Topic
personalization
New Training-Free Method Enables Robots to Follow Personalized Commands Like 'Bring My Cup'
Researchers propose Visual Attentive Prompting (VAP), a training-free perceptual adapter that enables vision-language-action models to follow personalized commands by using reference images as visual prompts. VAP outperforms generic policies and token-learning baselines on simulation and real-world benchmarks.
Technology AI-Driven Personalization Now Drives 75% of Meesho Orders, Signals Shift in Consumer Commerce
At the ETRetail E-Commerce and Digital Natives Summit 2026, Meesho's Goyal revealed that over 75% of orders now come from AI-powered personalized feeds. He outlined how AI is shifting commerce from search-led discovery to an intent-first model, with benefits across the value chain including reduced customer acquisition costs and improved inventory planning.
G2Rec Framework Structures and Tokenizes User Interests for Generative Recommendation
The G2Rec framework, proposed by researchers, addresses limitations in generative recommendation by unifying holistic graph-based user co-engagement modeling with semantic tokenization. It enables scalable, accurate user interest modeling without requiring ground-truth interests, and has demonstrated superiority through online deployment and experiments on public datasets.
Token Factory: Efficiently Integrating Diverse Signals into Large Recommendation Models
Token Factory is a framework that converts diverse traditional signals into soft tokens for large recommendation models (LRMs), addressing challenges of long prompts, memory footprint, and computational overhead. The approach has been validated in a production-scale environment, promising enhanced performance and efficiency.
CLoVE: New Federated Learning Algorithm Clusters Loss Vectors for Personalization
Researchers propose CLoVE (Clustering of Loss Vector Embeddings), a novel clustered federated learning algorithm that groups clients based on loss patterns. It achieves high cluster recovery in few rounds and state-of-the-art accuracy across supervised and unsupervised tasks.
User as Code: Executable Memory Paradigm Boosts AI Agent Personalization with Deterministic Execution
A new arXiv preprint introduces User as Code (UaC), a paradigm where AI agent user memory is stored as executable Python code rather than unstructured text. On benchmarks, UaC achieves 78.8% recall on LOCOMO and 99% accuracy on aggregate questions, while enabling unsolicited safety alerts that retrieval-based systems cannot provide.
ChatPlanner: LLM Framework Personalizes Public Transit Routing with Fine-Tuning and RAG
Researchers present ChatPlanner, a novel framework that leverages fine-tuned Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to capture diverse user preferences for public transit routing. The system extracts routing parameters from natural language queries, integrates preferences into the routing algorithm, and generates feasible, personalized alternatives. Three experiments show that the combined fine-tuning and RAG approach achieves highest accuracy and uncovers valuable solutions overlooked by existing route planners.
Technology Instagram expands algorithm personalization, but not for accounts you follow
Instagram has expanded its algorithm personalization feature to the main feed, allowing users to control which topics they see more or less of. However, the feature does not support requests to see more posts from accounts the user follows, a limitation that frustrates creators and businesses. Instagram chief Adam Mosseri explained that the change is powered by large language models and aims to give users more agency, but acknowledged that the decline of the 'following' feed was a consequence of shifting user behavior.