Author

Chengbing Wang

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#natural language process... Preprint Jun 2026

Towards Root Memory: Benchmarking and Enhancing Implicit Logical Memory Retrieval for Personalized LLMs

Memory systems are essential for personalized Large Language Models (LLMs). However, retrieval within these systems primarily relies on semantic similarity, potentially missing logically critical memories with limited semantic overlap. We refer to this failure as semantic deflection, which remains insufficiently evalua...

Hongxun Ding, Xiang-Yu Yu, Cheng-Bing Wang et al. · 1 citation

#natural language process... Preprint Sep 2026

Rubric-Aware On-Policy Self-Distillation for LLM Personalization

GRASP is a rubric-aware on-policy self-distillation framework for LLM personalization that turns user-specific rubric aspects into fine-grained, token-level supervision, and Rubric-based Teacher Validation (RTV) is introduced, which retains only instances where the teacher sufficiently covers the target aspects, improv...

Yilun Qiu, Xiao-Yan Zhao, Cheng-Bing Wang et al. · 0 citations

Jul 2026

PrefReward: Learning User Preference Matrix for Personalized Text Generation

Experiments on the LongLaMP dataset show that PrefReward outperforms non-personalized and retrieval-based baselines in both generation quality and personalization interpretability.

Yue Wu, Chengbing Wang, Yimeng Bai et al. · arXiv.org · 0 citations