1 citations · 1 across the 9 of their papers we have counts for
9 papers
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…
Towards Reliable Agentic Progressive Text-to-Visualization with Verification Rules
Wenxin Xu, Chen Jason Zhang, Xiaoyong Wei +4
Text-to-Visualization (Text-to-Vis) translates natural language queries into visualization query languages, enabling non-expert users to perform data analysis. However, most existi…
MarkovScale: Towards Optimal Sequential Scaling at Inference Time
Youkang Wang, Jian Wang, Rubing Chen +3
Sequential scaling is a prominent inference-time scaling paradigm, yet its performance improvements are typically modest and not well understood, largely due to the prevalence of h…
To Retrieve or To Think? Cross-Boundary Context Evolution for Multi-hop Complex Reasoning
Rubing Chen, Jian Wang, Wenjie Li +2
Current context augmentation methods, such as retrieval-augmented generation, play a crucial role in bridging a model's internal knowledge boundary and external evidence for multi-…
OptPO: Optimal Rollout Allocation for Test-time Policy Optimization
Youkang Wang, Jian Wang, Rubing Chen +3
Test-time policy optimization enables large language models (LLMs) to adapt to distribution shifts by leveraging feedback from self-generated rollouts. However, existing methods re…
Benchmarking for Domain-Specific LLMs: A Case Study on Academia and Beyond
Rubing Chen, Jiaxin Wu, Jian Wang +5
The increasing demand for domain-specific evaluation of large language models (LLMs) has led to the development of numerous benchmarks. These efforts often adhere to the principle…
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…
OptScale: Probabilistic Optimality for Inference-time Scaling
Youkang Wang, Jian Wang, Rubing Chen +1
Inference-time scaling has emerged as a powerful technique for enhancing the reasoning performance of Large Language Models (LLMs). However, existing approaches often rely on heuri…
Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue
Jian Wang, Chak Tou Leong, Jiashuo Wang +3
Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as res…