Personalization is essential for Large Language Model (LLM)-based agents to adapt to users' preferences and improve response quality and task performance. However, most existing approaches infer personas from chat histories, which capture only self-disclosed information rather than users' everyday behaviors in the physical world, limiting the ability to infer comprehensive user personas. In this work, we introduce SensorPersona, an LLM-empowered system that continuously infers stable user personas from multimodal longitudinal sensor streams unobtrusively collected from users' mobile devices. SensorPersona first performs person-oriented context encoding on continuous sensor streams to enrich the semantics of sensor contexts. It then employs hierarchical persona reasoning that integrates intra- and inter-episode reasoning to infer personas spanning physical patterns, psychosocial traits, and life experiences. Finally, it employs clustering-aware incremental verification and temporal evidence-aware updating to adapt to evolving personas. We evaluate SensorPersona on a self-collected dataset containing 1,580 hours of sensor data from 20 participants, collected over up to 3 months across 17 cities on 3 continents. Results show that SensorPersona achieves up to 31.4% higher recall in persona extraction, an 85.7% win rate in persona-aware agent responses, and notable improvements in user satisfaction compared to state-of-the-art baselines.
Figures & tables
Figure 1. Application scenario of SensorPersona.
Methods
Multi- Modal
Long- Term
Open- Ended
System Outputs
System Setting
Mem0 ( Chhikara et al., 2025 )
✘
✔
✔
Personas
Chatbot
M3-Agent ( Long et al., 2025 )
✔
✘
✔
Episodes
Camera
AutoLife ( Xu et al., 2025a )
✔
✘
✔
Episodes
Smartphone
ContextLLM ( Post et al., 2025 )
✔
✘
✔
Episodes
Wearables
MobileMiner ( Srinivasan et al., 2014 )
✔
✔
✘
Behaviors
Smartphone
SensorPersona
✔
✔
✔
Personas
Smartphone
Table 1. Comparison of SensorPersona with prior work.
Figure 2. Example personas extracted from the chat histories of two users using ChatGPT memory.
Figure 3. Performance of existing sensor context understanding approaches for persona inference.
Figure 4. Performance of existing approaches for persona inference from longitudinal sensor streams.
Figure 5. Evolving personas over time.
Figure 6. System overview of SensorPersona.
Figure 7. Semantic similarity heatmap of multimodal sensor contexts over time (left) and the corresponding similarity score distribution (right), suggesting that similar patterns frequently occur.
Figure 8. SensorPersona derives personas from sensor contexts via inter- and intra-episode reasoning.
Figure 9. Hierarchical persona maintenance in SensorPersona for adapting to evolving personas.
Category
Count
Details
Participants
20
10 male, 10 female.
Age
N.A.
18–63 years old (mean 31.4±12.6 ).
Cities
17
The cities 1 1 1 Specific city names in our self-collected dataset are anonymized throughout the paper for double-blind review. span three continents (Europe, North America, and Asia), with inter-city distances ranging from about 27 km to 12,955 km.
Duration
N.A.
3 months, totaling 1580 hours of multimodal sensor streams from smartphones.
iPhone (8–17 series), Pixel 7, Huawei Mate 40, OnePlus Ace 3 Pro, Xiaomi 12S Ultra, and vivo S17 Pro
Table 2. Detailed statistics of the self-collected dataset.
Figure 10. Overall performance of persona extraction.
Figure 11. Overall performance on the persona-aware agent response task. Blue, gray, and red denote win, tie, and loss against the baseline. “Chatbot” denotes personas derived from dialogue history.
Figure 12. Qualitative results of persona extraction. Only a subset of inferred personas is shown due to space limits. “Chatbot” refers to personas derived from the user’s conversation history with ChatGPT.
Figure 13. Qualitative results of agent response. The left and right examples show chatbot responses using memories from the ChatGPT-5.2 text box and personas inferred by SensorPersona, respectively.
Figure 14. Impact of context compression.
Figure 15. Impact of multi-dimensional personas. “w/o phy” and “w/o psy” represent removing physical and psychological personas, respectively.
Figure 16. Comparison of different base LLMs used in SensorPersona. Qw refers to Qwen, Claude to Claude Opus 4.6, and Gemini-3.1 to Gemini 3.1 Pro.
Figure 17. Impact of persona maintenance.
Figure 20
Figure 20. Token consumption and cost. Phy Ep and Psy Ep are physical and psychosocial episode construction, respectively. Cost is computed using GPT-4.1 pricing.
Figure 22
Figure 23. User study results of SensorPersona.
Figure 24. User study. Blue and red denote Precision scores, while grey is human ratings. Human ratings exceed Precision , suggesting that SensorPersona infers insightful personas users may not explicitly recognize.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 25. Persona extraction results.
Figure 26. Agent response results.
Figure 27. Comparison of the average Recall and F1 across all participants for SensorPersona, ContextLLM, and human reference rates for SensorPersona.
Figure 28. An example of persona clustering in SensorPersona, where personas with similar patterns are grouped together despite variations across different time periods (Phase ❶ and Phase ❷). Sensitive identifiers are anonymized.
Personalizing large language models (LLMs) to individual users is essential for improving user experience, yet existing approaches typically rely on explicit preference supervision such as pairwise comparisons or demographic attributes, limiting their applicability in natural interaction settings. We propose IRIS, a framework that learns dynamic user personas directly from implicit interaction streams by extracting behavioral signals from everyday conversations and iteratively refining persona representations through a prediction-driven closed loop without requiring explicit feedback. We introduce an evaluation protocol based on behavior prediction, persona stability, and decision prediction. A proof-of-concept study on a synthetic interaction stream derived from public-domain autobiographical text shows that IRIS produces stable personas and distinguishes individual users while revealing limitations of memory-only approaches on recall-oriented metrics. We then validate IRIS on anonymized real-world Reddit r/AmItheAsshole (AITA) data, with personas built solely from each author's historical interactions. Across 100 authors, IRIS achieves the highest decision prediction accuracy among all evaluated methods (61.0%), outperforming static personas, memory-only retrieval, and no-personalization baselines. These results suggest that implicit behavioral modeling provides a scalable alternative to explicit preference learning for personalized LLMs and offers a practical foundation for adaptive conversational systems and embodied agents that require continuously evolving models of their users.
Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality over time (see Fig.1). In this paper, we introduce PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization. It transforms a general-purpose MLLM into a personalized assistant by integrating three key capabilities: (a) Remembering: It proactively extracts and summarizes chronological multimodal memories from interactions, consolidating them into a personalized database. (b) Reasoning: It conducts multi-turn reasoning by retrieving and integrating relevant memories from the database. (c) Response Alignment: It infers the user's evolving personality throughout long-term interactions to ensure outputs remain aligned with their unique characteristics. For evaluation, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases, designed to assess long-term MLLM personalization across seven key aspects and 14 fine-grained tasks. Extensive experiments validate our method's effectiveness, improving the baseline by 22.4% (Persona-MME) and 9.8% (PERSONAMEM) under a 128k context, while outperforming GPT-4o by 5.2% and 2.0%, respectively. Project page: https://PersonaVLM.github.io.
Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogue? More comprehensive personalization demands a harder capability -- inferring what users care about from the multimodal traces they naturally leave behind. We introduce SocialPersona, a benchmark for evaluating whether multimodal large language models (MLLMs) can recover revealed preferences from longitudinal social-media timelines and use them in dialogue. Built from longitudinal timelines of 171 everyday, non-promotional social-media users, SocialPersona contains text, images, timestamps, and 2,597 human-verified preference tags across seven interest domains, separating stable interests from recent interests. It supports two tasks: constructing structured user profiles from multimodal context and generating responses aligned with inferred profiles. Experiments with proprietary and open-weight MLLMs show that models can identify broad interest domains, yet their performance drops on fine-grained and recent interests and degrades further when inferred profiles must be used to personalize dialogue. Together with evidence that text and images provide complementary preference signals, these results indicate that robust cross-modal, long-horizon user modeling remains a key challenge, and that SocialPersona can help measure and advance progress toward assistants that infer and act on revealed preferences.