# Future You: Designing and Evaluating Multimodal AI-generated Digital Twins for Strengthening Future Self-Continuity

arXiv: https://arxiv.org/abs/2512.06106 · HTML: https://mlsanity.com/paper/2512.06106/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 114 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Builds on (most cited first, 19 of 19)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI](https://mlsanity.com/paper/2010.07487/) (2020)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [VividTalk: One-Shot Audio-Driven Talking Head Generation Based on 3D Hybrid Prior](https://mlsanity.com/paper/2312.01841/) (2023)
- [Why human-AI relationships need socioaffective alignment](https://mlsanity.com/paper/2502.02528/) (2025)
- [TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles](https://mlsanity.com/paper/2304.00334/) (2023)
- ["My Boyfriend is AI": A Computational Analysis of Human-AI Companionship in Reddit's AI Community](https://mlsanity.com/paper/2509.11391/) (2025)
- [Generative Ghosts: Anticipating Benefits and Risks of AI Afterlives](https://mlsanity.com/paper/2402.01662/) (2024)
- [Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2401.13699/) (2024)
- [MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation](https://mlsanity.com/paper/2403.19144/) (2024)
- [Future You: A Conversation with an AI-Generated Future Self Reduces Anxiety, Negative Emotions, and Increases Future Self-Continuity](https://mlsanity.com/paper/2405.12514/) (2024)
- [Constructing Dreams using Generative AI](https://mlsanity.com/paper/2305.12013/) (2023)
- [Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Agents to Enhance Young Adults' Career Exploration](https://mlsanity.com/paper/2502.18881/) (2025)
- [Embedding Atlas: Low-Friction, Interactive Embedding Visualization](https://mlsanity.com/paper/2505.06386/) (2025)
- [Leveraging AI-Generated Emotional Self-Voice to Nudge People towards their Ideal Selves](https://mlsanity.com/paper/2409.11531/) (2024)
- [Large Language Models Predict Human Well-being -- But Not Equally Everywhere](https://mlsanity.com/paper/2507.06141/) (2025)

## Built on by (most cited first, 2 of 2)

- ["Death" of a Chatbot: Investigating and Designing Toward Psychologically Safe Endings for Human-AI Relationships](https://mlsanity.com/paper/2602.07193/) (2026)
- [NextMe-800: Anticipating Personal Behavior from Months of Egocentric Video](https://mlsanity.com/paper/2610.01461/) (2026)

## Abstract (arXiv)

What if users could meet their future selves today? AI-generated future selves simulate meaningful encounters with a digital twin decades in the future. As AI systems advance, combining cloned voices, age-progressed facial rendering, and autobiographical narratives, a central question emerges: Does the modality of these future selves alter their psychological and affective impact? How might a text-based chatbot, a voice-only system, or a photorealistic avatar shape present-day decisions and our feeling of connection to the future? We report a randomized controlled study (N=92) evaluating three modalities of AI-generated future selves (text, voice, avatar) against a neutral control condition. We also report a systematic model evaluation between Claude 4 and three other Large Language Models (LLMs), assessing Claude 4 across psychological and interaction dimensions and establishing conversational AI quality as a critical determinant of intervention effectiveness. All personalized modalities strengthened Future Self-Continuity (FSC), emotional well-being, and motivation compared to control, with avatar producing the largest vividness gains, yet with no significant differences between formats. Interaction quality metrics, particularly persuasiveness, realism, and user engagement, emerged as robust predictors of psychological and affective outcomes, indicating that how compelling the interaction feels matters more than the form it takes. Content analysis found thematic patterns: text emphasized career planning, while voice and avatar facilitated personal reflection. Claude 4 outperformed ChatGPT 3.5, Llama 4, and Qwen 3 in enhancing psychological, affective, and FSC outcomes.