arXiv：cs.AI· Kounianhua Du, Jianxing Liu, Kangning Zhang, Wenxiang Jiao, Yuan Lu, Jiarui Jin, Jianghao Lin, Weiwen Liu, Yong Yu, Weinan Zhang·· 10 小时前
FleetPilot：细粒度引导实现真实场景下的 LLM 个性化
FleetPilot: Fine-Grained Steering Empowers Real-World LLM Personalization
AI 导读
FleetPilot 是一个细粒度、按实例定制的 LLM 引导框架，能从用户数据动态生成样本级干预向量并注入模型前向过程，实现个性化适配。它由 match contrast、细粒度引导组件和查询条件聚合模块组成，在短到长文本生成及 web 函数调用等场景中均带来显著性能提升，在快速变化和数据稀疏环境下优势尤为突出。该方法无需训练，仅带来少量存储或推理延迟开销，实现已开源。
正文
Abstract:With the rapid evolution of large language models (LLMs), there is an increasing demand for effective personalization techniques that can adapt model behavior to individual user preferences. Personalized LLMs must infer a user's query-dependent preferences from limited, heterogeneous, and evolving interaction histories. Existing approaches rely primarily on in-context personalization, parametric adaptation, or personalized reward modeling, but respectively incur recurring costs in context management, model optimization and deployment, or multi-candidate generation and scoring. These limitations become particularly restrictive when user observations are sparse and the preference evidence relevant to each query varies across contexts and over time. To address these challenges, we propose \modelname, a fine-grained and instance-tailored steering framework that dynamically generates sample-level interference vectors from user data and injects them into the model's forward pass for personalized adaptation. Our approach introduces: match contrast that produces user-relevant signals, fine-grained steering component that captures nuanced signals by hooking activations from attention and MLP layers, and query-conditioned aggregation module that synthesizes these signals into contextually relevant enhancements. Extensive experiments across diverse scenarios, including short-to-long text generation, and web function calling, validate the effectiveness and compatibility of \modelname. Results show that our method consistently brings significant performance gains, and show extreme superiority in fast-shifting and data-sparse environments. Additionally, \modelname rids us of training cycles and incurs light overhead of storage or inference latency. These make \modelname a strong and real-world deployable method. Implementation is available \footnote{this https URL}.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.14970 [cs.AI] |
| (or arXiv:2610.14970v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.14970 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Kounianhua Du [view email]
[v1]
Sat, 10 Oct 2026 14:44:50 UTC (787 KB)
来源：arXiv：cs.AI · arxiv.org