4 citations · 18 across the 22 of their papers we have counts for
23 papers
EvoHarness-RL: Learning Runtime Harness Coordination for Self-Evolving Agents
Xuying Ning, Dongqi Fu, Tianxin Wei +17
Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, recover from failures, and reuse experience across extended interactions.…
Code as Agent Harness
Xuying Ning, Katherine Tieu, Dongqi Fu +39
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineerin…
Learning Higher-Order Interactions from Incomplete Spatiotemporal Data with Mixtures of Hypergraph Laplacians
Keshu Wu, Sixu Li, Zihao Li +3
Traffic sensor data exhibit strong temporal and cross-sensor dependencies, yet long gaps erase both a sensor's local history and the joint observations needed to learn its relation…
Heterogeneous Scientific Foundation Model Collaboration
Zihao Li, Jiaru Zou, Feihao Fang +6
Agentic large language model systems have demonstrated strong capabilities. However, their reliance on language as the universal interface fundamentally limits their applicability…
TSAQA: Time Series Analysis Question And Answering Benchmark
Baoyu Jing, Sanhorn Chen, Lecheng Zheng +13
Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science. While recent work has begun to explore…
OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection
Lecheng Zheng, Dongqi Fu, Zihao Li +1
Graph data is informative to represent complex relationships such as transactions between accounts, communications between devices, and dependencies among machines or processes. Co…
Flow Matching Meets Biology and Life Science: A Survey
Zihao Li, Zhichen Zeng, Xiao Lin +9
Over the past decade, advances in generative modeling, such as generative adversarial networks, masked autoencoders, and diffusion models, have significantly transformed biological…
Learnable Spatial-Temporal Positional Encoding for Link Prediction
Katherine Tieu, Dongqi Fu, Zihao Li +2
Accurate predictions rely on the expressiveness power of graph deep learning frameworks like graph neural networks and graph transformers, where a positional encoding mechanism has…
Chain-of-Model Learning for Language Model
Kaitao Song, Xiaohua Wang, Xu Tan +14
In this paper, we propose a novel learning paradigm, termed Chain-of-Model (CoM), which incorporates the causal relationship into the hidden states of each layer as a chain style,…
CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
Zhining Liu, Zihao Li, Ze Yang +6
Class-imbalanced learning (CIL) on tabular data is important in many real-world applications where the minority class holds the critical but rare outcomes. In this paper, we presen…
Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuning
Jiaru Zou, Yikun Ban, Zihao Li +4
Large language models are typically adapted to downstream tasks through supervised fine-tuning on domain-specific data. While standard fine-tuning focuses on minimizing generation…
ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method
Dongqi Fu, Yada Zhu, Zhining Liu +10
Climate science studies the structure and dynamics of Earth's climate system and seeks to understand how climate changes over time, where the data is usually stored in the format o…
RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking
Jiaru Zou, Dongqi Fu, Sirui Chen +5
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating them with an external knowledge base to improve the answer relevance and accuracy. In real…
Language in the Flow of Time: Time-Series-Paired Texts Weaved into a Unified Temporal Narrative
Zihao Li, Xiao Lin, Zhining Liu +8
While many advances in time series models focus exclusively on numerical data, research on multimodal time series, particularly those involving contextual textual information, rema…
PyG-SSL: A Graph Self-Supervised Learning Toolkit
Lecheng Zheng, Baoyu Jing, Zihao Li +9
Graph Self-Supervised Learning (SSL) has emerged as a pivotal area of research in recent years. By engaging in pretext tasks to learn the intricate topological structures and prope…
APEX: Adaptive and Extreme Summarization for Personalized Knowledge Graphs
Zihao Li, Dongqi Fu, Mengting Ai +1
Knowledge graphs (KGs), which store an extensive number of relational facts, serve various applications. Recently, personalized knowledge graphs (PKGs) have emerged as a solution t…
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
Zihao Li, Lecheng Zheng, Bowen Jin +5
While great success has been achieved in building vision models with Contrastive Language-Image Pre-training (CLIP) over internet-scale image-text pairs, building transferable Grap…
Local Clustering on Complex Graphs and Complex Hypergraphs
Zihao Li, Dongqi Fu, Hengyu Liu +1
Local/seeded clustering aims to find a compact cluster near the given starting instances. While most existing studies on graph clustering assume a discrete graph setting (i.e., unw…
PageRank Bandits for Link Prediction
Yikun Ban, Jiaru Zou, Zihao Li +5
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been dire…
Hypergraphs as Weighted Directed Self-Looped Graphs: Spectral Properties, Clustering, Cheeger Inequality
Zihao Li, Dongqi Fu, Hengyu Liu +1
Hypergraphs naturally arise when studying group relations and have been widely used in the field of machine learning. To the best of our knowledge, the recently proposed edge-depen…
What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs
Dongqi Fu, Liri Fang, Zihao Li +3
Graphs, as a relational data structure, have been widely used for various application scenarios, like molecule design and recommender systems. Recently, large language models (LLMs…
SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval
Zihao Li, Yuyi Ao, Jingrui He
Knowledge graphs (KGs), which store an extensive number of relational facts (head, relation, tail), serve various applications. While many downstream tasks highly rely on the expre…
Heterogeneous Contrastive Learning for Foundation Models and Beyond
Lecheng Zheng, Baoyu Jing, Zihao Li +2
In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. Many existing f…