21 citations · 65 across the 19 of their papers we have counts for
20 papers
Where Should the Gaussians Go? Reusable Label-Aware Allocation for Budget-Constrained Gaussian Splatting of Cinematic CT
Bowen Xiang, Dingjie Su, Jon S. Heiselman +1
Anatomical labels help 3D Gaussian splatting preserve vessels in cinematic renderings of CT, but the representation is still found by optimising each patient's scene, where densifi…
Learning to Evaluate Before Improving: Automatic Rubric Induction for Automatic Research Agents
Xuehai Wang, Haowei Qin, Tongxin Liu +6
Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generatio…
PRISM Edit: One Vector for All Temporal Answers
Chen Huang, Qi Zheng, Ruiqin Zheng +2
Model editing keeps large language models (LLMs) up to date without retraining, but temporal facts expose a limitation of the prevailing locate-and-edit paradigm: an update is not…
OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents
Jingsheng Zheng, Xinyuan Fang, Jintian Zhang +3
LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life. These tasks are long-horizon, cross-environment, and multimodal, forcing the ag…
Exploring Autonomous Agentic Data Engineering for Model Specialization
Yujie Luo, Xiangyuan Ru, Jingsheng Zheng +10
Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without high-quality domain-specific data…
LightThinker++: From Reasoning Compression to Memory Management
Yuqi Zhu, Jintian Zhang, Zhenjie Wan +7
Large language models (LLMs) excel at complex reasoning, yet their efficiency is limited by the surging cognitive overhead of long thought traces. In this paper, we propose LightTh…
Can We Predict Before Executing Machine Learning Agents?
Jingsheng Zheng, Jintian Zhang, Yujie Luo +5
Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous approaches suffer from a…
InnoGym: Benchmarking the Innovation Potential of AI Agents
Jintian Zhang, Kewei Xu, Jingsheng Zheng +10
LLMs and Agents have achieved impressive progress in code generation, mathematical reasoning, and scientific discovery. However, existing benchmarks primarily measure correctness,…
Scaling Generalist Data-Analytic Agents
Shuofei Qiao, Yanqiu Zhao, Zhisong Qiu +8
Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engi…
Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study
Yuqi Zhu, Yi Zhong, Jintian Zhang +7
Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In…
AutoMind: Adaptive Knowledgeable Agent for Automated Data Science
Yixin Ou, Yujie Luo, Jingsheng Zheng +9
Large Language Model (LLM) agents have shown great potential in addressing real-world data science problems. LLM-driven data science agents promise to automate the entire machine l…
Knowledge Augmented Complex Problem Solving with Large Language Models: A Survey
Da Zheng, Lun Du, Junwei Su +6
Problem-solving has been a fundamental driver of human progress in numerous domains. With advancements in artificial intelligence, Large Language Models (LLMs) have emerged as powe…
LightThinker: Thinking Step-by-Step Compression
Jintian Zhang, Yuqi Zhu, Mengshu Sun +6
Large language models (LLMs) have shown remarkable performance in complex reasoning tasks, but their efficiency is hindered by the substantial memory and computational costs associ…
OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs
Jintian Zhang, Cheng Peng, Mengshu Sun +6
Despite the recent advancements in Large Language Models (LLMs), which have significantly enhanced the generative capabilities for various NLP tasks, LLMs still face limitations in…
OneEdit: A Neural-Symbolic Collaboratively Knowledge Editing System
Ningyu Zhang, Zekun Xi, Yujie Luo +11
Knowledge representation has been a central aim of AI since its inception. Symbolic Knowledge Graphs (KGs) and neural Large Language Models (LLMs) can both represent knowledge. KGs…
A Comprehensive Study of Knowledge Editing for Large Language Models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian +19
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies…
Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View
Jintian Zhang, Xin Xu, Ningyu Zhang +3
As Natural Language Processing (NLP) systems are increasingly employed in intricate social environments, a pressing query emerges: Can these NLP systems mirror human-esque collabor…
Agents: An Open-source Framework for Autonomous Language Agents
Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li +15
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with e…
InstructIE: A Bilingual Instruction-based Information Extraction Dataset
Honghao Gui, Shuofei Qiao, Jintian Zhang +6
Large language models can perform well on general natural language tasks, but their effectiveness is still suboptimal for information extraction (IE). Recent works indicate that th…
Continual Multimodal Knowledge Graph Construction
Xiang Chen, Jintian Zhang, Xiaohan Wang +5
Current Multimodal Knowledge Graph Construction (MKGC) models struggle with the real-world dynamism of continuously emerging entities and relations, often succumbing to catastrophi…