17 citations · 27 across the 14 of their papers we have counts for
14 papers
Calibration is the Bottleneck: An Action-Class Diagnostic of Multi-Turn Tool-Calling
Kangjia Zhao, Jiajun Li, Haozhan Shen +8
Multi-turn tool calling is a core evaluation scenario for large language model (LLM) agents. On public tool-calling benchmarks, open-weight models now approach or even surpass clos…
Which Pretraining Paradigm Better Serves Spatial Intelligence? An Empirical Comparison of Vision-Language and Video Generation Models
Haozhan Shen, Tiancheng Zhao, Kangjia Zhao +1
Spatial intelligence requires visual representations that capture both semantic objects and geometric structure in the physical world. To support this, two major pre-training schem…
MM-CondChain: A Programmatically Verified Benchmark for Visually Grounded Deep Compositional Reasoning
Haozhan Shen, Shilin Yan, Hongwei Xue +5
Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as navigating GUIs, where the next step depends on verified visual compositional c…
Talking to Yourself: Defying Forgetting in Large Language Models
Yutao Sun, Mingshuai Chen, Tiancheng Zhao +5
Catastrophic forgetting remains a major challenge when fine-tuning large language models (LLMs) on narrow, task-specific data, often degrading their general knowledge and reasoning…
VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained Perception in VLMs
Peng Liu, Haozhan Shen, Chunxin Fang +3
Vision-Language Models (VLMs) excel at high-level scene understanding but falter on fine-grained perception tasks requiring precise localization. This failure stems from a fundamen…
Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning
Zilun Zhang, Zian Guan, Tiancheng Zhao +7
Referring expression understanding in remote sensing poses unique challenges, as it requires reasoning over complex object-context relationships. While supervised fine-tuning (SFT)…
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research
Qianqian Zhang, Jiajia Liao, Heting Ying +9
Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. However, developing robu…
VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
Haozhan Shen, Peng Liu, Jingcheng Li +9
Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective…
GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing
Zilun Zhang, Haozhan Shen, Tiancheng Zhao +7
The application of Vision-Language Models (VLMs) in remote sensing (RS) has demonstrated significant potential in traditional tasks such as scene classification, object detection,…
GUI Testing Arena: A Unified Benchmark for Advancing Autonomous GUI Testing Agent
Kangjia Zhao, Jiahui Song, Leigang Sha +5
Nowadays, research on GUI agents is a hot topic in the AI community. However, current research focuses on GUI task automation, limiting the scope of applications in various GUI sce…
ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration
Haozhan Shen, Kangjia Zhao, Tiancheng Zhao +4
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in vision-language understanding. Recently, with the integration of test-time scaling techniques,…
ImageRAG: Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG
Zilun Zhang, Haozhan Shen, Tiancheng Zhao +7
Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large La…
GroundVLP: Harnessing Zero-shot Visual Grounding from Vision-Language Pre-training and Open-Vocabulary Object Detection
Haozhan Shen, Tiancheng Zhao, Mingwei Zhu +1
Visual grounding, a crucial vision-language task involving the understanding of the visual context based on the query expression, necessitates the model to capture the interactions…
VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations
Tiancheng Zhao, Tianqi Zhang, Mingwei Zhu +4
Vision-Language Pretraining (VLP) models have recently successfully facilitated many cross-modal downstream tasks. Most existing works evaluated their systems by comparing the fine…