31 citations · 88 across the 23 of their papers we have counts for
24 papers
CHORUS: Complementary Experts for High-Coverage Testbench Stimulus Generation
Hejia Zhang, Sheng Lu, Zhongming Yu +3
Large language models (LLMs) have advanced code generation, where executable feedback provides a more reliable learning signal than textual imitation alone. Hardware verification i…
Language Equality has a Price: A Systematic Investigation of Multi-turn LLM Performance for EU-24+
Sherzod Hakimov, Karl Osswald, Jelle Psurek +3
We evaluate large language models (LLMs) as language agents playing goal-directed dialogue games in self-play across 30 languages: the 24 official EU languages plus six others. Unl…
FinHardBench: Can LLMs Generate Latency-Aware Hardware for Financial Computing?
Weimin Fu, Hejia Zhang, Minghao Shao +6
Can large language models generate not just correct, but fast hardware? This paper investigates the question in financial FPGA design, where 5-10 nanoseconds of latency determines…
CodeNib: A Multi-View Data System for Serving Repository Context to Coding Agents
Zhongming Yu, Hengjia Yu, Boqin Yuan +12
An incremental code index can complete every update without error and still return the wrong graph half the time. Coding agents depend on such reused views for search and navigatio…
Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model
Minghao Wu, Yuting Yan, Zhenyang Cai +9
Sepsis management in the ICU requires sequential treatment decisions under rapidly evolving patient physiology. Although large language models (LLMs) encode broad clinical knowledg…
ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation
Zhongkai Yu, Yichen Lin, Chenyang Zhou +12
Existing API-based agentic systems for RTL code generation are fundamentally misaligned with industrial practice: they assume a golden testbench is available at generation time, re…
LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation
Hejia Zhang, Zhongming Yu, Chia-Tung Ho +3
Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making online reinforcement learning (…
ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation
Enyu Zhao, Vedant Raval, Hejia Zhang +5
Vision-Language Models (VLMs) have revolutionized artificial intelligence and robotics due to their commonsense reasoning capabilities. In robotic manipulation, VLMs are used prima…
OrcaLoca: An LLM Agent Framework for Software Issue Localization
Zhongming Yu, Hejia Zhang, Yujie Zhao +4
Recent developments in Large Language Model (LLM) agents are revolutionizing Autonomous Software Engineering (ASE), enabling automated coding, problem fixes, and feature improvemen…
GPT-Fabric: Smoothing and Folding Fabric by Leveraging Pre-Trained Foundation Models
Vedant Raval, Enyu Zhao, Hejia Zhang +2
Fabric manipulation has applications in folding blankets, handling patient clothing, and protecting items with covers. It is challenging for robots to perform fabric manipulation s…
Multi-Robot Geometric Task-and-Motion Planning for Collaborative Manipulation Tasks
Hejia Zhang, Shao-Hung Chan, Jie Zhong +6
We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple rob…
Effective Long-Context Scaling of Foundation Models
Wenhan Xiong, Jingyu Liu, Igor Molybog +18
We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with l…
Surrogate Assisted Generation of Human-Robot Interaction Scenarios
Varun Bhatt, Heramb Nemlekar, Matthew C. Fontaine +4
As human-robot interaction (HRI) systems advance, so does the difficulty of evaluating and understanding the strengths and limitations of these systems in different environments an…
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
Shivin Dass, Karl Pertsch, Hejia Zhang +3
Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting l…
A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning
Hejia Zhang, Shao-Hung Chan, Jie Zhong +3
We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple rob…
Video Game Level Repair via Mixed Integer Linear Programming
Hejia Zhang, Matthew C. Fontaine, Amy K. Hoover +3
Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples. Howe…
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
Ming Bo Cai, Michael Shvartsman, Anqi Wu +2
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent ye…
An Efficient Hardware Accelerator for Structured Sparse Convolutional Neural Networks on FPGAs
Chaoyang Zhu, Kejie Huang, Shuyuan Yang +3
Deep Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in a wide range of applications. However, deeper CNN models, which are usually computation cons…
Zero-Shot Imitating Collaborative Manipulation Plans from YouTube Cooking Videos
Hejia Zhang, Jie Zhong, Stefanos Nikolaidis
People often watch videos on the web to learn how to cook new recipes, assemble furniture or repair a computer. We wish to enable robots with the very same capability. This is chal…
Interactive Differentiable Simulation
Eric Heiden, David Millard, Hejia Zhang +1
Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While learning-based models of the environment dynamics have con…
Simulator Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing
Zhanpeng He, Ryan Julian, Eric Heiden +5
Simulation-to-real transfer is an important strategy for making reinforcement learning practical with real robots. Successful sim-to-real transfer systems have difficulty producing…
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robot Skills
Hejia Zhang, Eric Heiden, Stefanos Nikolaidis +2
Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives…
Scaling simulation-to-real transfer by learning composable robot skills
Ryan Julian, Eric Heiden, Zhanpeng He +5
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simu…
Segmenting Brain Tumors with Symmetry
Hejia Zhang, Xia Zhu, Theodore L. Willke
We explore encoding brain symmetry into a neural network for a brain tumor segmentation task. A healthy human brain is symmetric at a high level of abstraction, and the high-level…