arXiv Machine Learning September 3, 2026 By Yihang Sun, Encheng Xie, Tao Feng, Jiaxuan You

GMTRouter: Personalized LLM Router over Multi-turn User Interactions

Read the original on arXiv Machine Learning →

GMTRouter is a personalized large language model router that represents multi‑turn user‑LLM interactions as a heterogeneous graph with five node types—user, LLM, query, response, and turn—to preserve relational structure. Using a lightweight inductive graph learning framework and a user‑conditioned graph sampling mechanism, it captures user preferences from few‑shot data, enabling effective personalization without extensive fine‑tuning. Experiments show GMTRouter outperforms strong baselines, improving accuracy by up to 0.108 and AUC by 0.124, and adapts to new users with minimal data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 5

Learning to Route LLMs from Implicit Cost-Performance Preferences via Meta-Learning

arXiv:2606. 06178v1 Announce Type: new Abstract: Large language models (LLMs) present a trade-off between performance and cost, where more powerful models incur greater expense.

By Jiahao Zeng, Ming Tang, Ningning Ding

llms reinforcement-learning

arXiv AI

Aug 18

LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking

arXiv:2506. 07449v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLMs) have driven their adoption in recommender systems through Retrieval-Augmented Generation (RAG) frameworks.

By Vahid Azizi, Fatemeh Koochaki

llms rag fine-tuning

arXiv Machine Learning

Jun 30

Synthetic Interaction Data for Scalable Personalization in Large Language Models

arXiv:2602. 12394v2 Announce Type: replace Abstract: Personalized prompting offers large opportunities for deploying large language models (LLMs) to diverse users, yet existing prompt optimization methods primarily focus on task-level optimization while largely overlooking user-specific preferences and latent constraints of individual users.

By Yuchen Ma, Yue Huang, Wenjie Wang, Xiaonan Luo, Xiangliang Zhang, Stefan Feuerriegel

llms agents reinforcement-learning benchmarks safety

arXiv AI

Jul 29

REPREC: Representation Driven Parameter-Efficient Recommendation System

arXiv:2607. 24845v1 Announce Type: cross Abstract: Large language models (LLMs) have been applied to sequential recommendation by formulating it as a natural language task.

By Harshini Kavuru, Dwipam Katariya, Giri Iyengar, Pranab Mohanty, Kalanand Mishra, Kalanand Mishra

llms rag fine-tuning efficiency benchmarks safety

arXiv Computation and Language

Sep 1

Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey

arXiv:2603.04445v3 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent mo...

By Yasmin Moslem, John D. Kelleher

llms reinforcement-learning

arXiv Computation and Language

Sep 3

Beyond Retrieval: Learning Compact User Representations for Scalable LLM Personalization

The paper introduces TAP-PER, a prefix‑based framework that learns compact user representations for large language model personalization. By encoding user preferences into lightweight prefix embeddings and incorporating temporal signals, TAP‑PER avoids the need for heavy per‑user adapters or prompt‑serialized histories. Experiments on six LaMP tasks show that TAP‑PER outperforms both prompt‑based and model‑based baselines while using far fewer per‑user parameters, enabling scalable personalization at large user scales.

By Heng Cao, Fan Zhang, Jian Yao, Yujie Zheng, Changlin Zhao, Lu Hao, Yuxuan Wei, Wangze Ni, Huaiyu Fu, Yuqian Sun, Xuyan Mo

llms rag