9 papers
Can LLM Agents Discover? Evaluating Creativity on ML Engineering Tasks
Shitanshu Bhushan, Yunxiang Zhang, Lu Wang
Recent AI systems promise autonomous scientific discovery, claiming to discover algorithms and produce research papers, yet understanding whether they exhibit creativity, the capac…
MET: Theory-Grounded and Culture-Aware Multilingual Moral Reasoning
Ayoung Lee, Ryan Kwon, Yunxiang Zhang +3
Language models are increasingly used for moral decision-making across diverse linguistic and cultural contexts, yet existing work overlooks multilinguality on three aspects: 1) mu…
AdaMEM: Test-Time Adaptive Memory for Language Agents
Yunxiang Zhang, Yiheng Li, Ali Payani +1
A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions. While recent work demonstrates the promise of agentic memory mechanis…
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…
Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation
Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim +6
Large language models (LLMs) are increasingly used as judges to evaluate agent performance, particularly in non-verifiable settings where judgments rely on agent trajectories inclu…
Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation
Lechen Zhang, Yunxiang Zhang, Wei Hu +1
Large reasoning models such as DeepSeek-R1 and their distilled variants achieve strong performance on complex reasoning tasks. Yet, distilling these models often demands large-scal…