Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation

arXiv:2610.02353 · cs.LG, cs.CL · Submitted 2026-10-01 · Read on arXiv

cs.LG, cs.CL

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Adaptation — 1× in this paper · explained in 1 episode
• Decoupling — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes

Sources

• LoRA: Low-Rank Adaptation of Large Language Models
• Adam: A Method for Stochastic Optimization
• LongLaMP: A Benchmark for Personalized Long-form Text Generation
• A Survey of Personalized Large Language Models: Progress and Future Directions
• CoSteer: Collaborative Decoding-Time Personalization via Local Delta Steering
• Integrating Summarization and Retrieval for Enhanced Personalization via Large Language Models
• Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
• PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks