most citedNextQuill: Causal Preference Modeling for Enhancing LLM Personalization

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cs.AI2026

Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective

Xiaoyan Zhao, Yujie Cai, Yang Zhang +2

Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to ground their outputs in extern…

cs.AI2026

PAFO: Pareto Fairness Optimization for Personalized Reward Modeling

Xiaoyan Zhao, Haoting Ni, Yang Zhang +3

Large language models (LLMs) increasingly rely on reward models to align their outputs with diverse user preferences. While personalized reward models aim to capture such heterogen…

cs.AI2026

Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology

Tianhao Shi, Yang Zhang, Xiaoyan Zhao +8

The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its beh…

cs.AI2026

NextMem: Towards Latent Factual Memory for LLM-based Agents

Zeyu Zhang, Rui Li, Xiaoyan Zhao +4

Memory is critical for LLM-based agents to preserve past observations for future decision-making, where factual memory serves as its foundational part. However, existing approaches…

cs.AI2025

Reinforced Latent Reasoning for LLM-based Recommendation

Yang Zhang, Wenxin Xu, Xiaoyan Zhao +4

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks, sparking growing interest in their application to preference reas…