1 citations · 1 across the 9 of their papers we have counts for
10 papers
BrailleBench: Investigating Multi-Criteria Braille Comprehension in Large Language Models
Jinghan Zhang, Fengran Mo, Zhiyu Chen +3
Although Large language models (LLMs) mediate access to knowledge and computational assistance, their capabilities should benefit vulnerable groups in the same way. However, it is…
AUDITFLOW: Executable Symbolic Environments for Structured Financial Reporting Verification
Yan Wang, Xuguang Ai, Jaisal Patel +7
Structured financial reporting verification requires connecting reported facts to accounting constraints, reconstructing expected values, and executing numerical checks. We study t…
Herculean: An Agentic Benchmark for Financial Intelligence
Xueqing Peng, Zhuohan Xie, Yupeng Cao +60
As AI agents advance, the central question is no longer whether they can solve isolated financial tasks, but whether their capabilities generalize across complete professional work…
CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation
Rui Qi, Fengran Mo, Sijin Lu +3
A multilingual collection may contain useful knowledge in other languages to supplement and correct the facts in the original language for Retrieval-Augmented Generation (RAG). How…
StaRPO: Stability-Augmented Reinforcement Policy Optimization
Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +5
Reinforcement learning (RL) is effective in enhancing the accuracy of large language models in complex reasoning tasks. Existing RL policy optimization frameworks rely on final-ans…
Towards Dynamic Dense Retrieval with Routing Strategy
Zhan Su, Fengran Mo, Jinghan Zhang +4
The \textit{de facto} paradigm for applying dense retrieval (DR) to new tasks involves fine-tuning a pre-trained model for a specific task. However, this paradigm has two significa…
Entropy-based Exploration Conduction for Multi-step Reasoning
Jinghan Zhang, Xiting Wang, Fengran Mo +3
Multi-step processes via large language models (LLMs) have proven effective for solving complex reasoning tasks. However, the depth of exploration of the reasoning procedure can si…
LEKA:LLM-Enhanced Knowledge Augmentation
Xinhao Zhang, Jinghan Zhang, Fengran Mo +3
Humans excel in analogical learning and knowledge transfer and, more importantly, possess a unique understanding of identifying appropriate sources of knowledge. From a model's per…
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer
Jinghan Zhang, Fengran Mo, Tharindu Cyril Weerasooriya +4
Large language models have shown strong reasoning capabilities through chain-structured methods such as Chain-of-Thought. Recent studies optimize thought structures by generating p…
Retrieval-Augmented Feature Generation for Domain-Specific Classification
Xinhao Zhang, Jinghan Zhang, Fengran Mo +4
Feature generation can significantly enhance learning outcomes, particularly for tasks with limited data. An effective way to improve feature generation is to expand the current fe…