activity
20232026
most citedDocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding

7 citations · 12 across the 18 of their papers we have counts for

collaborators

20 papers

cs.CL2026

From Player to Master: Enhancing Test-Time Learning of LLM Agents via Reinforcement Learning over Memory

Yishuo Cai, Xingyu Guo, Xuancheng Huang +8

Large language model (LLM) agents are increasingly deployed in long-running settings where improving through experience at test time becomes important. A common approach is to upda…

cs.CV2026

AeSlides: Incentivizing Aesthetic Layout in LLM-Based Slide Generation via Verifiable Rewards

Yiming Pan, Chengwei Hu, Xuancheng Huang +6

Large language models (LLMs) have demonstrated strong potential in agentic tasks, particularly in slide generation. However, slide generation poses a fundamental challenge: the gen…

cs.CL2026

Projection-Free Evolution Strategies for Continuous Prompt Search

Yu Cai, Canxi Huang, Xiaoyu He

Continuous prompt search offers a computationally efficient alternative to conventional parameter tuning in natural language processing tasks. Nevertheless, its practical effective…

cs.CV2026

TextPecker: Rewarding Structural Anomaly Quantification for Enhancing Visual Text Rendering

Hanshen Zhu, Yuliang Liu, Xuecheng Wu +7

Visual Text Rendering (VTR) remains a critical challenge in text-to-image generation, where even advanced models frequently produce text with structural anomalies such as distortio…

cs.CV2026

Dolphin-v2: Universal Document Parsing via Scalable Anchor Prompting

Hao Feng, Wei Shi, Ke Zhang +9

Document parsing has garnered widespread attention as vision-language models (VLMs) advance OCR capabilities. However, the field remains fragmented across dozens of specialized mod…

cs.CV2025

UniRec-0.1B: Unified Text and Formula Recognition with 0.1B Parameters

Yongkun Du, Zhineng Chen, Yazhen Xie +6

Text and formulas constitute the core informational components of many documents. Accurately and efficiently recognizing both is crucial for developing robust and generalizable doc…

cs.CL2025

Benchmarking Vision-Language Models on Chinese Ancient Documents: From OCR to Knowledge Reasoning

Haiyang Yu, Yuchuan Wu, Fan Shi +18

Chinese ancient documents, invaluable carriers of millennia of Chinese history and culture, hold rich knowledge across diverse fields but face challenges in digitization and unders…

cs.CL2025

Post-Completion Learning for Language Models

Xiang Fei, Siqi Wang, Shu Wei +5

Current language model training paradigms typically terminate learning upon reaching the end-of-sequence (<eos>) token, overlooking the potential learning opportunities in the post…

cs.AI2025

MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement

Weitao Jia, Jinghui Lu, Haiyang Yu +17

Recent advances demonstrate that reinforcement learning with verifiable rewards (RLVR) significantly enhances the reasoning capabilities of large language models (LLMs). However, s…

cs.CL2025

Advancing Sequential Numerical Prediction in Autoregressive Models

Xiang Fei, Jinghui Lu, Qi Sun +6

Autoregressive models have become the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlo…

cs.CL2025

Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning

Jinghui Lu, Haiyang Yu, Siliang Xu +9

Recent advancements in reasoning have significantly enhanced the capabilities of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) across diverse tasks. How…

cs.CV2025

Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting

Hao Feng, Shu Wei, Xiang Fei +10

Document image parsing is challenging due to its complexly intertwined elements such as text paragraphs, figures, formulas, and tables. Current approaches either assemble specializ…

cs.CV2025★ 1 cited

Seed1.5-VL Technical Report

Dong Guo, Faming Wu, Feida Zhu +194

We present Seed1.5-VL, a vision-language foundation model designed to advance general-purpose multimodal understanding and reasoning. Seed1.5-VL is composed with a 532M-parameter v…

cs.CV2024

MCTBench: Multimodal Cognition towards Text-Rich Visual Scenes Benchmark

Bin Shan, Xiang Fei, Wei Shi +6

The comprehension of text-rich visual scenes has become a focal point for evaluating Multi-modal Large Language Models (MLLMs) due to their widespread applications. Current benchma…

cs.CV2024

ParGo: Bridging Vision-Language with Partial and Global Views

An-Lan Wang, Bin Shan, Wei Shi +7

This work presents ParGo, a novel Partial-Global projector designed to connect the vision and language modalities for Multimodal Large Language Models (MLLMs). Unlike previous work…

cs.CV2024★ 1 cited

Harmonizing Visual Text Comprehension and Generation

Zhen Zhao, Jingqun Tang, Binghong Wu +7

In this work, we present TextHarmony, a unified and versatile multimodal generative model proficient in comprehending and generating visual text. Simultaneously generating images a…

cs.CL2024

A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding

Jinghui Lu, Haiyang Yu, Yanjie Wang +9

Recently, many studies have demonstrated that exclusively incorporating OCR-derived text and spatial layouts with large language models (LLMs) can be highly effective for document…

cs.CV2024★ 1 cited

TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy

Weichao Zhao, Hao Feng, Qi Liu +9

Tables contain factual and quantitative data accompanied by various structures and contents that pose challenges for machine comprehension. Previous methods generally design task-s…

cs.CV2023★ 2 cited

Multi-modal In-Context Learning Makes an Ego-evolving Scene Text Recognizer

Zhen Zhao, Jingqun Tang, Chunhui Lin +6

Scene text recognition (STR) in the wild frequently encounters challenges when coping with domain variations, font diversity, shape deformations, etc. A straightforward solution is…

cs.CV2023★ 7 cited

DocPedia: Unleashing the Power of Large Multimodal Model in the Frequency Domain for Versatile Document Understanding

Hao Feng, Qi Liu, Hao Liu +4

This work presents DocPedia, a novel large multimodal model (LMM) for versatile OCR-free document understanding, capable of parsing images up to 2,5602,560 resolution. Unli…