49 citations · 168 across the 29 of their papers we have counts for
30 papers
WebHarbor: Docking Real Websites for Evolving GUI Agent Environments
Zhaoyang Wang, Qianhui Wu, Boyu Gou +54
GUI web agents require realistic and interactive environments that remain stable during both evaluation and training. Live website evaluation suffers from changing content, access…
Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
Sizhe Zhou, Sheldon Yu, Hui Wei +8
Deployed LLM agents increasingly keep their long-term memory as a filesystem: a directory tree of markdown files that the agent itself reads, writes, and reorganizes through generi…
How Hard Does It Think? Analyzing Step-Aware Reasoning Energy in LLM Chain-of-Thought Trajectories
Hui Wei, Junda Wu, Sheldon Yu +8
Understanding how computational effort is allocated across individual chain-of-thought (CoT) reasoning steps remains an open challenge: existing interpretability methods rely on ou…
F-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking
Rohan Surana, Gagan Mundada, Junda Wu +9
Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models…
Primal Generation, Dual Judgment: Self-Training from Test-Time Scaling
Yizhu Jiao, Ruixiang Zhang, Richard Bai +3
Code generation is typically trained in the primal space of programs: a model produces a candidate solution and receives sparse execution feedback, often a single pass/fail bit. Te…
SkillOS: Learning Skill Curation for Self-Evolving Agents
Siru Ouyang, Jun Yan, Yanfei Chen +13
LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills disti…
Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation
Jiashuo Sun, Pengcheng Jiang, Saizhuo Wang +13
Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise, even when the required evidence appears in the top-K results. A key reason is that retr…
TEXT2DB: Integration-Aware Information Extraction with Large Language Model Agents
Yizhu Jiao, Sha Li, Sizhe Zhou +2
The task of information extraction (IE) is to extract structured knowledge from text. However, it is often not straightforward to utilize IE output due to the mismatch between the…
Think Twice: Branch-and-Rethink Reasoning Reward Model
Yizhu Jiao, Jiaqi Zeng, Julien Veron Vialard +3
Large language models (LLMs) increasingly rely on thinking models that externalize intermediate steps and allocate extra test-time compute, with think-twice strategies showing that…
A Survey on Retrieval And Structuring Augmented Generation with Large Language Models
Pengcheng Jiang, Siru Ouyang, Yizhu Jiao +3
Large Language Models (LLMs) have revolutionized natural language processing with their remarkable capabilities in text generation and reasoning. However, these models face critica…
When Do LLMs Help With Node Classification? A Comprehensive Analysis
Xixi Wu, Yifei Shen, Fangzhou Ge +4
Node classification is a fundamental task in graph analysis, with broad applications across various fields. Recent breakthroughs in Large Language Models (LLMs) have enabled LLM-ba…
ProCom: A Few-shot Targeted Community Detection Algorithm
Xixi Wu, Kaiyu Xiong, Yun Xiong +4
Targeted community detection aims to distinguish a particular type of community in the network. This is an important task with a lot of real-world applications, e.g., identifying f…
Investigating Instruction Tuning Large Language Models on Graphs
Kerui Zhu, Bo-Wei Huang, Bowen Jin +5
Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks, there's growing interest in applying LLMs to graph-related tasks. This study delves into the capab…
Synergizing Unsupervised Episode Detection with LLMs for Large-Scale News Events
Priyanka Kargupta, Yunyi Zhang, Yizhu Jiao +2
State-of-the-art automatic event detection struggles with interpretability and adaptability to evolving large-scale key events -- unlike episodic structures, which excel in these a…
Establishing Knowledge Preference in Language Models
Sizhe Zhou, Sha Li, Yu Meng +3
Language models are known to encode a great amount of factual knowledge through pretraining. However, such knowledge might be insufficient to cater to user requests, requiring the…
Multi-LoRA Composition for Image Generation
Ming Zhong, Yelong Shen, Shuohang Wang +6
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated im…
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Yizhu Jiao, Ming Zhong, Sha Li +4
Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction - a classic task in natural…
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Siru Ouyang, Shuohang Wang, Yang Liu +7
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the exist…
RDGSL: Dynamic Graph Representation Learning with Structure Learning
Siwei Zhang, Yun Xiong, Yao Zhang +4
Temporal Graph Networks (TGNs) have shown remarkable performance in learning representation for continuous-time dynamic graphs. However, real-world dynamic graphs typically contain…
Dual Intents Graph Modeling for User-centric Group Discovery
Xixi Wu, Yun Xiong, Yao Zhang +2
Online groups have become increasingly prevalent, providing users with space to share experiences and explore interests. Therefore, user-centric group discovery task, i.e., recomme…
ReactIE: Enhancing Chemical Reaction Extraction with Weak Supervision
Ming Zhong, Siru Ouyang, Minhao Jiang +4
Structured chemical reaction information plays a vital role for chemists engaged in laboratory work and advanced endeavors such as computer-aided drug design. Despite the importanc…
Explaining and Adapting Graph Conditional Shift
Qi Zhu, Yizhu Jiao, Natalia Ponomareva +2
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution sh…
ConsRec: Learning Consensus Behind Interactions for Group Recommendation
Xixi Wu, Yun Xiong, Yao Zhang +4
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation tas…
Open-Vocabulary Argument Role Prediction for Event Extraction
Yizhu Jiao, Sha Li, Yiqing Xie +3
The argument role in event extraction refers to the relation between an event and an argument participating in it. Despite the great progress in event extraction, existing studies…