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20242026
most citedOvis: Structural Embedding Alignment for Multimodal Large Language Model

4 citations · 10 across the 29 of their papers we have counts for

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13 papers · 1 filter

cs.LG2025

SPACE: Noise Contrastive Estimation Stabilizes Self-Play Fine-Tuning for Large Language Models

Yibo Wang, Qing-Guo Chen, Zhao Xu +3

Self-play fine-tuning has demonstrated promising abilities in adapting large language models (LLMs) to downstream tasks with limited real-world data. The basic principle is to iter…

cs.CV2025

Diffusion-SDPO: Safeguarded Direct Preference Optimization for Diffusion Models

Minghao Fu, Guo-Hua Wang, Tianyu Cui +4

Text-to-image diffusion models deliver high-quality images, yet aligning them with human preferences remains challenging. We revisit diffusion-based Direct Preference Optimization…

cs.CV2025

Towards Efficient VLMs: Information-Theoretic Driven Compression via Adaptive Structural Pruning

Zhaoqi Xu, Yingying Zhang, Jian Li +3

Recent advances in vision-language models (VLMs) have shown remarkable performance across multimodal tasks, yet their ever-growing scale poses severe challenges for deployment and…

cs.CV2025

Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images

JiaKui Hu, Shanshan Zhao, Qing-Guo Chen +6

This paper presents Omni-View, which extends the unified multimodal understanding and generation to 3D scenes based on multiview images, exploring the principle that "generation fa…

cs.CL2025

DeepWideSearch: Benchmarking Depth and Width in Agentic Information Seeking

Tian Lan, Bin Zhu, Qianghuai Jia +6

Current search agents fundamentally lack the ability to simultaneously perform \textit{deep} reasoning over multi-hop retrieval and \textit{wide}-scale information collection-a cri…

cs.AI2025

MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools

Wenhao Wang, Peizhi Niu, Zhao Xu +8

Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (…

cs.CL2025

Marco-Voice Technical Report

Fengping Tian, Chenyang Lyu, Xuanfan Ni +8

This paper presents a multifunctional speech synthesis system that integrates voice cloning and emotion control speech synthesis within a unified framework. The goal of this work i…

cs.CV2025

TeEFusion: Blending Text Embeddings to Distill Classifier-Free Guidance

Minghao Fu, Guo-Hua Wang, Xiaohao Chen +4

Recent advances in text-to-image synthesis largely benefit from sophisticated sampling strategies and classifier-free guidance (CFG) to ensure high-quality generation. However, CFG…

cs.CL2025

TransBench: Benchmarking Machine Translation for Industrial-Scale Applications

Haijun Li, Tianqi Shi, Zifu Shang +13

Machine translation (MT) has become indispensable for cross-border communication in globalized industries like e-commerce, finance, and legal services, with recent advancements in…

cs.CV2025★ 2 cited

Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities

Shanshan Zhao, Xinjie Zhang, Jintao Guo +9

Recent years have seen remarkable progress in both multimodal understanding models and image generation models. Despite their respective successes, these two domains have evolved i…

cs.CV2025

CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for Text-to-Image Generation

Minghao Fu, Guo-Hua Wang, Liangfu Cao +4

Diffusion models have emerged as a dominant approach for text-to-image generation. Key components such as the human preference alignment and classifier-free guidance play a crucial…

cs.CV2025

Evaluating Image Caption via Cycle-consistent Text-to-Image Generation

Tianyu Cui, Jinbin Bai, Guo-Hua Wang +5

Evaluating image captions typically relies on reference captions, which are costly to obtain and exhibit significant diversity and subjectivity. While reference-free evaluation met…

cs.CV2025

MDP3: A Training-free Approach for List-wise Frame Selection in Video-LLMs

Hui Sun, Shiyin Lu, Huanyu Wang +5

Video large language models (Video-LLMs) have made significant progress in understanding videos. However, processing multiple frames leads to lengthy visual token sequences, presen…