PILA'26: Personal Intelligence in Agentic AI Era

Abstract

Large language models (LLMs) and agentic AI systems are rapidly moving into user-facing applications, yet most remain fundamentally generic, optimized for population-level objectives under the assumption that one model can serve all users. This assumption is increasingly misaligned with real-world deployment, where AI systems interact continuously with individuals whose preferences, knowledge, goals, and values evolve over time. PILA'26 is motivated by the need to move beyond static general models toward personal intelligence ---AI systems that explicitly model users and dynamically adapt their reasoning, behavior, and decisions through memory, interaction, and lifelong learning. The workshop brings together researchers and practitioners from data mining, LLMs, NLP, IR, human-centered AI, and AI safety to position personalization as a central research direction for next-generation AI systems at KDD. Topics include user memory and personalized alignment, self-evolving and lifelong learning, datasets and evaluation, real-world applications, and trustworthiness in user-adaptive AI. Workshop website: https://pila26-workshop.github.io.