most citedFrom Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space

1 citations · 1 across the 7 of their papers we have counts for

collaborators

19 papers

cs.IR2026

R-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search

Sheng Zhang, Junyi Li, Wenlin Zhang +6

Recent search agents for multi-hop reasoning often fail by either retrieving incomplete evidence or reasoning over irrelevant portions of the retrieved content, leading to a retrie…

cs.CL2026

Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization

Junyi Li, Xiaowei Qian, Yingyi Zhang +6

Recent advances in tool-integrated language agents have significantly improved their ability to solve complex reasoning tasks. However, existing alignment methods predominantly foc…

cs.IR2026

RAGR: Review-Augmented Generative Recommendation

Yingyi Zhang, Junyi Li, Yejing Wang +8

Sequential recommendation (SR) is traditionally formulated as next-item prediction over chronological item interactions. Although recent generative recommendation (GR) methods intr…

cs.IR2026

MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search

Sheng Zhang, Junyi Li, Yingyi Zhang +7

Recent advances in large language models (LLMs) have scaled the potential for reasoning and agentic search, wherein models autonomously plan, retrieve, and reason over external kno…

cs.IR2026

Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery

Xiaopeng Li, Wenlin Zhang, Yingyi Zhang +6

Deep Research agents driven by LLMs have automated the scholarly discovery pipeline, from planning and query formulation to iterative web exploration. Yet they remain constrained b…

cs.CL2026

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory

Derong Xu, Shuochen Liu, Pengfei Luo +8

Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving preferences over long interacti…