10 papers
When Do Corrective Features Help? An Agent for Corrective Feature Discovery on Black-Box Forecasters
Fangxin Wang, Ziyi Zhang, Diyi Zhuang +4
Frozen pretrained forecasters often fail in structured, recurring ways that are costly to repair through fine-tuning. We study corrective feature discovery: mining interpretable fe…
Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
Fangxin Wang, Peyman Baghershahi, Langzhou He +3
Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but existing methods are mostly designed for of…
GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
Peyman Baghershahi, Fangxin Wang, Debmalya Mandal +1
Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to graph neural networks (GNNs) re…
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation
Henry Peng Zou, Chunyu Miao, Wei-Chieh Huang +16
As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as addi…
Topology-Aware Conformal Prediction for Stream Networks
Jifan Zhang, Fangxin Wang, Zihe Song +3
Stream networks, a unique class of spatiotemporal graphs, exhibit complex directional flow constraints and evolving dependencies, making uncertainty quantification a critical yet c…
RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback
Chunyu Miao, Henry Peng Zou, Yangning Li +28
Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing…