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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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15 papers · 1 filter

cs.CL2026

Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework

Ziqiang Zhang, Jing Ma, Zilong Wang +6

Pricing automation in large-scale tourism is challenging because travel orders are highly unstructured, while pricing policies are complex, rapidly evolving, and inherently open-en…

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.LG2026

Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement

Siyuan Du, Mengxi Chen, Xinyang Jiang +6

Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex over…

cs.CV2026

Dual-Branch Cross-Projection Debiasing through Diffusion-based Disentanglement

Xiangqian Zhao, Xinyang Jiang, Zhipeng Xu +5

Foundation models trained on biased datasets often rely on spurious correlations between target labels and non-causal attributes, resulting in poor generalization on minority group…

cs.CL2026

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale

Ang Li, Ben Liu, Bin Han +215

Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…

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.CL2026

CogRAG: Tackling Heterogeneous Cognitive Demands in RAG via Stratified Retrieval and Reasoning

Xudong Wang, Zilong Wang, Kui Su +1

Retrieval-Augmented Generation (RAG) frameworks typically process all queries through a one-size-fits-all pipeline, ignoring the heterogeneous cognitive demands of different tasks.…

cs.CL2026

From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models

Junlong Tong, Zilong Wang, YuJie Ren +4

Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in dynamic, real-time scenarios. To…

cs.CL2026

CocoaBench: Evaluating Unified Digital Agents in the Wild

CocoaBench Team, Shibo Hao, Zhining Zhang +29

LLM agents now perform strongly in software engineering, deep research, GUI automation, and various other applications, while recent agent scaffolds and models are increasingly int…

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.CL2026

OMGs: A multi-agent system supporting MDT decision-making across the ovarian tumour care continuum

Yangyang Zhang, Zilong Wang, Jianbo Xu +24

Ovarian tumour management has increasingly relied on multidisciplinary tumour board (MDT) deliberation to address treatment complexity and disease heterogeneity. However, most pati…

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.SE2026

Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces

Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82

AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…

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.CV2026

GI-Bench: A Panoramic Benchmark Revealing the Knowledge-Experience Dissociation of Multimodal Large Language Models in Gastrointestinal Endoscopy Against Clinical Standards

Yan Zhu, Te Luo, Pei-Yao Fu +12

Multimodal Large Language Models (MLLMs) show promise in gastroenterology, yet their performance against comprehensive clinical workflows and human benchmarks remains unverified. T…