7 papers
UIS-Digger: Towards Comprehensive Research Agent Systems for Real-world Unindexed Information Seeking
Chang Liu, Chuqiao Kuang, Tianyi Zhuang +4
Recent advancements in LLM-based information-seeking agents have achieved record-breaking performance on established benchmarks. However, these agents remain heavily reliant on sea…
Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents
Yihong Tang, Kehai Chen, Liang Yue +11
With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence.…
ELAIPBench: A Benchmark for Expert-Level Artificial Intelligence Paper Understanding
Xinbang Dai, Huikang Hu, Yongrui Chen +6
While large language models (LLMs) excel at many domain-specific tasks, their ability to deeply comprehend and reason about full-length academic papers remains underexplored. Exist…
Deep Research Agents: A Systematic Examination And Roadmap
Yuxuan Huang, Yihang Chen, Haozheng Zhang +10
The rapid progress of Large Language Models (LLMs) has given rise to a new category of autonomous AI systems, referred to as Deep Research (DR) agents. These agents are designed to…
DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learning
Wenxuan Shi, Haochen Tan, Chuqiao Kuang +7
Information seeking demands iterative evidence gathering and reflective reasoning, yet large language models (LLMs) still struggle with it in open-web question answering. Existing…
DocPuzzle: A Process-Aware Benchmark for Evaluating Realistic Long-Context Reasoning Capabilities
Tianyi Zhuang, Chuqiao Kuang, Xiaoguang Li +4
We present DocPuzzle, a rigorously constructed benchmark for evaluating long-context reasoning capabilities in large language models (LLMs). This benchmark comprises 100 expert-lev…