108 citations · 115 across the 22 of their papers we have counts for
22 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…
Continual Video-MLLM Adaptation over Evolving Domains
Rui Cheng, Meixing Shi, Yuxiang Cai +3
Video multimodal large language models have shown strong capability in video understanding, yet their adaptation to sequentially evolving domains remains underexplored. In real-wor…
Spectral Evolution-Guided Token Pruning in Multimodal Large Language Models
Bin Chen, Yuxiang Cai, Yadan Luo +3
Reducing visual token redundancy is critical for accelerating Multimodal Large Language Models (MLLMs) without degrading cross-modal reasoning performance. Existing token pruning m…
Accelerating Multimodal Large Language Models with Prior-Corrected Token Reduction
Zengjie Chen, Yuxiang Cai, Jingcai Guo +3
Visual token reduction has emerged as an effective strategy for accelerating Multimodal Large Language Models (MLLMs). Many existing methods prune tokens by ranking text-visual att…
REKEY: Metadata-Grounded Visual-Key Regeneration for Contamination-Resilient VQA Evaluation
Tengjie Lin, Yutao Sun, Jingwei Ni +7
Static visual question answering (VQA) benchmarks age quickly: Once the items leak into training corpora, scores can reflect memorization rather than genuine visual ability, thus o…
VITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs
Qiaoru Li, Shaotian Liang, Jintao Chen +4
Latent reasoning enables reasoning over continuous hidden states rather than explicit tokens, avoiding the language bottleneck and inference overhead of chain-of-thought for medica…
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…
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)…
SRMF: A Data Augmentation and Multimodal Fusion Approach for Long-Tail UHR Satellite Image Segmentation
Yulong Guo, Zilun Zhang, Yongheng Shang +4
The long-tail problem presents a significant challenge to the advancement of semantic segmentation in ultra-high-resolution (UHR) satellite imagery. While previous efforts in UHR s…
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,…
The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?
Yutao Sun, Mingshuai Chen, Tiancheng Zhao +3
Self-improving large language models (LLMs) -- i.e., to improve the performance of an LLM by fine-tuning it with synthetic data generated by itself -- is a promising way to advance…
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,…
Parf: Adaptive Parameter Refining for Abstract Interpretation
Zhongyi Wang, Linyu Yang, Mingshuai Chen +6
The core challenge in applying abstract interpretation lies in the configuration of abstraction and analysis strategies encoded by a large number of external parameters of static a…
Preserving Knowledge in Large Language Model with Model-Agnostic Self-Decompression
Zilun Zhang, Yutao Sun, Tiancheng Zhao +4
Humans can retain old knowledge while learning new information, but Large Language Models (LLMs) often suffer from catastrophic forgetting when post-pretrained or supervised fine-t…
HORAE: A Domain-Agnostic Language for Automated Service Regulation
Yutao Sun, Mingshuai Chen, Tiancheng Zhao +8
Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domai…
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…
Benchmarking Sequential Visual Input Reasoning and Prediction in Multimodal Large Language Models
Mingwei Zhu, Leigang Sha, Yu Shu +3
Multimodal large language models (MLLMs) have shown great potential in perception and interpretation tasks, but their capabilities in predictive reasoning remain under-explored. To…
RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing
Zilun Zhang, Tiancheng Zhao, Yulong Guo +1
Pre-trained Vision-Language Models (VLMs) utilizing extensive image-text paired data have demonstrated unprecedented image-text association capabilities, achieving remarkable resul…
DetailCLIP: Injecting Image Details into CLIP's Feature Space
Zilun Zhang, Cuifeng Shen, Yuan Shen +4
Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e.g., 224),…