activity
20242026
collaborators

11 papers

cs.CV2026

MG-Thinker: Bi-Axial Self-Reflection for Multi-Image Reasoning Grounding

Heyu Huang, Chi Chen, Zonghao Guo +3

Reinforcement learning (RL) has recently delivered substantial gains in multimodal reasoning, opening a promising route for fine-grained visual perception. Yet for multi-image reas…

cs.AI2026

RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation

Bingxian Wu, Yu Zhang, Zonghao Guo +9

Geoscience research requires complex analysis and domain expertise, with remote sensing (RS) observations as a key foundation. However, existing RS agents built on general-purpose…

cs.IR2026

HiEviDR-Bench: A Benchmark for Hierarchical Evidence Aggregation in Deep Research

Yubo Sun, Chunyi Peng, Yukun Yan +6

Deep research requires models to retrieve, connect, and synthesize evidence from large-scale heterogeneous sources to answer complex queries and produce analytical reports. Existin…

cs.CV2026

Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation

Yichen Zhang, Da Peng, Zonghao Guo +19

A recent cutting-edge topic in multimodal modeling is to unify visual comprehension and generation within a single model. However, the two tasks demand mismatched decoding regimes…

cs.CL2026

Imagination Helps Visual Reasoning, But Not Yet in Latent Space

You Li, Chi Chen, Yanghao Li +4

Latent visual reasoning aims to mimic human's imagination process by meditating through hidden states of Multimodal Large Language Models. While recognized as a promising paradigm…

cs.CV2025

MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?

Shiqi Dai, Zizhi Ma, Zhicong Luo +8

While Multimodal Large Language Models (MLLMs) have exhibited remarkable general intelligence across diverse domains, their potential in low-altitude applications dominated by Unma…

cs.CV2025

LLaVA-UHD v3: Progressive Visual Compression for Efficient Native-Resolution Encoding in MLLMs

Shichu Sun, Yichen Zhang, Haolin Song +6

Visual encoding followed by token condensing has become the standard architectural paradigm in multi-modal large language models (MLLMs). Many recent MLLMs increasingly favor globa…

cs.CL2025

VisRAG2.0: Mitigating Visual Hallucinations via Evidence-Guided Multi-Image Reasoning in Visual Retrieval-Augmented Generation

Yubo Sun, Chunyi Peng, Yukun Yan +5

Visual Retrieval-Augmented Generation (VRAG) has emerged as a promising paradigm for equipping Vision-Language Models (VLMs) with external visual evidence, enabling them to go beyo…

cs.AI2025

AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning

Zhong Zhang, Yaxi Lu, Yikun Fu +22

The recent progress of large language model agents has opened new possibilities for automating tasks through graphical user interfaces (GUIs), especially in mobile environments whe…

cs.CV2025

Will Pre-Training Ever End? A First Step Toward Next-Generation Foundation MLLMs via Self-Improving Systematic Cognition

Xiaoying Zhang, Da Peng, Yipeng Zhang +7

Recent progress in (multimodal) large language models ((M)LLMs) has shifted focus from pre-training to inference-time computation and post-training optimization, largely due to con…

cs.CV2024

LLaVA-UHD v2: an MLLM Integrating High-Resolution Semantic Pyramid via Hierarchical Window Transformer

Yipeng Zhang, Yifan Liu, Zonghao Guo +10

Vision transformers (ViTs) are widely employed in multimodal large language models (MLLMs) for visual encoding. However, they exhibit inferior performance on tasks regarding fine-g…