1 citations · 1 across the 7 of their papers we have counts for
7 papers
Joint Agent Memory and Exploration Learning via Novelty Signals
Shizuo Tian, Xiaohong Weng, Rui Kong +9
In open-ended environments, exploration is fundamental for autonomous agents, yet current language model agents struggle with this. Effective exploration requires memory, but retai…
Large Language Model-Powered Query-Driven Event Timeline Summarization in Industrial Search
Mingyue Wang, Xingyu Xie, Hang Yang +5
Understanding how events evolve over time is essential for search engines handling queries about trending news. We present QDET (Query-Driven Event Timeline Summarization), a produ…
Beyond ReAct: A Planner-Centric Framework for Complex Tool-Augmented LLM Reasoning
Xiaolong Wei, Yuehu Dong, Xingliang Wang +5
Existing tool-augmented large language models (LLMs) encounter significant challenges when processing complex queries. Current frameworks such as ReAct are prone to local optimizat…
TURA: Tool-Augmented Unified Retrieval Agent for AI Search
Zhejun Zhao, Yuchen Li, Alley Liu +8
The advent of Large Language Models (LLMs) is transforming search engines into conversational AI search products, primarily using Retrieval-Augmented Generation (RAG) on web corpor…
Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong +20
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-maki…
GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval
Wonduk Seo, Haojie Zhang, Yueyang Zhang +6
Query reformulation is a well-known problem in Information Retrieval (IR) aimed at enhancing single search successful completion rate by automatically modifying user's input query.…
TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy
Yiqun Chen, Qi Liu, Yi Zhang +6
Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs f…