most citedFanar: An Arabic-Centric Multimodal Generative AI Platform

5 citations · 8 across the 9 of their papers we have counts for

collaborators

12 papers

cs.CL2026

PACER: Blockwise Pre-verification for Speculative Decoding with Adaptive Length

Situo Zhang, Yifan Zhang, Zichen Zhu +5

Speculative decoding (SD) is a powerful technique for accelerating the inference process of large language models (LLMs) without sacrificing accuracy. Typically, SD employs a small…

cs.CR2026

SMCP: Secure Model Context Protocol

Xinyi Hou, Shenao Wang, Yifan Zhang +4

Agentic AI systems built around large language models (LLMs) are moving away from closed, single-model frameworks and toward open ecosystems that connect a variety of agents, exter…

cs.AI2025

SPIRAL: Symbolic LLM Planning via Grounded and Reflective Search

Yifan Zhang, Giridhar Ganapavarapu, Srideepika Jayaraman +3

Large Language Models (LLMs) often falter at complex planning tasks that require exploration and self-correction, as their linear reasoning process struggles to recover from early…

cs.CV2025

Explicit Temporal-Semantic Modeling for Dense Video Captioning via Context-Aware Cross-Modal Interaction

Mingda Jia, Weiliang Meng, Zenghuang Fu +7

Dense video captioning jointly localizes and captions salient events in untrimmed videos. Recent methods primarily focus on leveraging additional prior knowledge and advanced multi…

cs.CL2025

Mental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Models

Khalid Hasan, Jamil Saquer, Yifan Zhang

Millions of people openly share mental health struggles on social media, providing rich data for early detection of conditions such as depression, bipolar disorder, etc. However, m…

cs.CV2025

Gradient-Attention Guided Dual-Masking Synergetic Framework for Robust Text-based Person Retrieval

Tianlu Zheng, Yifan Zhang, Xiang An +3

Although Contrastive Language-Image Pre-training (CLIP) exhibits strong performance across diverse vision tasks, its application to person representation learning faces two critica…