14 citations · 14 across the 10 of their papers we have counts for
11 papers
StructRL: Online Structured Reinforcement Learning for Long-Horizon Vision-Language-Action Tasks
Ziyi Yin, Sangmin Woo, Kang Zhou +4
Vision-language-action (VLA) models perform well on shorter-horizon manipulation tasks but still struggle with long-horizon tasks that require multiple dependent manipulations from…
TRACE: Transition-Aware Residual Control for Multi-Objective Materials Discovery
Kang Zhou, Yujia Tong, Yong Tao +1
Multi-objective materials discovery with LLM agents is often limited not only by how many candidates can be proposed, but by how effectively each costly property evaluation informs…
FinEvo-Bench: A Longitudinal Benchmark for Self-Evolving Agents in Professional Financial Workflows
Bo Deng, Kang Zhou, Lifan Guo +6
Agents used over time encounter recurring professional work: each case requires different evidence and judgment, while the underlying workflow can be reused. Benchmarks built from…
FinPerMA: A Theory-Informed, Event-Grounded Personalized-Memory Benchmark for LLM Agents
Ben Wang, Kang Zhou, Lifan Guo +2
Large language model (LLM) agents are increasingly used as personalized assistants in high-stakes domains such as financial advising, yet it remains unclear whether they can mainta…
FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables
Ben Wang, Kang Zhou, Lifan Guo +2
Evaluating financial AI agents requires criteria aligned with real professional work. Existing rubric methods typically derive criteria from task prompts or model outputs, overlook…
An Empirical Study of Automating Agent Evaluation
Kang Zhou, Sangmin Woo, Haibo Ding +15
Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises:…
Diffusion Language Model Inference with Monte Carlo Tree Search
Zheng Huang, Kiran Ramnath, Yueyan Chen +8
Diffusion language models (DLMs) have recently emerged as a compelling alternative to autoregressive generation, offering parallel generation and improved global coherence. During…
Learning to Ideate for Machine Learning Engineering Agents
Yunxiang Zhang, Kang Zhou, Zhichao Xu +5
Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. To address this, we introduce MLE-Ideator, a dua…
Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation
Zhichao Xu, Zongyu Wu, Yun Zhou +9
Inspired by the success of reinforcement learning (RL) in Large Language Model (LLM) training for domains like math and code, recent work has begun training LLMs to dynamically pla…
Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models
Sangmin Woo, Kang Zhou, Yun Zhou +4
Large Vision Language Models (LVLMs) often suffer from object hallucination, which undermines their reliability. Surprisingly, we find that simple object-based visual prompting --…
A Systematic Survey of Automatic Prompt Optimization Techniques
Kiran Ramnath, Kang Zhou, Sheng Guan +18
Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. Ho…