1 citations · 1 across the 11 of their papers we have counts for
11 papers
Code as Representation: A Compilable Parsing Paradigm for Academic Documents
Rihui Jin, Jun Wang, chengyuan zhu +11
Academic papers are a primary carrier of scientific knowledge, yet most of this knowledge remains locked in PDFs that are optimized for human reading rather than machine use. For M…
EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference Optimization
Xinbang Dai, Zheyu Xin, Huikang Hu +7
Large Reasoning Models (LRMs) often suffer from overthinking due to redundant verification steps. Existing approaches for mitigating overthinking, such as fast-slow thinking switch…
ELAIPBench: A Benchmark for Expert-Level Artificial Intelligence Paper Understanding
Xinbang Dai, Huikang Hu, Yongrui Chen +6
While large language models (LLMs) excel at many domain-specific tasks, their ability to deeply comprehend and reason about full-length academic papers remains underexplored. Exist…
Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
Yongrui Chen, Junhao He, Linbo Fu +10
Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and knowledge graphs in a unified way…
OneEval: Benchmarking LLM Knowledge-intensive Reasoning over Diverse Knowledge Bases
Yongrui Chen, Zhiqiang Liu, Jing Yu +21
Large Language Models (LLMs) have demonstrated substantial progress on reasoning tasks involving unstructured text, yet their capabilities significantly deteriorate when reasoning…
Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models
Rihui Jin, Zheyu Xin, Xing Xie +6
Table reasoning (TR) requires structured reasoning over semi-structured tabular data and remains challenging, particularly for small language models (SLMs, e.g., LLaMA-8B) due to t…
Magic Mushroom: A Customizable Benchmark for Fine-grained Analysis of Retrieval Noise Erosion in RAG Systems
Yuxin Zhang, Yan Wang, Yongrui Chen +4
Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) by incorporating external retrieved information, mitigating issues such as hallucination and outda…
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in Retrieval-Augmented Generation
Xinbang Dai, Huikang Hu, Yuncheng Hua +5
Retrieval-augmented generation (RAG) is a promising paradigm, yet its trustworthiness remains a critical concern. A major vulnerability arises prior to generation: models often fai…
Pandora: A Code-Driven Large Language Model Agent for Unified Reasoning Across Diverse Structured Knowledge
Yongrui Chen, Junhao He, Linbo Fu +10
Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions (NLQs) by using structured sources such as tables, databases, and knowledge graphs in a unif…
Question Answering Over Spatio-Temporal Knowledge Graph
Xinbang Dai, Huiying Li, Nan Hu +4
Spatio-temporal knowledge graphs (STKGs) enhance traditional KGs by integrating temporal and spatial annotations, enabling precise reasoning over questions with spatio-temporal dep…
Large Language Models Can Better Understand Knowledge Graphs Than We Thought
Xinbang Dai, Yuncheng Hua, Tongtong Wu +3
When we integrate factual knowledge from knowledge graphs (KGs) into large language models (LLMs) to enhance their performance, the cost of injection through training increases wit…