23 citations · 53 across the 69 of their papers we have counts for
78 papers
Where Do Embodied Decisions Come From? Rethinking Latent and Explicit Reasoning
Yuan Lin, Ziyue Zhou, JinLong Zhao +4
Chain-of-Thought (CoT) reasoning is increasingly incorporated into Vision-Language-Action (VLA) models, yet it can degrade the performance of stronger embodied agents. We investiga…
MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence
Haoran Wen, Wenfu Wang, Kunsong Shi +15
General-purpose robot control requires models to understand task intent, identify where to interact, capture how the scene evolves, and generate precise actions. Vision-language-ac…
LiAuto-MindViT: A Hybrid Vision Backbone with Adaptive Bidirectional Mamba
Lifu Mu, Shuai Chen, Wen Zheng +7
While Mamba-based models have shown strong potential for long sequence modeling, adapting them to vision is challenging due to the requirement of local neighborhood correlations an…
Information-Time Proximal Policy Optimization
Yongcheng Zeng, Xinyu Cui, Yan Song +9
RLVR has substantially improved the reasoning capabilities of LLMs. However, existing methods typically parameterize temporal progression in the Markov Decision Process by token-by…
ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence
Wei He, Hengtao Li, Zhongrui Yu +20
Current embodied systems largely rely on pretrained capabilities that remain fixed after deployment, limiting their ability to learn from physical interaction. We introduce MachEmb…
ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling
Xuancheng Zhang, Xuetao Liu, Qianying Tang +8
World Action Models bring the predictive capabilities of video models into robot action generation, providing a rich foundation for modeling future visual states. Tactile sensing c…
RGS: Reflection-aware Gaussian Splatting via Learning Geometry Continuity for Reflective Objects
Xiaobiao Du, Yida Wang, Cheng Bi +2
Gaussian Splatting has significantly improved the quality of novel view synthesis with explicit Gaussian representation. However, we observed that existing 3D Gaussian Splatting me…
What Makes an Efficient VLA? Navigating Action-Head Design, Scaling, and Latency
Luoyang Sun, Guoyang Xia, Fengfa Li +9
Vision-Language-Action (VLA) models combine a pretrained vision encoder, a language backbone, and an action head, but their relative contribution has not been established under con…
CLASVS: Continuous-Latent Autoregression for Melody-Preserving Lyric Editing in Singing Voice Synthesis
Yizhong Geng, Tian-Hao Zhang, Chunfeng Wang +7
Reference-conditioned melody-preserving lyric editing replaces words while retaining a performance's timing, singer identity, and naturalness. Continuous-latent autoregression avoi…
BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving
Bing Zhan, Shuyao Shang, Shuo Lu +6
Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, however, typically emphasize only one side of…
ChainSpace: A Chained-Reasoning Paradigm for Spatial Intelligence
Xiaohan Zhang, Feng Gu, Xudong Rao +4
Spatial intelligence requires foundation models to maintain coherent spatial state across interactions with the physical world. However, existing data-centric approaches typically…
RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning
Jinkun Hou, Zhuo Liu, Huimin Ren +3
Aligning Large Language Models (LLMs) for open-ended tasks is challenging because responses must satisfy multidimensional criteria without following a single correct generation tra…
Stable Curves, Unstable Items: Item-Level Scaling Heterogeneity in Video LLMs
Wenzhang Sun, Chunfeng Wang, Xiangchen Yin +3
Aggregate scaling curves suggest that Video LLMs improve smoothly or saturate as visual budgets grow. We show that this view can conceal large, opposing changes at the item level.…
Towards More Expressive Spoken LLMs: Fine-Grained Intent Benchmarking and Acoustic-Lexical Decoupled Policy Optimization
Xiang Lin, Tian-Hao Zhang, Chunfeng Wang +3
Spoken emotional dialogue requires a model to understand a user's spoken input and generate a response that is both semantically appropriate and emotionally expressive. This is cha…
VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment
Guoyang Xia, Fengfa Li, Hongjin Ji +4
Vision-language-action models (VLAs) have recently advanced robotic manipulation, yet the effects of different robot-data pre-training paradigms remain difficult to compare because…
Hear, Invoke, and Understand: A Skill-Calling Multimodal Agent for Large Audio Language Models
Yuwen Wang, Tian-Hao Zhang, Minghao Cai +7
Complex acoustic problems may require models to perform acoustic operations, interact with external tools and reason over the resulting textual or processed-audio observations rath…
RoadVGGT: Road-Structure-Aware Feed-Forward Road Surface Reconstruction
Han Jiao, Chen Liu, Jiakai Sun +6
Large-scale road surface reconstruction supports high-definition mapping, autonomous-driving perception, annotation, and simulation. Existing road-specialized optimization methods…
OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills
Qiyuan Liu, Tingfeng Hui, Kun Zhan +2
LLM-based agents leverage third-party skills to extend their capabilities in open-world scenarios. However, third-party skills can introduce extra security vulnerabilities, as seem…
LaCache: Exact Caching and Precision-Adaptive Inference for Diffusion Large Language Models
Xingru Chen, Zelang Liang, Yongjia Ma +4
Diffusion-based Large Language Models(DLLMs) enable parallel generation via Semi-Autoregressive (SAR) decoding in text generation. However, current methods suffer from severe opera…
FoMoVLA: Bridging Visual Foresight and Motion Guidance for Vision-Language-Action Models
Wei Li, Peijin Jia, Yuan Ma +9
Vision-Language-Action (VLA) models have achieved impressive results in visuomotor policy learning, yet remain fundamentally reactive, mapping current observations and language to…
Safe responses matter: Output-aware safety guardrail mitigate over-refusal in MLLMs
Jiayi Li, Kun Zhan
Existing safety mechanisms for multimodal large language models (MLLMs) face a fundamental trade-off between safety and utility. Model fine-tuning achieves robust safety but compro…
Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation
Jingyu Li, Zhe Liu, Dongnan Hu +10
World action models~(WAMs) have shown great promise for autonomous driving and urban navigation. Built upon Vision-Language-Action models or video generation models, existing appro…
LiAuto-GeoX: Efficient Grounded Driving Transformer
Jiawei Lian, Haoyi Sun, Yang Wu +8
Dense 3D reconstruction has demonstrated immense potential for spatial understanding, yet its viability as a real-time, onboard representation for autonomous driving remains an ope…
HMPO: Hybrid Median-length Policy Optimization for Chain-of-Thought Compression
Minghui Zheng, Hongxu Chen, Huimin Ren +8
Large language models achieve remarkable performance via extended chain-of-thought (CoT) reasoning, yet this lengthy process incurs substantial inference overhead. Existing CoT com…