arXiv AI By Xinyu Lin, Zhuosong Jiang, Zixiao Suo, Siqin Wang, Hanqing Zeng, Hanchao Yu, Yinglong Xia, Jiang Zhang, Aashu Singh, Fei Liu, Wenjie Wang, Fuli Feng, Yang Song, Qifan Wang, Tat-Seng Chua

Self-Evolving Memory for Generative Recommendation

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arXiv AI
Sep 30

ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents

ReMem is a new recommendation agent framework that rethinks perception and memory for long-context recommendation tasks. It replaces raw HTML parsing with OCR-based multimodal perception from screenshots, extracting structured information in a platform-agnostic way. The framework also introduces a chunk-wise sequential memory update strategy and a multi-memory GRPO variant to efficiently model evolving user preferences over arbitrarily long interaction histories, achieving a 5.16% average improvement over state-of-the-art baselines on three recommendation agent tasks.

By Haohao Qu, Yongcheng Jing, Chun Hin Chan, Shanru Lin, Wenqi Fan, Dacheng Tao
arXiv Machine Learning
Oct 8

Self-Consuming Generative Models with Co-Evolving Human Preferences

The paper examines generative models that are trained through self‑consuming loops where users curate preferred samples. It shows that when training relies solely on user‑curated synthetic data, initial biases are amplified, leading the system to converge to a single dominant outcome. Introducing reference data at a sufficient rate changes the dynamics, producing a unique globally attracting equilibrium, and the authors propose an algorithm to select reference distributions and mixing weights to steer the system toward desirable equilibria while reducing data collection costs.

By Xiukun Wei, Tian Xie, Ding Zhu, Xueru Zhang