most citedLarge Action Models: From Inception to Implementation

2 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2025

MEETING DELEGATE: Benchmarking LLMs on Attending Meetings on Our Behalf

Lingxiang Hu, Shurun Yuan, Xiaoting Qin +5

In contemporary workplaces, meetings are essential for exchanging ideas and ensuring team alignment but often face challenges such as time consumption, scheduling conflicts, and in…

cs.SE20251 cited

Enabling Autonomic Microservice Management through Self-Learning Agents

Fenglin Yu, Fangkai Yang, Xiaoting Qin +8

The increasing complexity of modern software systems necessitates robust autonomic self-management capabilities. While Large Language Models (LLMs) demonstrate potential in this do…

cs.SE20251 cited

Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation

Xing Zhang, Jiaheng Wen, Fangkai Yang +11

The advancement of large language models has intensified the need to modernize enterprise applications and migrate legacy systems to secure, versatile languages. However, existing…

cs.CL2025

DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale

Linghao Zhang, Junhao Wang, Shilin He +13

Large Language Models have advanced automated software development, however, it remains a challenge to correctly infer dependencies, namely, identifying the internal components and…

cs.AI20252 cited

Large Action Models: From Inception to Implementation

Lu Wang, Fangkai Yang, Chaoyun Zhang +15

As AI continues to advance, there is a growing demand for systems that go beyond language-based assistance and move toward intelligent agents capable of performing real-world actio…

cs.CL20241 cited

EfficientRAG: Efficient Retriever for Multi-Hop Question Answering

Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng +7

Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries. While iterative retrieval methods improve performance…