activity
20222026
most citedA Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering

30 citations · 40 across the 11 of their papers we have counts for

collaborators

11 papers

cs.CL2026

Efficient RAG with Intent-Aware Retrieval and Semantics-Preserving Chunking

Fachrina Dewi Puspitasari, Chaoning Zhang, Jiaquan Zhang +6

The demand for powerful instruction following and reasoning capability of large language models (LLMs) has promoted rapid development of retrieval-augmented generation (RAG). The R…

cs.CL2026

Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt

Zhenzhen Huang, Chaoning Zhang, Fachrina Dewi Puspitasari +4

Large language models (LLMs) are increasingly utilized in various complex reasoning tasks due to their excellent instruction following capability. However, the model's performance…

cs.NE2026

Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models

Xudong Wang, Chaoning Zhang, Chenghao Li +10

Large Language Models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, while recent prompting strategies such as Chain-of-Thought (CoT) have further elevate…

cs.LG2026

Geometric Neural Operators via Lie Group-Constrained Latent Dynamics

Jiaquan Zhang, Fachrina Dewi Puspitasari, Songbo Zhang +7

Neural operators offer an effective framework for learning solutions of partial differential equations for many physical systems in a resolution-invariant and data-driven manner. E…

cs.CL2026

Text summarization via global structure awareness

Jiaquan Zhang, Chaoning Zhang, Shuxu Chen +9

Text summarization is a fundamental task in natural language processing (NLP), and the information explosion has made long-document processing increasingly demanding, making summar…

cs.CV2025

Fast SAM2 with Text-Driven Token Pruning

Avilasha Mandal, Chaoning Zhang, Fachrina Dewi Puspitasari +6

Segment Anything Model 2 (SAM2), a vision foundation model has significantly advanced in prompt-driven video object segmentation, yet their practical deployment remains limited by…