6 papers
The Illusion of Visual Tool-Use: A Causal Audit of Thinking with Images
Zhiheng Wang, Bo Peng, Lai Wei +1
The "thinking-with-images" paradigm equips multimodal LLMs with active visual operations such as crop-and-zoom. However, models using these operations often achieve only marginal o…
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…
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…
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…
CauSight: Learning to Supersense for Visual Causal Discovery
Yize Zhang, Meiqi Chen, Sirui Chen +4
Causal thinking enables humans to understand not just what is seen, but why it happens. To replicate this capability in modern AI systems, we introduce the task of visual causal di…
IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data
Bo Peng, Zhiheng Wang, Heyang Gong +1
In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial. However, the s…