1 citations · 1 across the 8 of their papers we have counts for
12 papers
A Unified Arena for LLM Agents across Diverse Eurogame
Fengrui Zhang, Xiang Li, Ruomeng Ding +6
Interactive environments offer a practical way to evaluate large language model (LLM) agents, and competitive games extend this evaluation to decisions shaped by competing goals an…
Combinatorics of degeneracy in vertical systems
Carles Checa, Nidhi Kaihnsa
We characterize the generic consistency of vertical systems in terms of the iterated circuits of the column matroid of the coefficient matrix. More precisely, we prove that such a…
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study
Ruomeng Ding, Qianli Shen, Zhaoyang Han +3
LLM data engineering requires iterative experimentation to determine how data selection, rewards, and curricula affect model learning, making it a natural setting for AI-for-AI (AI…
SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation
Zhou Liu, Ligang Huang, Chenxu Zhang +7
Graphical user interface (GUI) agents can reuse past interaction experience. We study how illustrated procedures support action prediction and students deployed without retrieval.…
DataPrep-Bench: Benchmarking LLMs as Training Data Preparators
Hao Liang, Qifeng Cai, Yibo Lin +11
The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-c…
K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs
Hao Liang, Qihan Lin, Zhaoyang Han +7
Large language models are increasingly used in K-12 education, but existing benchmarks mainly test exam question answering rather than understanding how curriculum knowledge is str…
DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models
Hao Liang, Zhengyang Zhao, Meiyi Qiang +22
Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters but also the selection, composition, a…
Towards Next-Generation LLM Training: From the Data-Centric Perspective
Hao Liang, Zhengyang Zhao, Zhaoyang Han +8
Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks and domains, with data playing a central role in enabling these advances. Despite…
DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI
Hao Liang, Xiaochen Ma, Zhou Liu +32
The rapidly growing demand for high-quality data in Large Language Models (LLMs) has intensified the need for scalable, reliable, and semantically rich data preparation pipelines.…
DataGovBench: Benchmarking LLM Agents for Real-World Data Governance Workflows
Zhou Liu, Zhaoyang Han, Guochen Yan +5
Data governance ensures data quality, security, and compliance through policies and standards, a critical foundation for scaling modern AI development. Recently, large language mod…
LongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding
ZhaoYang Han, Qihan Lin, Hao Liang +3
We introduce \textbf{LongInsightBench}, the first benchmark designed to assess models' ability to understand long videos, with a focus on human language, viewpoints, actions, and o…
LoVR: A Benchmark for Long Video Retrieval in Multimodal Contexts
Qifeng Cai, Hao Liang, Zhaoyang Han +6
Long videos contain a vast amount of information, making video-text retrieval an essential and challenging task in multimodal learning. However, existing benchmarks suffer from lim…