@inproceedings{shen-etal-2026-hypertrace,
title = "{H}yper{T}race: Hypothesis-Based Preference Tracing for Online {LLM} Personalization",
author = "Shen, Jianzhi and
Mao, Keyu and
Shao, Minghao and
Jin, Chuanyang and
Wang, Yusong and
Lin, Ailiang and
Funakoshi, Kotaro and
Okumura, Manabu and
Shafique, Muhammad",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1038/",
doi = "10.18653/2026.findings-emnlp.1038",
pages = "21936--21951",
ISBN = "979-8-89176-462-0",
abstract = "Personalized language models aim to adapt responses to individual users, whose preferences are often latent and revealed gradually through interaction. Existing training-free methods rely on stored histories or retrieved memories, but they often struggle to reconcile long-term preferences with short-term topic-specific needs. To address this issue, we propose HyperTrace, a training-free framework that formulates online personalization as latent preference tracing. HyperTrace maintains interpretable natural-language hypotheses over short-term intent and long-term preferences, and updates them through an SMC-style reweight process using an LLM-based surrogate choice model. By updating these hypotheses across turns and sessions, HyperTrace enables personalization without parameter updates. Experiments on PRISM and PersonaMem-v2 show that HyperTrace improves response alignment, preference prediction, and profile consistency over strong online baselines, demonstrating the effectiveness of tracing latent user preferences for robust personalization. Code and scripts are available in the repository: [https://github.com/jiseshen/HyperTrace](https://github.com/jiseshen/HyperTrace)."
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<abstract>Personalized language models aim to adapt responses to individual users, whose preferences are often latent and revealed gradually through interaction. Existing training-free methods rely on stored histories or retrieved memories, but they often struggle to reconcile long-term preferences with short-term topic-specific needs. To address this issue, we propose HyperTrace, a training-free framework that formulates online personalization as latent preference tracing. HyperTrace maintains interpretable natural-language hypotheses over short-term intent and long-term preferences, and updates them through an SMC-style reweight process using an LLM-based surrogate choice model. By updating these hypotheses across turns and sessions, HyperTrace enables personalization without parameter updates. Experiments on PRISM and PersonaMem-v2 show that HyperTrace improves response alignment, preference prediction, and profile consistency over strong online baselines, demonstrating the effectiveness of tracing latent user preferences for robust personalization. Code and scripts are available in the repository: [https://github.com/jiseshen/HyperTrace](https://github.com/jiseshen/HyperTrace).</abstract>
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%0 Conference Proceedings
%T HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization
%A Shen, Jianzhi
%A Mao, Keyu
%A Shao, Minghao
%A Jin, Chuanyang
%A Wang, Yusong
%A Lin, Ailiang
%A Funakoshi, Kotaro
%A Okumura, Manabu
%A Shafique, Muhammad
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F shen-etal-2026-hypertrace
%X Personalized language models aim to adapt responses to individual users, whose preferences are often latent and revealed gradually through interaction. Existing training-free methods rely on stored histories or retrieved memories, but they often struggle to reconcile long-term preferences with short-term topic-specific needs. To address this issue, we propose HyperTrace, a training-free framework that formulates online personalization as latent preference tracing. HyperTrace maintains interpretable natural-language hypotheses over short-term intent and long-term preferences, and updates them through an SMC-style reweight process using an LLM-based surrogate choice model. By updating these hypotheses across turns and sessions, HyperTrace enables personalization without parameter updates. Experiments on PRISM and PersonaMem-v2 show that HyperTrace improves response alignment, preference prediction, and profile consistency over strong online baselines, demonstrating the effectiveness of tracing latent user preferences for robust personalization. Code and scripts are available in the repository: [https://github.com/jiseshen/HyperTrace](https://github.com/jiseshen/HyperTrace).
%R 10.18653/2026.findings-emnlp.1038
%U https://aclanthology.org/2026.findings-emnlp.1038/
%U https://doi.org/10.18653/2026.findings-emnlp.1038
%P 21936-21951
Markdown (Informal)
[HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization](https://aclanthology.org/2026.findings-emnlp.1038/) (Shen et al., Findings 2026)
ACL
- Jianzhi Shen, Keyu Mao, Minghao Shao, Chuanyang Jin, Yusong Wang, Ailiang Lin, Kotaro Funakoshi, Manabu Okumura, and Muhammad Shafique. 2026. HyperTrace: Hypothesis-Based Preference Tracing for Online LLM Personalization. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 21936–21951, Budapest, Hungary. Association for Computational Linguistics.