personalization

A reflection on the challenges AI agents face in personalizing based on user data, emphasizing the need for consented, scoped access rather than broad memory.

This paper proposes W-Switch and W-Composite, two training-free methods for combining multiple LoRA modules in text-to-image generation using prompt-aware importance weighting to reduce concept interference. The approach is evaluated on the ComposLoRA testbed and shows consistent improvements over state-of-the-art methods in visual quality and identity preservation.

Introduces Ψ-Bench, a benchmark for evaluating LLMs' ability to influence users through persuasive dialogues with personalized profiles. Tests 10 frontier LLMs and finds significant room for improvement, with profile access boosting performance by 18.24%.

An analysis of LLM memory limitations, arguing that true personal AI requires single-tenant weight customization which conflicts with current multi-tenant cloud economics, and highlighting open-weight models as the likely source of progress.

This paper introduces representational accuracy and a Behavioral Specification as an interpretive layer for AI personalization, showing that it improves representational accuracy at about 25× less context cost compared to raw data retrieval, especially for interpretation-required questions.

This paper proposes Polar, a multimodal memory-augmented framework for personalizing embodied MLLM agents over long-term user interactions, using a knowledge graph and episodic memory to ground user-intended instances from accumulated context.

BobCA is a sovereign AI agent that learns to code according to user preferences, available on Product Hunt.

This paper proposes PUMA, a framework for LLM personalization in multi-turn conversations that models latent user states and uses the Free Energy Principle to select dialogue actions, improving long-horizon outcomes on healthcare counseling benchmarks.

This paper proposes a unified framework for personalized agentic reinforcement learning that decouples generic task rewards from personalized preference rewards, introducing PARPO and PSGM for preference-aligned policy optimization and skill retrieval.

This paper introduces PerMemBench, the first benchmark for evaluating personalized memory systems in LLM-based agents, and proposes a session-level storage gating framework that adapts memory policies to individual user contexts.

Supafax is an email-native assistant that learns how you work to improve productivity.

Spotify is rolling out AI-powered features that let users generate personalized podcasts from prompts, schedule daily or weekly briefs, and ask questions about podcast episodes via a new Q&A feature for Premium users.

This paper introduces Spectral Souping, a framework for efficiently aligning LLMs with individual user preferences by discovering a universal spectral representation that enables merging of specialized policies at inference time without costly retraining.

Explores whether AI agents can learn from rejected recommendations without compromising user privacy or becoming overly personalized to unique past behaviors.

Google I/O 2026 reveals a vision where the search box evolves into an omnipresent AI assistant that can perform tasks, generate content, and personalize experiences across Google's ecosystem, including Search, Gemini, Workspace, and YouTube.

ThoughtTrace introduces a large-scale dataset pairing real-world multi-turn human-AI conversations with users' self-reported thoughts, enabling improved user behavior prediction and personalized assistant training through thought-guided rewrites.

This paper proposes a paradigm shift in context engineering, formulating it as a recommendation problem. The authors introduce Neural Collaborative Context Engineering (NCCE), which uses collaborative filtering to dynamically assign instance-specific contexts, improving LLM task accuracy.

Introduces Capability Conditioned Scaffolding, a framework for LLM collaboration that adapts intervention based on user expertise domains to prevent Professional Domain Drift, with pilot evaluation on MMLU subsets.

A software engineer asks for strategies to bootstrap personalization for new users with no behavioral data, discussing the cold-start problem in content recommendation.

Moody is a Mac wallpaper app that dynamically changes based on your music and weather.