Hypotheses-Guided Self Distillation for Continual Personalization

• URL: http://arxiv.org/abs/2609.00251v1
• Date: Mon, 31 Aug 2026 18:52:21 GMT
• Title: Hypotheses-Guided Self Distillation for Continual Personalization
• Abstract summary: We introduce HypReflect, a reliable, scalable framework for continual personalization.<n>HypReflect infers explicit, uncertainty-aware preference hypotheses from diverse user signals.<n>Results suggest a step towards reliable and scalable continual personalization.
• Score: 16.17908994206264
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As people increasingly interact with LLM assistants in daily life, continually adapting to individual preferences has become essential for effective long-term interactions. However, user preferences are rarely stated in full, and instead emerge through heterogeneous, latent, and noisy signals, with existing methods relying on raw interaction histories or costly reward-based optimization to manage personalization. We introduce HypReflect, a reliable, scalable framework for continual personalization that infers explicit, uncertainty-aware preference hypotheses from diverse user signals, reflectively refines them as new evidence accumulates, and incorporates the resulting user model through hypotheses-guided self-distillation. Experiments across three personalization settings: online personalization, multi-session interactions, and implicit behavioral signals, show that HypReflect outperforms a range of baselines, including raw-history and incremental-update methods. We further demonstrate strong generalization to unseen users and cross-domain settings, along with stability across context budgets, reusable hypotheses, and more focused personalization. These results suggest a step towards reliable and scalable continual personalization through explicit, revisable user preference hypotheses.

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