Qian Chen

The paper proposes CSMR, a cognitive scheduling framework that allows a language model to dynamically decide when to acquire task-relevant visual evidence, significantly improving multimodal reasoning accuracy.

This paper introduces CFMME, a comprehensive Chinese financial multimodal benchmark, and evaluates current Large Vision-Language Models (LVLMs), finding that while state-of-the-art models perform moderately, there is significant room for improvement in handling complex financial multimodal tasks.

This paper introduces PDAGENT-BENCH, a comprehensive benchmark for evaluating Large Language Models and vision-language models in the physical design stack of Very Large-Scale Integrated Circuits.

This paper proposes Efficient Chain-of-Modality Reasoning (ECoM Reasoning), a framework to improve reasoning ability in spoken language models (SLMs) for mathematical question answering tasks by compressing textual components.

Specula is an autonomous system that generates high-quality formal specifications for large, complex code using LLMs, improving understanding and finding bugs.