activity
20242026
most citedHarnessing Multiple Large Language Models: A Survey on LLM Ensemble

5 citations · 5 across the 6 of their papers we have counts for

collaborators

13 papers

cs.RO2026

RiskWorld: Object-Centric Latent World Modeling for Autonomous Driving Risk Identification

Jingzheng Li, Yufei Ge, Qianren Mao +5

Autonomous driving risk identification aims to determine which observed object is likely to become safety-critical to the ego vehicle. Existing approaches typically predict scene-l…

cs.RO2026

End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation

Jingzheng Li, Yufei Ge, Zhijun Chen +8

Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical intera…

cs.IR2026

H3D: Benchmarking Unsupervised Text Hashing for Fine-Grained Document Deduplication

Qianren Mao, Jiaxun Lyu, Junnan Liu +4

Document hashing provides compact representations for efficient similarity search and document deduplication, but existing studies rarely compare hashing pipelines under a unified…

cs.CL2026

XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation

Qili Zhang, Qianren Mao, Yangyifei Luo +15

Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output…

cs.CL2026

Enhancing Multilingual Reasoning via Steerable Model Merging

Zhuoran Li, Rui Xu, Jian Yang +8

Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reaso…

cs.CL2026

Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process

Zhijun Chen, Zeyu Ji, Qianren Mao +12

We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for each query, harnessing the collective…