7 papers
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? An Agentic Approach for Context Evolution
Rubing Chen, Jian Wang, Wenjie Li +2
Current context augmentation methods, such as retrieval-augmented generation, are essential for solving knowledge-intensive reasoning tasks. However, they typically adhere to a rig…
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…
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…
MolGround: A Benchmark for Molecular Grounding
Jiaxin Wu, Ting Zhang, Rubing Chen +4
Current molecular understanding approaches predominantly focus on the descriptive aspect of human perception, providing broad, topic-level insights. However, the referential aspect…