1 citations · 1 across the 7 of their papers we have counts for
8 papers
EVOKE: Eliciting World Knowledge in Agents for Transferable Decision-Making
Yuhan Guo, Jinming Liu, Liang Xu +8
Large language models (LLMs) are increasingly deployed as agents for multi-step decision-making, yet transfer poorly to unseen environments. World-model methods address this by tra…
.tmu: A Low-Entropy Tree-Structured Representation for LLM-Assisted Scientific Writing
Tianyou Liu, Ziqiang Li, Xurui Liu +2
As large language models (LLMs) increasingly assist scientific writing, the limitations and token costs of generating TeX become increasingly visible. This paper analyzes TeX's arc…
Vision-Centric Activation and Coordination for Multimodal Large Language Models
Yunnan Wang, Fan Lu, Kecheng Zheng +4
Multimodal large language models (MLLMs) integrate image features from visual encoders with LLMs, demonstrating advanced comprehension capabilities. However, mainstream MLLMs are s…
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
Qiuyu Chen, Xin Jin, Yue Song +45
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop a…
Follow-Your-MultiPose: Tuning-Free Multi-Character Text-to-Video Generation via Pose Guidance
Beiyuan Zhang, Yue Ma, Chunlei Fu +3
Text-editable and pose-controllable character video generation is a challenging but prevailing topic with practical applications. However, existing approaches mainly focus on singl…
DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation
Jia Li, Yangchen Yu, Yin Chen +6
Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affective computing and human-compute…
Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation
Yunnan Wang, Ziqiang Li, Zequn Zhang +5
There has been exciting progress in generating images from natural language or layout conditions. However, these methods struggle to faithfully reproduce complex scenes due to the…
Closed-Loop Unsupervised Representation Disentanglement with -VAE Distillation and Diffusion Probabilistic Feedback
Xin Jin, Bohan Li, BAAO Xie +5
Representation disentanglement may help AI fundamentally understand the real world and thus benefit both discrimination and generation tasks. It currently has at least three unreso…