2 citations · 3 across the 18 of their papers we have counts for
25 papers
RealtimeWAM: How Fast Can I Run My World Action Model?
Huanan Liu, Ye Li, Kangye Ji +8
World Action Models (WAMs) combine visual dynamics modeling with action generation, but their high inference latency limits responsive robot control. Recent efforts accelerate infe…
ChunkTrust: Adapting Execution Horizons for Robot Policies with Action-Expert Evidence
Fanding Huang, Jingyan Jiang, Shifeng Bao +13
Robot foundation policies predict action chunks, but how many actions to execute before replanning depends on the current task phase. We introduce ChunkTrust, which treats the exec…
CurvSpec: Adaptive Multi-Curvature Learning for Partial Relevant Video Retrieval
Zhen Liu, Letian Li, Jinpeng Wang +4
Partially Relevant Video Retrieval (PRVR) seeks to retrieve untrim-med videos containing a moment that matches a text query, without temporal annotations. The relevant moment may l…
FutureDuet: Decoupling Observation Access from Future Supervision in World Action Models
Jie Wu, Yuzhi Huang, Junqi Liu +5
World Action Models (WAMs) augment robot action generation with future visual supervision. Existing WAMs commonly fuse main and wrist observations into one visual stream and train…
GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions
Chenghao Gu, Hanyang Yu, Jingbo Zhang +7
Generalist robot policies exhibit strong capabilities, but their robustness in complex and unseen environments remains limited. Scaling robot learning and evaluation in diverse rea…
Respect Your Zero-Shot Uncertainty: Conservative Calibration for Test-Time-Adapted Vision-Language Models
Jingyan Jiang, Yaru Sun, Xiao Chen +5
Test-time adaptation (TTA) can improve the recognition accuracy of vision-language models under distribution shift, but often degrades calibration, making predictive confidence unr…
TAU-Bench: From Anomaly Instance Tracking to Fine-Grained Video Anomaly Understanding
Kepeng Yang, Dongxuan Liu, Rongxin Gao +8
Humans understand anomalous events through a coherent perceptual process in which they identify the focal instance, follow its behavior as the event unfolds, and interpret why it v…
ChainVLA: Chaining Vision-Language-Action Queries through a Unified Execution State for Long-Horizon Manipulation
Yuzhi Huang, Weijue Bu, Ziyi Xiong +4
Humans perform long-horizon manipulation by retaining knowledge of what earlier actions have established while continuously adapting the motion underway. By contrast, action-chunke…
DynTrace: Tracking Dynamic Object Evidence for 4D Spatio-Temporal Reasoning in MLLMs
Rongxin Gao, Yuzhi Huang, Dongxuan Liu +8
4D spatio-temporal reasoning, jointly modeling 3D spatial structure and temporal evolution, is essential for understanding dynamic worlds and enabling embodied interaction. While c…
What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective
Jiazhen Huang, Xiao Chen, Zhiming Liu +3
Vision-Language Models (VLMs) such as CLIP have become a standard backbone for open-vocabulary recognition, yet their zero-shot predictions remain vulnerable to distribution shifts…
MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching
Shuzhao Xie, Junchen Ge, Weixiang Zhang +10
3D Gaussian Splatting (3DGS) achieves high-quality novel view synthesis with real-time rendering, but its storage cost remains prohibitive for practical deployment. Existing post-t…
Thinking in Dynamics: How Multimodal Large Language Models Perceive, Track, and Reason Dynamics in Physical 4D World
Yuzhi Huang, Kairun Wen, Rongxin Gao +14
Humans inhabit a physical 4D world where geometric structure and semantic content evolve over time, constituting a dynamic 4D reality (spatial with temporal dimension). While curre…
RoboStream: Weaving Spatio-Temporal Reasoning with Memory in Vision-Language Models for Robotics
Yuzhi Huang, Jie Wu, Weijue Bu +9
Enabling reliable long-horizon robotic manipulation is a crucial step toward open-world embodied intelligence. However, VLM-based planners treat each step as an isolated observatio…
Neural Collapse in Test-Time Adaptation
Xiao Chen, Zhongjing Du, Jiazhen Huang +4
Test-Time Adaptation (TTA) enhances model robustness to out-of-distribution (OOD) data by updating the model online during inference, yet existing methods lack theoretical insights…
MoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm
Xiao Fan, Jingyan Jiang, Zhaoru Chen +6
Test-Time adaptation (TTA) has proven effective in mitigating performance drops under single-domain distribution shifts by updating model parameters during inference. However, real…
Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning
Fanding Huang, Guanbo Huang, Xiao Fan +7
Reinforcement Learning with Verifiable Rewards (RLVR) for LLM reasoning is often framed as balancing exploration and exploitation in action space, typically operationalized with to…
Accelerating Parallel Diffusion Model Serving with Residual Compression
Jiajun Luo, Yicheng Xiao, Jianru Xu +5
Diffusion models produce realistic images and videos but require substantial computational resources, necessitating multi-accelerator parallelism for real-time deployment. However,…
Taming Latency and Bandwidth: A Theoretical Framework and Adaptive Algorithm for Communication-Constrained Training
Rongwei Lu, Jingyan Jiang, Chunyang Li +2
Regional energy caps limit the growth of any single data center used for large-scale model training. This single-center training paradigm works when model size remains manageable,…
Feature-Based Instance Neighbor Discovery: Advanced Stable Test-Time Adaptation in Dynamic World
Qinting Jiang, Chuyang Ye, Dongyan Wei +4
Despite progress, deep neural networks still suffer performance declines under distribution shifts between training and test domains, leading to a substantial decrease in Quality o…
Test-Time Distillation for Continual Model Adaptation
Xiao Chen, Jiazhen Huang, Zhiming Liu +4
Deep neural networks often suffer performance degradation upon deployment due to distribution shifts. Continual Test-Time Adaptation (CTTA) aims to address this issue in an unsuper…
COSMIC: Clique-Oriented Semantic Multi-space Integration for Robust CLIP Test-Time Adaptation
Fanding Huang, Jingyan Jiang, Qinting Jiang +3
Recent vision-language models (VLMs) face significant challenges in test-time adaptation to novel domains. While cache-based methods show promise by leveraging historical informati…
Beyond A Single AI Cluster: A Survey of Decentralized LLM Training
Haotian Dong, Jingyan Jiang, Rongwei Lu +5
The emergence of large language models (LLMs) has revolutionized AI development, yet the resource demands beyond a single cluster or even datacenter, limiting accessibility to well…
Discover Your Neighbors: Advanced Stable Test-Time Adaptation in Dynamic World
Qinting Jiang, Chuyang Ye, Dongyan Wei +3
Despite progress, deep neural networks still suffer performance declines under distribution shifts between training and test domains, leading to a substantial decrease in Quality o…
Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
Lei Chen, Yuan Meng, Chen Tang +5
Recent advancements in diffusion models, particularly the architectural transformation from UNet-based models to Diffusion Transformers (DiTs), significantly improve the quality an…