14 citations · 15 across the 7 of their papers we have counts for
9 papers
ForkCast: Predictive Recovery for Vision-Language-Action Policies via Counterfactual Futures
Chenru Wen, Huajian Zeng, Haokai Ding +5
Vision-language-action (VLA) policies perform manipulation tasks using camera images and language instructions, but object slip or visual occlusion can disrupt execution. Recovery…
Optimize Once, Execute Fast: Latency-Aware Multi-Agent Workflow Learning for Recurrent Queries
Enpei Zhang, Feiyu Qu, Zheng Huang +3
Large Language Model-based Multi-Agent Systems (LLM-based MAS) have shown remarkable success in solving complex tasks by coordinating specialized agents through multi-step workflow…
Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks
Tianyu Pang, Vignesh Kothapalli, Shenyang Deng +3
We study optimal learning-rate selection in two-layer and three-layer linear neural networks trained to learn linear target functions. In particular, we derive the exact closed-for…
RoSHAP: A Distributional Framework and Robust Metric for Stable Feature Attribution
Lanxin Xiang, Liang Shi, Youhui Ye +3
Feature attribution analysis is critical for interpreting machine learning models and supporting reliable data-driven decisions. However, feature attribution measures often exhibit…
HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs
Xinyue Zeng, Junhong Lin, Yujun Yan +4
The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typi…
Seeing Through the Brain: New Insights from Decoding Visual Stimuli with fMRI
Zheng Huang, Enpei Zhang, Weikang Qiu +7
Understanding how the brain encodes visual information is a central challenge in neuroscience and machine learning. A promising approach is to reconstruct visual stimuli, essential…
Are Vision LLMs Road-Ready? A Comprehensive Benchmark for Safety-Critical Driving Video Understanding
Tong Zeng, Longfeng Wu, Liang Shi +2
Vision Large Language Models (VLLMs) have demonstrated impressive capabilities in general visual tasks such as image captioning and visual question answering. However, their effect…
Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning
Zheng Huang, Qihui Yang, Dawei Zhou +1
Although most graph neural networks (GNNs) can operate on graphs of any size, their classification performance often declines on graphs larger than those encountered during trainin…
Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation
Baoyu Jing, Dawei Zhou, Kan Ren +1
Spatiotemporal time series are usually collected via monitoring sensors placed at different locations, which usually contain missing values due to various failures, such as mechani…