64 citations · 205 across the 60 of their papers we have counts for
69 papers
WAM-Sim0.1: World Anchored Memory Meets Online RL for Consistent and Realistic Driving Simulation
Jiahao Cui, Jiaye Li, Wenhao Zhang +7
Autoregressive driving world models systematically suffer from structural drift, identity drift, and quality decay over long rollout horizons due to transient conditioning signals…
DK-VLM: Geometry as Position for Spatial Reasoning in Vision-Language Models
Han Su, Meng Tian, Tianyu Huang +7
Vision-language models still struggle to understand 3D spatial relations across views and update spatial judgments as the observer moves. A common approach fuses 3D reconstruction…
Nodal nematic superconductivity in moiré graphene
Shuwen Sun, Grgur Palle, Kenji Watanabe +4
Unconventional superconductivity can emerge in various forms in strongly correlated systems. Historically, the most extensively studied examples include nodal and triplet pairing,…
EWAM: Emergent Depth-Wise Specialization in a Unified Embodied Model -- From Semantic Understanding through Visual Foresight to Action
Hao Wang, Jiajun Wen, Jingzhi Liu +21
Vision-language-action (VLA) policies emphasize semantic understanding, whereas world-action models (WAMs) learn predictive representations of environment dynamics. Systems that ex…
VA-Judger: Reward Modeling from Human Preference Feedback for Joint Video-Audio Generation
Yinming Huang, Shuyuan Tu, Xi Yan +6
Using reinforcement learning to post-train joint video-audio generation models requires a reward signal. Existing methods construct this reward by combining metrics for individual…
WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA
Zhihao Zhu, Hanlin Shang, Mingwang Xu +6
Vision-Language-Action (VLA) models have emerged as a prominent paradigm for end-to-end autonomous driving; however, their efficient deployment is severely constrained by high comp…
4D-WAM: 4D Consistent World Modeling for Autonomous Driving
Jiacheng Fu, Yibo Yuan, Meng Tian +8
Emerging World-Action Models (WAMs) have demonstrated promising performance in autonomous driving by jointly modeling future driving scene evolution and trajectory planning. Howeve…
SUV: Future Scene Understanding as Video Generation for End-to-End Driving
Yibo Yuan, Jiacheng Fu, Jiangtong Zhu +8
End-to-end driving requires a coherent understanding of future scenes, yet existing methods model these scenes using task-specific heads and output formats, with limited scalabilit…
Beyond the Eye: Efficient Multimodal Reasoning via Self-Regulated Implicit Visual Tools
Xiuwei Chen, Quanlin Chen, Wentao Hu +8
Recent multimodal large language models (MLLMs) have made remarkable progress on fine-grained perception tasks under the "Thinking with Images" (TwI) paradigm by iteratively perfor…
Latent Visual States for Efficient Multimodal Reasoning
Xiuwei Chen, Wentao Hu, Yongxin Wang +8
The integration of visual evidence has significantly enhanced the capabilities of large multimodal models. However, this integration predominantly relies on generating discrete out…
Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving
Zisheng Chen, Yuping Qiu, Jianhua Han +6
Recent Vision-Language-Action (VLA) models have advanced end-to-end autonomous driving by incorporating reasoning for better interpretability and planning quality. However, most ex…
MagicSeg: Open-World Segmentation Pretraining via Counterfactural Diffusion-Based Auto-Generation
Kaixin Cai, Pengzhen Ren, Jianhua Han +4
Open-world semantic segmentation presently relies significantly on extensive image-text pair datasets, which often suffer from a lack of fine-grained pixel annotations on sufficien…
Towards Unified Multimodal Interleaved Generation via Group Relative Policy Optimization
Ming Nie, Chunwei Wang, Jianhua Han +2
Unified vision-language models have made significant progress in multimodal understanding and generation, yet they largely fall short in producing multimodal interleaved outputs, w…
CGL: Advancing Continual GUI Learning via Reinforcement Fine-Tuning
Zhenquan Yao, Zitong Huang, Yihan Zeng +5
Graphical User Interface (GUI) Agents, benefiting from recent advances in multimodal large language models (MLLM), have achieved significant development. However, due to the freque…
AtomicVLA: Unlocking the Potential of Atomic Skill Learning in Robots
Likui Zhang, Tao Tang, Zhihao Zhan +9
Recent advances in Visual-Language-Action (VLA) models have shown promising potential for robotic manipulation tasks. However, real-world robotic tasks often involve long-horizon,…
RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training
Yunshuang Nie, Bingqian Lin, Minzhe Niu +7
Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training by leveraging their inherent perception and reasoning capabilities to so…
SlowFocus: Enhancing Fine-grained Temporal Understanding in Video LLM
Ming Nie, Dan Ding, Chunwei Wang +4
Large language models (LLMs) have demonstrated exceptional capabilities in text understanding, which has paved the way for their expansion into video LLMs (Vid-LLMs) to analyze vid…
Thinking with Geometry: Active Geometry Integration for Spatial Reasoning
Haoyuan Li, Qihang Cao, Tao Tang +6
Recent progress in spatial reasoning with Multimodal Large Language Models (MLLMs) increasingly leverages geometric priors from 3D encoders. However, most existing integration stra…
Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving
Jianhua Han, Meng Tian, Jiangtong Zhu +16
Autonomous driving heavily relies on accurate and robust spatial perception. Many failures arise from inaccuracies and instability, especially in long-tail scenarios and complex in…
Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
Kun Xiang, Terry Jingchen Zhang, Yinya Huang +13
The rapid advancement of embodied intelligence and world models has intensified efforts to integrate physical laws into AI systems, yet physical perception and symbolic physics rea…
SyncLoop: A Multimodal Dual-Loop Framework for Self-Improving Mathematical Reasoning
Xiuwei Chen, Wentao Hu, Hanhui Li +9
Recent advances in multimodal large language models (MLLMs) have shown impressive reasoning capabilities. However, further enhancing existing MLLMs necessitates high-quality vision…
Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs
Haoyuan Li, Yanpeng Zhou, Yufei Gao +7
Remarkable progress in 2D Vision-Language Models (VLMs) has spurred interest in extending them to 3D settings for tasks like 3D Question Answering, Dense Captioning, and Visual Gro…
EvolveNav: Empowering LLM-Based Vision-Language Navigation via Self-Improving Embodied Reasoning
Bingqian Lin, Yunshuang Nie, Khun Loun Zai +10
Recent studies have revealed the potential of training open-source Large Language Models (LLMs) to unleash LLMs' reasoning ability for enhancing vision-language navigation (VLN) pe…
SeePhys: Does Seeing Help Thinking? -- Benchmarking Vision-Based Physics Reasoning
Kun Xiang, Heng Li, Terry Jingchen Zhang +11
We present SeePhys, a large-scale multimodal benchmark for LLM reasoning grounded in physics questions ranging from middle school to PhD qualifying exams. The benchmark covers 7 fu…