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EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis
Xiaoshuai Song, Haofei Chang, Guanting Dong +3
Large language models (LLMs) are expected to be trained to act as agents in various real-world environments, but this process relies on rich and varied tool-interaction sandboxes.…
GISA: A Benchmark for General Information-Seeking Assistant
Yutao Zhu, Xingshuo Zhang, Maosen Zhang +9
The advancement of large language models (LLMs) has significantly accelerated the development of search agents capable of autonomously gathering information through multi-turn web…
Memory in the Age of AI Agents
Yuyang Hu, Shichun Liu, Yanwei Yue +44
Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attentio…
Large Language Models for Information Retrieval: A Survey
Yutao Zhu, Huaying Yuan, Shuting Wang +7
As a primary means of information acquisition, information retrieval (IR) systems, such as search engines, have integrated themselves into our daily lives. These systems also serve…
Neuro-Symbolic Query Compiler
Yuyao Zhang, Zhicheng Dou, Xiaoxi Li +5
Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with…
OmniEval: An Omnidirectional and Automatic RAG Evaluation Benchmark in Financial Domain
Shuting Wang, Jiejun Tan, Zhicheng Dou +1
As a typical and practical application of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) techniques have gained extensive attention, particularly in vertical do…