personalization

The paper demonstrates that prompt-space meta-learning for personalizing frozen large language models does not transfer across users, as the meta-validation objective is statistically invariant to user-support correspondence, leading to no significant improvement over seed prompts or controls.

A tweet highlights Audible's book suggestion feature, which recommends books based on a user's listening history in warfare and conquest.

FedRoRA is a novel framework for personalized federated LoRA fine-tuning that addresses rank heterogeneity and data heterogeneity in federated learning by decoupling adaptation into shared global directions and personalized magnitudes.

Amazon has launched a new AI feature for Alexa called 'Update Me When' that sends personalized notifications to alert users about potential shopping temptations, enhancing the shopping experience through anticipation of user needs.

This paper introduces PRISK, a framework for evaluating risks in LLM personalization, finding that personalized context increases irrelevant personalization, preference narrowing, and sycophantic bias across 13 models.

A user shares a detailed prompt they used to train an AI to mimic their personal writing style, focusing on final-pass editing for communications like emails and social media.

A former OpenAI posttraining researcher announces their new focus on building sovereign, user-owned AI models that adapt to individual goals and data, emphasizing privacy, control, and long-term personalization.

The paper proposes FlatLand, a personalized federated learning method that uses tailored Lorentz space in hyperbolic geometry to handle heterogeneous graph structures among clients, improving performance in privacy-preserving collaborative training.

The article discusses how LLMs are enabling a new era of extensible software on the web, allowing users to create personalized applications and address long-tail needs through concepts like 'Small Software' and 'LLM-native software'.

This paper evaluates the use of self-supervised learning on PPG data for real-life emotion detection, finding that general representations fail without individual personalization.

The paper introduces a framework for personalized auto-research systems that condition every stage of the research process on individual scientist representations, arguing that personalization is essential for AI to serve as true co-scientists rather than generic instruments.

This article explains test-time training, where AI models adapt during inference to improve personalization and reduce memory usage, but at the cost of increased per-user compute. It discusses implications for serving models at scale, balancing long context and user concurrency.

GBrain now supports personalized agent generation for Codex and Claude Code, including creating a SOUL.md file and installing 70 agent skills to quickly set up an AI agent.

ProblyHQ is developing a personalization layer for prediction markets that adapts feeds to user interests, focusing on real-time markets across various domains like crypto and sports.

Palisade introduces hyper-personalized AI sales agents for online marketplaces, providing tailored interactions for buyers, sellers, and teams to enhance engagement and conversions.

OpenAI announces that ChatGPT can now remember user activity across apps and websites via Computer History in the desktop app, enabling more personalized interactions with less explanation.

This paper introduces Weightless Fine-Tuning (WFT), a training-free decoding-time method that approximates supervised fine-tuning effects via logit-space transport, achieving competitive personalization performance with less than 7% of the computation.

This paper introduces EDPFRL-IM, a framework that integrates curiosity-driven intrinsic motivation into personalized federated reinforcement learning to improve exploration in sparse-reward, non-stationary environments while preserving client privacy.

UserToolBench is a new benchmark for evaluating personalized decision-making in tool-use LLMs, testing whether models can infer latent user preferences, decide when to clarify, and produce user-aligned tool-call trajectories under incomplete information. Experiments show current models struggle with multi-tool coordination and long-horizon consistency.

This paper introduces LUNAR, a benchmark for evaluating how large language models personalize responses from longitudinal app interaction histories across daily-life domains such as clothing, food, housing, and mobility. Experiments on 19 mainstream LLMs reveal that effective personalization depends on evidence selection and cross-domain integration, and that stronger personalization can come at the cost of privacy protection.