activity
20242026
collaborators

8 papers

cs.CV2026

FinMTM: A Multi-Turn Multimodal Benchmark for Financial Reasoning and Agent Evaluation

Chenxi Zhang, Ziliang Gan, Liyun Zhu +3

The financial domain poses substantial challenges for vision-language models (VLMs) due to specialized chart formats and knowledge-intensive reasoning requirements. However, existi…

cs.CV2026

Compress to Focus: Efficient Coordinate Compression for Policy Optimization in Multi-Turn GUI Agents

Yurun Song, Jiong Yin, Rongjunchen Zhang +1

Multi-turn GUI agents enable complex task completion through sequential decision-making, but suffer from severe context inflation as interaction history accumulates. Existing strat…

q-fin.GN2026

UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos

Zhi Yang, Lingfeng Zeng, Fangqi Lou +16

Multimodal large language models are playing an increasingly significant role in empowering the financial domain, however, the challenges they face, such as multimodal and high-den…

cs.CL2025

RETuning: Upgrading Inference-Time Scaling for Stock Movement Prediction with Large Language Models

Xueyuan Lin, Cehao Yang, Ye Ma +7

Recently, large language models (LLMs) have demonstrated outstanding reasoning capabilities on mathematical and coding tasks. However, their application to financial tasks-especial…

cs.CL2025

CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning

Wenqiao Zhu, Ji Liu, Rongjuncheng Zhang +2

Reasoning capability plays a significantly critical role in the the broad applications of Large Language Models (LLMs). To enhance the reasoning performance of LLMs, diverse Reinfo…

cs.AI2025

BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs

Guilong Lu, Xuntao Guo, Rongjunchen Zhang +2

Large language models excel in general tasks, yet assessing their reliability in logic-heavy, precision-critical domains like finance, law, and healthcare remains challenging. To a…