4 citations · 5 across the 4 of their papers we have counts for
4 papers
MARS: Margin-Adversarial Risk-controlled Stopping for Parallel LLM Test-time Scaling
Wenbo Chen, Puheng Li, Mengyang Liu +2
Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial c…
Cross-Modal Memory Compression for Efficient Multi-Agent Debate
Jing Wu, Yue Sun, Tianpei Xie +7
Multi-agent debate can improve reasoning quality and reduce hallucinations, but it incurs rapidly growing context as debate rounds and agent count increase. Retaining full textual…
TabDeco: A Comprehensive Contrastive Framework for Decoupled Representations in Tabular Data
Suiyao Chen, Jing Wu, Yunxiao Wang +6
Representation learning is a fundamental aspect of modern artificial intelligence, driving substantial improvements across diverse applications. While selfsupervised contrastive le…
SwitchTab: Switched Autoencoders Are Effective Tabular Learners
Jing Wu, Suiyao Chen, Qi Zhao +9
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or…