16 citations · 37 across the 16 of their papers we have counts for
21 papers
The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement
Yi Duan, Ying Liu, Zirui Tang +32
Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and the process of future improveme…
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…
MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale
Bin Wang, Tianyao He, Linke Ouyang +40
Current document parsing methods advance primarily through model architecture innovation, while systematic engineering of training data remains underexplored. Yet state-of-the-art…
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…
MinerU-Diffusion: Rethinking Document OCR as Inverse Rendering via Diffusion Decoding
Hejun Dong, Junbo Niu, Bin Wang +3
Optical character recognition (OCR) has evolved from line-level transcription to structured document parsing, requiring models to recover long-form sequences containing layout, tab…
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.…
VADE: Variance-Aware Dynamic Sampling via Online Sample-Level Difficulty Estimation for Multimodal RL
Zengjie Hu, Jiantao Qiu, Tianyi Bai +5
Group-based policy optimization methods like GRPO and GSPO have become standard for training multimodal models, leveraging group-wise rollouts and relative advantage estimation. Ho…
Heterogeneous Adaptive Policy Optimization: Tailoring Optimization to Every Token's Nature
Zheng Liu, Mengjie Liu, Siwei Wen +4
Using entropy as a measure of heterogeneity to guide optimization has emerged as a crucial research direction in Reinforcement Learning for LLMs. However, existing methods typicall…
Native Visual Understanding: Resolving Resolution Dilemmas in Vision-Language Models
Junbo Niu, Yuanhong Zheng, Ziyang Miao +8
Vision-Language Models (VLMs) face significant challenges when dealing with the diverse resolutions and aspect ratios of real-world images, as most existing models rely on fixed, l…
Multi-Step Visual Reasoning with Visual Tokens Scaling and Verification
Tianyi Bai, Zengjie Hu, Fupeng Sun +7
Multi-modal large language models (MLLMs) have achieved remarkable capabilities by integrating visual perception with language understanding, enabling applications such as image-gr…
Hallucination at a Glance: Controlled Visual Edits and Fine-Grained Multimodal Learning
Tianyi Bai, Yuxuan Fan, Jiantao Qiu +7
Multimodal large language models (MLLMs) have achieved strong performance on vision-language tasks but still struggle with fine-grained visual differences, leading to hallucination…
SAS-Bench: A Fine-Grained Benchmark for Evaluating Short Answer Scoring with Large Language Models
Peichao Lai, Kexuan Zhang, Yi Lin +8
Subjective Answer Grading (SAG) plays a crucial role in education, standardized testing, and automated assessment systems, particularly for evaluating short-form responses in Short…
FLARE: Fully Integration of Vision-Language Representations for Deep Cross-Modal Understanding
Zheng Liu, Mengjie Liu, Jingzhou Chen +4
We introduce FLARE, a family of vision language models (VLMs) with a fully vision-language alignment and integration paradigm. Unlike existing approaches that rely on single MLP pr…
A Comprehensive Survey on Imbalanced Data Learning
Xinyi Gao, Dongting Xie, Yihang Zhang +5
With the expansion of data availability, machine learning (ML) has achieved remarkable breakthroughs in both academia and industry. However, imbalanced data distributions are preva…
Gradual Learning: Optimizing Fine-Tuning with Partially Mastered Knowledge in Large Language Models
Bozhou Li, Hao Liang, Yang Li +4
During the pretraining phase, large language models (LLMs) acquire vast amounts of knowledge from extensive text corpora. Nevertheless, in later stages such as fine-tuning and infe…
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
Linzhuang Sun, Hao Liang, Jingxuan Wei +4
Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains. However, they still encounter difficulties in solving mathematical pr…
Synth-Empathy: Towards High-Quality Synthetic Empathy Data
Hao Liang, Linzhuang Sun, Jingxuan Wei +5
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capabilities has become a crucial prerequisite. Consequently,…
KeyVideoLLM: Towards Large-scale Video Keyframe Selection
Hao Liang, Jiapeng Li, Tianyi Bai +7
Recently, with the rise of web videos, managing and understanding large-scale video datasets has become increasingly important. Video Large Language Models (VideoLLMs) have emerged…
A Survey of Multimodal Large Language Model from A Data-centric Perspective
Tianyi Bai, Hao Liang, Binwang Wan +12
Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing data from multiple modalities, including text, vis…
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training
Le Zhuo, Zewen Chi, Minghao Xu +5
We propose ProtLLM, a versatile cross-modal large language model (LLM) for both protein-centric and protein-language tasks. ProtLLM features a unique dynamic protein mounting mecha…
IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts
Bohan Zeng, Shanglin Li, Yutang Feng +9
Recent advances in 3D generation have been remarkable, with methods such as DreamFusion leveraging large-scale text-to-image diffusion-based models to guide 3D object generation. T…