activity
20242026
most citedHelloBench: Evaluating Long Text Generation Capabilities of Large Language Models

2 citations · 2 across the 14 of their papers we have counts for

collaborators

23 papers

cs.AI2026

CLI-Universe: Towards Verifiable Task Synthesis Engine for Terminal Agents

Zhanbo Hua, Yifan Yao, Weihao Xie +14

While recent LLM-based terminal agents have demonstrated promising capabilities, the scarcity of high-quality, executable training data remains a critical bottleneck. Existing synt…

cs.SE2026

WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models

Xinping Lei, Xinyu Che, Junqi Xiong +16

Large language models are rapidly evolving into interactive coding agents capable of end-to-end web coding, yet existing benchmarks evaluate only narrow slices of this capability,…

cs.SE2026

CodeTracer: Towards Traceable Agent States

Han Li, Yifan Yao, Letian Zhu +13

Code agents are advancing rapidly, but debugging them is becoming increasingly difficult. As frameworks orchestrate parallel tool calls and multi-stage workflows over complex tasks…

cs.CL2026

NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents

Jingzhe Ding, Shengda Long, Changxin Pu +46

Recent advances in coding agents suggest rapid progress toward autonomous software development, yet existing benchmarks fail to rigorously evaluate the long-horizon capabilities re…

cs.CL2026

Encyclo-K: Evaluating LLMs with Dynamically Composed Knowledge Statements

Yiming Liang, Yizhi Li, Yantao Du +14

Benchmarks play a crucial role in tracking the rapid advancement of large language models (LLMs) and identifying their capability boundaries. However, existing benchmarks predomina…

cs.CL2025

AI4Reading: Chinese Audiobook Interpretation System Based on Multi-Agent Collaboration

Minjiang Huang, Jipeng Qiang, Yi Zhu +3

Audiobook interpretations are attracting increasing attention, as they provide accessible and in-depth analyses of books that offer readers practical insights and intellectual insp…