3 citations · 8 across the 22 of their papers we have counts for
6 papers · 1 filter
XTurnix: Large-Scale Self-Supervised Turn Control through Two-State Binary Decisions
Zhanxun Liu, Yifan Duan, Hengtao Wu +7
General turn-taking behavior in real-time dialogue systems requires deciding whether to keep listening or start responding while listening, and whether to continue or stop while sp…
CAST: Causal Advantage-Structured Training with Spatially Grounded Compositional Rewards for Diffusion Models
Shu Yu, Chaochao Lu
Online reinforcement learning has been extended to flow matching for diffusion model (DM) image generation. However, this paradigm faces three limitations: (1) Window selection. Ex…
CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery
Bo Peng, Kaiwen Wu, Sirui Chen +3
Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equival…
CauScale: Neural Causal Discovery at Scale
Bo Peng, Sirui Chen, Jiaguo Tian +2
Causal discovery is essential for advancing data-driven fields such as scientific AI and data analysis, yet existing approaches face significant time- and space-efficiency bottlene…
Can Post-Training Transform LLMs into Causal Reasoners?
Junqi Chen, Sirui Chen, Chaochao Lu
Causal inference is essential for decision-making but remains challenging for non-experts. While large language models (LLMs) show promise in this domain, their precise causal esti…
CauScientist: Teaching LLMs to Respect Data for Causal Discovery
Bo Peng, Sirui Chen, Lei Xu +1
Causal discovery is fundamental to scientific understanding and reliable decision-making. Existing approaches face critical limitations: purely data-driven methods suffer from stat…