1 citations · 1 across the 14 of their papers we have counts for
19 papers
HeteroVDiff: Pipelining Video Diffusion across Mobile NPU and GPU with Shared Weights
Zheyu Shen, Ziyao Wang, Yexiao He +5
Video generation is moving from the cloud to phones for privacy and offline use, and applications often ask for several clips at a time, so on-device video generation is a throughp…
DIVA: Dual-Space Intent-Aware Visual Attenuation for Vision-Language-Action Policies
Kaixi Feng, Guoheng Sun, Ziyao Wang +3
Vision-language-action (VLA) policies typically feed dense visual patch tokens into a language-action backbone, preserving scene context but offering no explicit mechanism to regul…
XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation
Tingting Du, Ziyao Wang, Guoheng Sun +1
World action models (WAMs) have advanced robot control by predicting how observations and actions evolve over time. Despite this progress, RGB and action based future prediction do…
TacGen: Touch Is a Necessary Dimension of Physical-World Representation -- Addressing Tactile Data Scarcity with Scalable Vision-to-Touch Alignment and Generation
Wanghao Ye, Aarosh Das, Sihan Chen +19
Touch resolves the physical-property ambiguity left by vision: exploratory contact recovers shape, texture, compliance, and material, and visuo-haptic object representations conver…
Drop-Then-Recovery: How Redundant Are Vision-Language-Action Models?
Guoheng Sun, Kaixi Feng, Shwai He +8
Vision-Language-Action (VLA) models enable instruction-driven robotic manipulation, but they inherit oversized language backbones from pretrained VLMs whose capacity far exceeds wh…
Martingale Doppelgänger-Eval: An Identification Framework for Auditing Candlestick Understanding in Vision-Language Models
Ziyao Wang
We introduce Martingale Doppelgänger-Eval, a public shadow-market benchmark for auditing whether vision-language models (VLMs) use candlestick evidence rather than extrapolate past…
Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines
Ziyao Wang, Bingying Wang, Hanrong Zhang +7
Despite remarkable progress in Vision--Language--Action (VLA) models, a central bottleneck remains underexamined: the data infrastructure that underlies embodied learning. In this…
Cognibit: From Digital Exhaustion to Real-World Connection Through Gamified Territory Control and LLM-Powered Twin Networking
Wanghao Ye, Sihan Chen, Yiting Wang +20
We present an LLM-powered social discovery platform that uses digital twins to autonomously evaluate interpersonal compatibility through behavioral simulation. The platform unifies…
UniCompress: Token Compression for Unified Vision-Language Understanding and Generation
Ziyao Wang, Chen Chen, Jingtao Li +4
Unified models aim to support both understanding and generation by encoding images into discrete tokens and processing them alongside text within a single autoregressive framework.…
FedMOA: Federated GRPO for Personalized Reasoning LLMs under Heterogeneous Rewards
Ziyao Wang, Daeun Jung, Yexiao He +4
Group Relative Policy Optimization (GRPO) has recently emerged as an effective approach for improving the reasoning capabilities of large language models through online multi-objec…
Towards Building Non-Fine-Tunable Foundation Models
Ziyao Wang, Nizhang Li, Pingzhi Li +3
Open-sourcing foundation models (FMs) enables broad reuse but also exposes model trainers to economic and safety risks from unrestricted downstream fine-tuning. We address this pro…
MindCraft: How Concept Trees Take Shape In Deep Models
Bowei Tian, Yexiao He, Wanghao Ye +3
Large-scale foundation models demonstrate strong performance across language, vision, and reasoning tasks. However, how they internally structure and stabilize concepts remains elu…
EvoVerilog: Large Langugage Model Assisted Evolution of Verilog Code
Ping Guo, Yiting Wang, Wanghao Ye +5
Large Language Models (LLMs) have demonstrated great potential in automating the generation of Verilog hardware description language code for hardware design. This automation is cr…
Predictive Auditing of Hidden Tokens in LLM APIs via Reasoning Length Estimation
Ziyao Wang, Guoheng Sun, Yexiao He +3
Commercial LLM services often conceal internal reasoning traces while still charging users for every generated token, including those from hidden intermediate steps, raising concer…
CogniPair: From LLM Chatbots to Conscious AI Agents -- GNWT-Based Multi-Agent Digital Twins for Social Pairing -- Dating & Hiring Applications
Wanghao Ye, Sihan Chen, Yiting Wang +18
Current large language model (LLM) agents lack authentic human psychological processes necessary for genuine digital twins and social AI applications. To address this limitation, w…
FedHQ: Hybrid Runtime Quantization for Federated Learning
Zihao Zheng, Ziyao Wang, Xiuping Cui +11
Federated Learning (FL) must balance training efficiency and model accuracy across heterogeneous clients. Post-training quantization (PTQ) avoids the extra computation of quantizat…
NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis
Yexiao He, Ziyao Wang, Yuning Zhang +4
Alzheimer's disease (AD) diagnosis is complex, requiring the integration of imaging and clinical data for accurate assessment. While deep learning has shown promise in brain MRI an…
Towards counterfactual fairness through auxiliary variables
Bowei Tian, Ziyao Wang, Shwai He +5
The challenge of balancing fairness and predictive accuracy in machine learning models, especially when sensitive attributes such as race, gender, or age are considered, has motiva…
SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence
Bowei Tian, Yexiao He, Ziyao Wang +3
Deep learning models exhibit fairness concerns when predictions are inadvertently influenced by sensitive attributes. However, existing attempts to make Path-Specific Counterfactua…