8 citations · 12 across the 12 of their papers we have counts for
13 papers
Long-Horizon Textual World Modeling through Structured Reasoning
Fangxin Wang, Xiang Gao, Yuguang Yao +3
World models must predict how an environment evolves under sequences of actions, enabling agents to compare possible futures and reason about counterfactual actions before acting.…
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…
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…
Can Molecular Foundation Models Know What They Don't Know? A Simple Remedy with Preference Optimization
Langzhou He, Junyou Zhu, Fangxin Wang +5
Molecular foundation models are rapidly advancing scientific discovery, but their unreliability on out-of-distribution (OOD) samples severely limits their application in high-stake…
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…
BANGS: Game-Theoretic Node Selection for Graph Self-Training
Fangxin Wang, Kay Liu, Sourav Medya +1
Graph self-training is a semi-supervised learning method that iteratively selects a set of unlabeled data to retrain the underlying graph neural network (GNN) model and improve its…
Uncertainty in Graph Neural Networks: A Survey
Fangxin Wang, Yuqing Liu, Kay Liu +3
Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inhere…
Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction
Chen Wang, Fangxin Wang, Ruocheng Guo +2
In Sequential Recommendation Systems (SRecsys), traditional training approaches that rely on Cross-Entropy (CE) loss often prioritize accuracy but fail to align well with user sati…
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models
Kay Liu, Hengrui Zhang, Ziqing Hu +2
A fundamental challenge confronting supervised graph outlier detection algorithms is the prevalent problem of class imbalance, where the scarcity of outlier instances compared to n…
Equal Opportunity of Coverage in Fair Regression
Fangxin Wang, Lu Cheng, Ruocheng Guo +2
We study fair machine learning (ML) under predictive uncertainty to enable reliable and trustworthy decision-making. The seminal work of ``equalized coverage'' proposed an uncertai…