3 citations · 8 across the 22 of their papers we have counts for
24 papers
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…
X-Talk: On the Underestimated Potential of Modular Speech-to-Speech Dialogue System
Zhanxun Liu, Yifan Duan, Mengmeng Wang +15
We present X-Talk, an open-source framework that champions a decoupled, modular design for LLM-driven speech-to-speech (S2S) systems. While the dominant trend favors end-to-end (E2…
LINA: Learning INterventions Adaptively for Physical Alignment and Generalization in Diffusion Models
Shu Yu, Chaochao Lu
Diffusion models (DMs) have achieved remarkable success in image and video generation. However, they still struggle with (1) physical alignment and (2) out-of-distribution (OOD) in…
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…
DEPO: Dual-Efficiency Preference Optimization for LLM Agents
Sirui Chen, Mengshi Zhao, Lei Xu +5
Recent advances in large language models (LLMs) have greatly improved their reasoning and decision-making abilities when deployed as agents. Richer reasoning, however, often comes…
Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
Lei Tong, Zhihua Liu, Chaochao Lu +5
We present Causal-Adapter, a modular framework that adapts frozen text-to-image diffusion backbones for counterfactual image generation. Our method supports causal interventions on…
Synthesis by Design: Controlled Data Generation via Structural Guidance
Lei Xu, Sirui Chen, Yuxuan Huang +1
Mathematical reasoning remains challenging for LLMs due to complex logic and the need for precise computation. Existing methods enhance LLM reasoning by synthesizing datasets throu…
Exploring Consciousness in LLMs: A Systematic Survey of Theories, Implementations, and Frontier Risks
Sirui Chen, Shuqin Ma, Shu Yu +3
Consciousness stands as one of the most profound and distinguishing features of the human mind, fundamentally shaping our understanding of existence and agency. As large language m…
ARise: Towards Knowledge-Augmented Reasoning via Risk-Adaptive Search
Yize Zhang, Tianshu Wang, Sirui Chen +6
Large language models (LLMs) have demonstrated impressive capabilities and are receiving increasing attention to enhance their reasoning through scaling test--time compute. However…
Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?
Yujin Han, Andi Han, Wei Huang +2
Despite the remarkable success of diffusion models (DMs) in data generation, they exhibit specific failure cases with unsatisfactory outputs. We focus on one such limitation: the a…
Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension Ability
Yujin Han, Lei Xu, Sirui Chen +2
Large language models (LLMs) have shown remarkable capability in natural language tasks, yet debate persists on whether they truly comprehend deep structure (i.e., core semantics)…
ADAM: An Embodied Causal Agent in Open-World Environments
Shu Yu, Chaochao Lu
In open-world environments like Minecraft, existing agents face challenges in continuously learning structured knowledge, particularly causality. These challenges stem from the opa…
From Imitation to Introspection: Probing Self-Consciousness in Language Models
Sirui Chen, Shu Yu, Shengjie Zhao +1
Self-consciousness, the introspection of one's existence and thoughts, represents a high-level cognitive process. As language models advance at an unprecedented pace, a critical qu…
Interpreting Low-level Vision Models with Causal Effect Maps
Jinfan Hu, Jinjin Gu, Shiyao Yu +5
Deep neural networks have significantly improved the performance of low-level vision tasks but also increased the difficulty of interpretability. A deep understanding of deep model…
CELLO: Causal Evaluation of Large Vision-Language Models
Meiqi Chen, Bo Peng, Yan Zhang +1
Causal reasoning is fundamental to human intelligence and crucial for effective decision-making in real-world environments. Despite recent advancements in large vision-language mod…
CLEAR: Can Language Models Really Understand Causal Graphs?
Sirui Chen, Mengying Xu, Kun Wang +4
Causal reasoning is a cornerstone of how humans interpret the world. To model and reason about causality, causal graphs offer a concise yet effective solution. Given the impressive…
Causal Evaluation of Language Models
Sirui Chen, Bo Peng, Meiqi Chen +7
Causal reasoning is viewed as crucial for achieving human-level machine intelligence. Recent advances in language models have expanded the horizons of artificial intelligence acros…
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Chaochao Lu, Chen Qian, Guodong Zheng +33
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents. However, there is still a wide ga…
ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
Bo Peng, Xinyuan Chen, Yaohui Wang +2
Recent works have successfully extended large-scale text-to-image models to the video domain, producing promising results but at a high computational cost and requiring a large amo…