The study examined how personalising language models affects user interactions over five days, comparing a non‑personalised baseline with memory‑based and survey‑based personalisation. Results showed that many interaction changes were due to repeated exposure, but personalisation influenced specific behaviors: memory‑based users disclosed more and found the model less creepy, while survey‑based users felt more regret about sharing personal data. The authors emphasize the nuanced, approach‑specific impacts on user attitudes and the need for careful design of personalised AI.
arXiv:2608.28833v1 Announce Type: new Abstract: While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providin...
The paper introduces ReaLMem, a benchmark built from authentic multi‑year personal visual archives with first‑person annotations, designed to evaluate AI systems on factual recall, persona inference, and predictive personalization. It also proposes ChronoProfiler, a temporal‑weighting module that calculates stability scores for user attributes to resolve preference conflicts and enhance personalized decision making. Experiments with multimodal large language models and memory systems show that predictive personalization remains the hardest task, highlight performance gaps, and demonstrate that temporally informed representations significantly improve personalization.
arXiv:2510. 09905v2 Announce Type: replace Abstract: When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive?
arXiv:2607. 27056v1 Announce Type: new Abstract: Personalized agents are increasingly applied to assist users across a wide range of tasks.
PRAGMA is a benchmark designed to evaluate personalized guidance in long‑term conversations. It includes curated longitudinal conversation histories, evidence annotations, and guidance scenarios that reflect evolving user contexts and incorrect assumptions. Experiments show that current retrieval, memory, and long‑context models struggle to recover relevant conversational evidence and to use it effectively for personalized guidance.
arXiv:2607. 14593v1 Announce Type: cross Abstract: As conversational AI systems are designed for repeated use, a central question is how a series of interactions becomes a relationship.
The paper examines how advisors should tailor recommendations when users consult personal AI assistants whose advice is predictable. It models the influence of personal AI through consultation probability and relative trust, finding that optimal counteraction and loss are hump‑shaped in these dimensions. The study also explores partial predictability, costly adjustments, richer information structures, and presents an online experiment showing participants weigh advisor, personal AI, and their own judgments differently.
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.
arXiv:2607. 26473v1 Announce Type: new Abstract: 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.
arXiv:2606. 06614v1 Announce Type: cross Abstract: Despite growing interest, most evaluations of large language models' (LLMs') personalization abilities have relied on synthetic data.
arXiv:2609.22204v1 Announce Type: new Abstract: This exploratory pilot study evaluates the scope and perceived accuracy of personal information output from ongoing conversational interactions in gene...
The study evaluates how well language‑model agents can simulate individual social media reactions by comparing predictions under different prompt conditions. Eight Serbian participants’ reactions to 68 posts were recorded, and four language models were asked to predict these reactions using prompts that varied in profile content and instruction style. The results show that prompts emphasizing attitudinal content and intuitive, immediate responses yield the highest fidelity, outperforming demographic backstories and a crowd baseline, and suggesting that such agents could act as general‑purpose simulated users.