7 citations · 11 across the 12 of their papers we have counts for
14 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…
Gradient Immunity: Null-Space Resistance to Malicious Fine-Tuning
Yuxuan Huang, Xingyu Zeng, Tianhang Zheng +1
Released aligned large language models remain vulnerable to malicious downstream finetuning. Existing defenses are largely designed for the fine-tuning-as-a-service (FTaaS) paradig…
Prisma-World: Camera-Controllable Multi-Agent Video World Model
Huiqiang Sun, Zhan Peng, Size Wu +9
Video world models have made rapid progress in generating controllable visual experiences, but most of them still simulate the world from a single observer. Extending such models t…
Performance-guided Reinforced Active Learning for Object Detection
Zhixuan Liang, Xingyu Zeng, Rui Zhao +1
Active learning (AL) strategies aim to train high-performance models with minimal labeling efforts, only selecting the most informative instances for annotation. Current approaches…
Generative Photographic Control for Scene-Consistent Video Cinematic Editing
Huiqiang Sun, Liao Shen, Zhan Peng +9
Cinematic storytelling is profoundly shaped by the artful manipulation of photographic elements such as depth of field and exposure. These effects are crucial in conveying mood and…
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…
GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning
Chengqi Duan, Rongyao Fang, Yuqing Wang +5
Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that specify multiple objects with precise…
GoT: Unleashing Reasoning Capability of Multimodal Large Language Model for Visual Generation and Editing
Rongyao Fang, Chengqi Duan, Kun Wang +9
Current image generation and editing methods primarily process textual prompts as direct inputs without reasoning about visual composition and explicit operations. We present Gener…
PUMA: Empowering Unified MLLM with Multi-granular Visual Generation
Rongyao Fang, Chengqi Duan, Kun Wang +7
Recent advancements in multimodal foundation models have yielded significant progress in vision-language understanding. Initial attempts have also explored the potential of multimo…
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…
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems
Yilun Kong, Jingqing Ruan, Yihong Chen +9
Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools that require a blend…
To be or not to be? an exploration of continuously controllable prompt engineering
Yuhan Sun, Mukai Li, Yixin Cao +4
As the use of large language models becomes more widespread, techniques like parameter-efficient fine-tuning and other methods for controlled generation are gaining traction for cu…
Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection
Zhixuan Liang, Xingyu Zeng, Rui Zhao +1
Active learning strategies aim to train high-performance models with minimal labeled data by selecting the most informative instances for labeling. However, existing methods for as…