personalization

MoEngage acquires Aampe to bring AI agents that personalize marketing for each customer, moving beyond traditional segment-based campaigns. The deal aims to help MoEngage win enterprise customers from rivals like Salesforce and Adobe.

AOHP is an Android-based open-source OS framework that treats AI agents as first-class entities, improving task completion by 21.12% and reducing token costs by 51.55% through agent-oriented mechanisms like personalized service composition and secure information flow.

Proposes User as Engram, a method to store per-user memory as sparse local parametric edits in a hash-keyed memory table, inspired by hippocampal engrams, achieving better reasoning accuracy and memory efficiency compared to per-user LoRA.

The author critiques the idea of agents remembering everything and introduces TrueMemory, a system that converts memories into trait claims with confidence and evidence to better calibrate agent behavior.

This paper introduces MemSlides, a hierarchical memory framework for personalized slide generation that separates long-term user profiles, working memory for session constraints, and tool memory for localized edits, enabling multi-turn revision without full regeneration.

ChatPlanner is a novel framework that uses fine-tuned LLMs with Retrieval-Augmented Generation (RAG) to interpret user preferences from natural language queries and integrate them into public transit routing algorithms, outperforming existing route planners.

Proposes FedSPC, a modular correction method for personalized federated learning that applies control-variate correction only to shared parameters, improving performance across various PFL methods on CIFAR-100 and Tiny-ImageNet.

Preply integrates OpenAI's API into its language learning platform to create Lesson Insights, which automatically generates personalized feedback and homework from lesson transcripts, enhancing the tutor-learner experience.

The tweet suggests using Hermes Agent to convert an article into a personalized setup by pasting it into the session and issuing commands to build a SOUL.md, create profiles, and write a goal command.

This paper introduces a diagnostic framework for user-side memory in LLMs, showing that personalization factors into behavioral consistency, factual presence, and factual absence. It demonstrates that no single method (e.g., LoRA vs RAG) excels at all three axes, and highlights an alignment tax on parametric user memory.

New research from Writer shows that memory tools designed to personalize AI models can actually degrade accuracy by introducing sycophancy and bias, as the model becomes more likely to agree with user errors or irrelevant preferences.

This paper proposes PAFO, a Pareto fairness optimization framework to mitigate personalized reward bias in reward models for LLMs, improving accuracy for minority user groups without harming majority groups.

Apple announced a major Siri overhaul at WWDC 2026, including a partnership with Google Gemini, a standalone Siri app, and the ability to leverage personal data from users' devices, aiming to make the assistant more helpful and action-oriented.

Introduces iOSWorld, an interactive native iOS simulator benchmark with persistent user identity across 26 apps, designed to evaluate personalized mobile agent capabilities through 133 tasks of increasing difficulty.

Explores the potential of AI agents to take over marketing decisions like audience selection and personalization, questioning whether marketers should hand over control to AI.

A mental health professional argues that AI, when properly prompted, can offer surprisingly effective therapeutic advice and personalization, sometimes surpassing traditional therapy in nuance and accessibility, especially for neurodivergent individuals.

Introduces BUMP, a self-supervised framework for training a profile generator for LLM personalization without task labels, using bidirectional in-batch ranking and GRPO. It matches or outperforms supervised methods on the LaMP benchmark.

OpenAI is rolling out 'Dreaming,' a new background memory synthesis system for ChatGPT that automatically curates and updates memories from chat history, addressing staleness, correctness, and scalability issues. The update is initially available to Plus and Pro users in the US, with broader rollout planned.

LifeSide is a new benchmark for evaluating AI agents as lifelong digital companions, testing memory tracking, user understanding, privacy control, and emotional companionship across 2,000 personas and 111K tasks in multi-session settings. Results show that even top models fail to sustain accurate user understanding and genuine companionship over long horizons.

SaliMory is a framework that trains a single language model to manage cognitively-structured memory (user facts, preferences, and working memory) for conversational agents, using hierarchical stage-wise process rewards and reward-decomposed contrastive refinement. It reduces memory-attributed failures by one-third, outperforms state-of-the-art by over 10% in end-to-end accuracy, and more than doubles the Good Personalization rate.