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20232026
most citedRetrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely

13 citations · 44 across the 37 of their papers we have counts for

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10 papers · 1 filter

cs.AI2026

A report-grounded vision-language foundation model for colonoscopy from 280000 routine reports

Jia Yu, Yan Zhu, Yili He +12

Vision-language models remain underused in colonoscopy despite the rich expert descriptions recorded in routine reports. These reports document lesion appearance, size and location…

cs.AI2026

MedCUA-Bench: A Screenshot-Only Benchmark for Clinical Computer-Use Agents

Jia Yu, Zilong Wang, Xinyang Jiang +2

Computer-use agents could automate repetitive screen-based clinical work, but their reliability in medical graphical user interfaces remains largely unvalidated. Existing benchmark…

cs.AI2026

Reasoning-Driven Multimodal LLM for Domain Generalization

Zhipeng Xu, Zilong Wang, Xinyang Jiang +3

This paper addresses the domain generalization (DG) problem in deep learning. While most DG methods focus on enforcing visual feature invariance, we leverage the reasoning capabili…

cs.AI2026

Mirror: A Multi-Agent System for AI-Assisted Ethics Review

Yifan Ding, Yuhui Shi, Zhiyan Li +10

Ethics review is a foundational mechanism of modern research governance, yet contemporary systems face increasing strain as ethical risks arise as structural consequences of large-…

cs.AI2026

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

Xingjun Ma, Yixu Wang, Hengyuan Xu +18

The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and generation across language and…

cs.AI2025

A Tale of LLMs and Induced Small Proxies: Scalable Small Language Models for Knowledge Mining

Sipeng Zhang, Longfei Yun, Zilong Wang +2

At the core of Deep Research is knowledge mining, the task of extracting structured information from massive unstructured text in response to user instructions. Large language mode…

cs.AI2025

Agent Lightning: Train ANY AI Agents with Reinforcement Learning

Xufang Luo, Yuge Zhang, Zhiyuan He +5

We present Agent Lightning, a flexible and extensible framework that enables Reinforcement Learning (RL)-based training of Large Language Models (LLMs) for any AI agent. Unlike exi…

cs.AI2025

RRO: LLM Agent Optimization Through Rising Reward Trajectories

Zilong Wang, Jingfeng Yang, Sreyashi Nag +5

Large language models (LLMs) have exhibited extraordinary performance in a variety of tasks while it remains challenging for them to solve complex multi-step tasks as agents. In pr…

cs.AI2025

Memorize or Generalize? Evaluating LLM Code Generation with Code Rewriting

Lizhe Zhang, Wentao Chen, Li Zhong +3

Large language models (LLMs) have recently demonstrated exceptional code generation capabilities. However, there is a growing debate whether LLMs are mostly doing memorization (i.e…

cs.AI2024

The Application of ChatGPT in Responding to Questions Related to the Boston Bowel Preparation Scale

Xiaoqiang Liu, Yubin Wang, Zicheng Huang +8

Background: Colonoscopy, a crucial diagnostic tool in gastroenterology, depends heavily on superior bowel preparation. ChatGPT, a large language model with emergent intelligence wh…