcement Learning for LLM Reasoning
Zhihao Dou, Qinjian Zhao, Zhongwei Wan +10
Large language models (LLMs) demonstrate strong reasoning abilities via Chain-of-Thought (CoT), but their token-level generation encourages local decisions and lacks global plannin…
ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System
Dong Han, Zhehong Ai, Pengxiang Cai +16
Bayesian optimization (BO) is a powerful tool for scientific discovery in chemistry, yet its efficiency is often hampered by the sparse experimental data and vast search space. Her…
CMPhysBench: A Benchmark for Evaluating Large Language Models in Condensed Matter Physics
Weida Wang, Dongchen Huang, Jiatong Li +32
We introduce CMPhysBench, designed to assess the proficiency of Large Language Models (LLMs) in Condensed Matter Physics, as a novel Benchmark. CMPhysBench is composed of more than…
Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery
Jiatong Li, Weida Wang, Qinggang Zhang +6
Large language models (LLMs), especially Explicit Long Chain-of-Thought (CoT) reasoning models like DeepSeek-R1 and QWQ, have demonstrated powerful reasoning capabilities, achievin…
QCBench: Evaluating Large Language Models on Domain-Specific Quantitative Chemistry
Jiaqing Xie, Weida Wang, Ben Gao +5
Quantitative chemistry is central to modern chemical research, yet the ability of large language models (LLMs) to perform its rigorous, step-by-step calculations remains underexplo…
Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric Attention
Weida Wang, Changyong He, Jin Zeng +1
Depth images captured by Time-of-Flight (ToF) sensors are prone to noise, requiring denoising for reliable downstream applications. Previous works either focus on single-frame proc…
Control-R: Towards controllable test-time scaling
Di Zhang, Weida Wang, Junxian Li +10
This paper target in addressing the challenges of underthinking and overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning Models (LRMs) by introducing Reasoning…
DSADF: Thinking Fast and Slow for Decision Making
Zhihao Dou, Dongfei Cui, Jun Yan +5
Although Reinforcement Learning (RL) agents are effective in well-defined environments, they often struggle to generalize their learned policies to dynamic settings due to their re…
Speak-to-Structure: Evaluating LLMs in Open-domain Natural Language-Driven Molecule Generation
Jiatong Li, Junxian Li, Weida Wang +6
Recently, Large Language Models (LLMs) have demonstrated great potential in natural language-driven molecule discovery. However, existing datasets and benchmarks for molecule-text…
Critic-V: VLM Critics Help Catch VLM Errors in Multimodal Reasoning
Di Zhang, Junxian Li, Jingdi Lei +10
Vision-language models (VLMs) have shown remarkable advancements in multimodal reasoning tasks. However, they still often generate inaccurate or irrelevant responses due to issues…