12 papers
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…
Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design
Wengyu Zhang, Xiao-Yong Wei, Qing Li
Text-guided molecular design is a key capability for AI-driven drug discovery, yet it remains challenging to map sequential natural-language instructions with non-linear molecular…
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…
Adaptive Multi-Agent Reasoning for Text-to-Video Retrieval
Jiaxin Wu, Xiao-Yong Wei, Qing Li
The rise of short-form video platforms and the emergence of multimodal large language models (MLLMs) have amplified the need for scalable, effective, zero-shot text-to-video retrie…