14 citations · 14 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…