6 papers
Minority Sentinel: When to Overturn Majority Voting in Multi-Agent LLM Debates
Chuan He, Zebin Chen, Zhengyi Yang +5
Multi-Agent Debate (MAD) with Majority Voting is a dominant paradigm for improving LLM reasoning, yet its effectiveness rests on the Condorcet Jury Theorem's assumption of independ…
A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning
Jiate Liu, Zebin Chen, Shaobo Qiao +10
Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing evidence through relational structure.…
HyperSU: Corpus-Driven Semantic-Unit Hypergraph for Retrieval-Augmented Generation
Jiate Liu, Liuyi Chen, Zhengyi Yang +5
Recent Hypergraph-based retrieval-augmented generation (HyperRAG) methods use hyperedges to connect multiple entities simultaneously, enabling more efficient multi-entity evidence…
EulerESG: Automating ESG Disclosure Analysis with LLMs
Yi Ding, Xushuo Tang, Zhengyi Yang +12
Environmental, Social, and Governance (ESG) reports have become central to how companies communicate climate risk, social impact, and governance practices, yet they are still publi…
Do They Understand Them? An Updated Evaluation on Nonbinary Pronoun Handling in Large Language Models
Xushuo Tang, Yi Ding, Zhengyi Yang +7
Large language models (LLMs) are increasingly deployed in sensitive contexts where fairness and inclusivity are critical. Pronoun usage, especially concerning gender-neutral and ne…
Graphy'our Data: Towards End-to-End Modeling, Exploring and Generating Report from Raw Data
Longbin Lai, Changwei Luo, Yunkai Lou +2
Large Language Models (LLMs) have recently demonstrated remarkable performance in tasks such as Retrieval-Augmented Generation (RAG) and autonomous AI agent workflows. Yet, when fa…