Wenlin Zhang

Beyond Retrieval: Evidence Access and Event Aggregation in Agent Memory

Memory Type Varies: Empowering LLM Agents for Long-Term Memory with Diverse Strategies

This work proposes a novel memory framework, named MemoType, which can adaptively recognize each memory and query type with the learned router model, and proposes a novel retrieval framework, named MemoType, which can adaptively recognize each memory and query type with the learned router model.

EvoSkillRec: Skill-Genome Evolution for Recommender Architecture Discovery

Modern recommender systems advance not only by scaling data and parameters, but also by encoding task-specific inductive biases through architecture, including sparse feature interactions for click-through rate (CTR) prediction, temporal attention for sequential recommendation, and expert routing for multi-task learnin...

Persona Following Is Not Selective Control: The Neutrality Gap in LLM User Simulation

A systematic failure of selective control is identified and a disparity, the neutrality gap, demonstrates that successful persona following does not imply selective persona control, which additionally requires keeping non-target attributes stable.

Tutorial on Generative Recommendation: Foundations and Frontiers

This work comprehensively surveys recent generative recommendation advances through a tri-decoupled perspective, summarize the evolution of tokenization strategies, analyze the trade-offs of major generative architectures, and summarize the transition from supervised next-token prediction to reinforcement-learning-base...

Tutorial on Generative Recommendation: Foundations and Frontiers

In the current digital ecosystem, recommender systems serve as the core infrastructure for navigating large-scale content catalogs and delivering personalized services, typically following multi-stage discriminative pipelines (e.g., retrieval, ranking, and re-ranking). However, their fragmented architectures cause casc...