12 citations · 12 across the 10 of their papers we have counts for
12 papers
TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
Peng Cai, Zhaofan Zou, Shifa Liu +7
Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-Language Models (VLMs) have significantly…
DataOrchestra: Learning to Orchestrate Per-Example Curation of Pretraining Data
Zhen Huang, Yikun Wang, Shijie Xia +1
Pretraining data processing is critical to the downstream performance of Large Language Models (LLMs). However, many existing approaches define a fixed processing strategy at the c…
Diagnosing and Mitigating Context Rot in Long-horizon Search
Shijie Xia, Yikun Wang, Zhen Huang +1
Extensive context has become the norm as Large Language Models (LLMs) are increasingly deployed in long-horizon search tasks. The concern that increasing context length degrades mo…
LAGS: Low-Altitude Gaussian Splatting with Groupwise Heterogeneous Graph Learning
Yikun Wang, Yujie Wan, Wei Zuo +4
Low-altitude Gaussian splatting (LAGS) facilitates 3D scene reconstruction by aggregating aerial images from distributed drones. However, as LAGS prioritizes maximizing reconstruct…
GeoVista: Web-Augmented Agentic Visual Reasoning for Geolocalization
Yikun Wang, Zuyan Liu, Ziyi Wang +3
Current research on agentic visual reasoning enables deep multimodal understanding but primarily focuses on image manipulation tools, leaving a gap toward more general-purpose agen…
EnvX: Agentize Everything with Agentic AI
Linyao Chen, Zimian Peng, Yingxuan Yang +4
The widespread availability of open-source repositories has led to a vast collection of reusable software components, yet their utilization remains manual, error-prone, and disconn…
MoIIE: Mixture of Intra- and Inter-Modality Experts for Large Vision Language Models
Dianyi Wang, Siyuan Wang, Zejun Li +6
Large Vision-Language Models (LVLMs) have demonstrated remarkable performance across multi-modal tasks by scaling model size and training data. However, these dense LVLMs incur sig…
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better
Dianyi Wang, Wei Song, Yikun Wang +4
Typical large vision-language models (LVLMs) apply autoregressive supervision solely to textual sequences, without fully incorporating the visual modality into the learning process…
GeometryZero: Advancing Geometry Solving via Group Contrastive Policy Optimization
Yikun Wang, Yibin Wang, Dianyi Wang +4
Recent progress in large language models (LLMs) has boosted mathematical reasoning, yet geometry remains challenging where auxiliary construction is often essential. Prior methods…
VisuoThink: Empowering LVLM Reasoning with Multimodal Tree Search
Yikun Wang, Siyin Wang, Qinyuan Cheng +5
Recent advancements in Large Vision-Language Models have showcased remarkable capabilities. However, they often falter when confronted with complex reasoning tasks that humans typi…
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
Junjie Ye, Nuo Xu, Yikun Wang +4
Despite the impressive capabilities of large language models (LLMs), their performance on information extraction tasks is still not entirely satisfactory. However, their remarkable…
Rescue: Ranking LLM Responses with Partial Ordering to Improve Response Generation
Yikun Wang, Rui Zheng, Haoming Li +3
Customizing LLMs for a specific task involves separating high-quality responses from lower-quality ones. This skill can be developed using supervised fine-tuning with extensive hum…