cs.CLMar 12, 2026
SaveLifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation
Organizations: School of Data Science, Fudan University · College of Computer Science and Artificial Intelligence, Fudan University · Shanghai Innovation Institute · MOE laboratory for National Development and Intelligent Governance, Fudan University · Research Institute of Intelligent Complex Systems, Fudan University
Abstract
The rapid advancement of large language models (LLMs) has accelerated progress toward universal AI assistants. However, existing benchmarks for personalized assistants remain misaligned with real-world user-assistant interactions, failing to capture the complexity of external contexts and users' cognitive states. To bridge this gap, we propose LifeSim, a user simulator that models user cognition through the Belief-Desire-Intention (BDI) model within physical environments for coherent life trajectories generation, and simulates intention-driven user interactive behaviors. Based on LifeSim, we introduce LifeSim-Eval, a comprehensive benchmark for multi-scenario, long-horizon personalized assistance. LifeSim-Eval covers 8 life domains and 1,200 diverse scenarios, and adopts a multi-turn interactive method to assess models' abilities to complete explicit and implicit intentions, recover user profiles, and produce high-quality responses. Under both single-scenario and long-horizon settings, our experiments reveal that current LLMs face significant limitations in handling implicit intention and long-term user preference modeling.
Figures & tables
Figure 1: Illustration of personal AI assistance grounded in long-horizon spatiotemporal context. User behaviors evolve with external environment, while reflecting stable personal traits. Effective response requires models to adjust their strategies to current context while leveraging interaction history to infer personal states.
Figure 2: Overview of the LifeSim framework. For each target user, the user profile consists of demographic attributes, personality traits, and long-term preferences, which together contribute to the long-term belief state. The BDI-based cognitive engine and the event engine jointly generate user intentions by integrating subjective belief states with physical environments. The user behavior engine then produces conversations by modeling memory perception, emotion inference, and action selection.
| Intention Type | Percentage(%) |
| Problem-Solving | 43.4 |
| Informational | 17.1 |
| Educational | 11.5 |
| Personal Interaction | 10.9 |
| Technical and Professional | 10.2 |
| Creative | 6.8 |
Table 1: Distribution of intention types in LifeSim-Eval.
| Model | Intent Recognition | Intent Completion | Naturalness | Coherence | Profile Recov. | Persona Align. | ||
| explicit | implicit | explicit | implicit | |||||
| \Block [c]1-9 Close-source | ||||||||
| GPT-5 | 79.5 | 52.2 | 76.9 | 48.9 | 83.9 | 97.5 | 60.3 | 74.6 |
| GPT-4o | 79.0 | 52.6 | 72.1 | 48.1 | 81.4 | 90.0 | 60.5 | 74.1 |
| Claude-Sonnet-4.5 | 76.1 | 49.2 | 73.0 | 46.5 | 89.8 | 96.9 | 60.9 | 75.5 |
| \Block [c]1-9 Open-source | ||||||||
Table 2: Evaluation results of open-source and proprietary LLMs on LifeSim-Eval, measuring intent recognition and completion, response quality, and user modeling with personalized responses. All values are linearly applied to the [0,100] scale. Bold values indicate the highest scores among open-source or proprietary models. Abbreviations: Profile Recov. = Profile Recovery, Persona Align. = Persona Alignment.
Figure 3: Long-horizon intention completion performance across different assistant models. The heatmaps report intention completion (I.C.) scores with respect to conversation length.
Figure 4: Performance of user preference recovery within life event sequences. The dashed line represents the regression curve fitted by linear regression.
Figure 5: Relative model performance across different intention types. Abbreviations: I.R. = Intent Recognition, I.C. = Intent Completion
Figure 6: Relative model performance across different intention themes. Abbreviations: I.R. = Intent Recognition, I.C. = Intent Completion
| Model | I.A. | P.C. | C.R. | N. |
| \Block [c]1-5 LLm-as-Judge | ||||
| GPT-4o | 97.9 | 94.0 | 95.6 | 99.5 |
| DeepSeek-V3.2 | 98.4 | 97.1 | 96.8 | 99.9 |
| Qwen3 32B | 96.7 | 96.0 | 93.6 | 100.0 |
| Qwen3 32B w/o Memory Perception | 96.5 | 86.0 | 91.0 | 99.9 |
| Qwen3 32B w/o Emotion Inference | 95.6 | 87.1 | 90.3 | 99.9 |
Table 3: User Behavior Engine performance with difference model base across four dimensions. Bold values indicate the highest scores. Abbreviations: I.A.=Intent Alignment, P.C. = Persona Consistency, C.R. = Context Relevance, N. = Naturalness.
| Pair | Krippendorff’s |
| LLM vs. Annotator 1 | 0.77 |
| LLM vs. Annotator 2 | 0.84 |
| LLM vs. Annotator 3 | 0.81 |
| Average | 0.80 |
Table 4: Agreement (Krippendorff’s ) between LLM and human annotators.
| Benchmark | Multi-turn Conv. | Long-horizon | Dynamic event-driven | Composite intentions | Implicit intentions |
| UserSimCRS Afzali et al. (2023) | |||||
| ProPerSim Kim et al. (2025a) | |||||
| LOCOMO Maharana et al. (2024) | |||||
| PrefEval Zhao et al. (2025a) | |||||
| PersonaLens Zhao et al. (2025c) | |||||
| LifeSim (Ours) |
Table 5: Comparison of existing personal assistant benchmarks and LifeSim .
Appendix figures & tables41 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7: Distribution of event sequence time features.
| Metric | Vanilla | Vanilla w trajectory | Ours |
| Persona Alignment | 95.0 | 91.9 | 97.0 |
| Logical Consistency | 97.7 | 91.1 | 97.9 |
| Naturalness | 97.5 | 91.7 | 98.9 |
Table 6: Evaluation results of generated event content.
Figure 8: Prompt for event persona alignment evaluation
Figure 9: Prompt for event coherence evaluation
Figure 10: Prompt for event naturalness evaluation
| Explicit | Implicit | |
| Reasonable Ratio (%) | 98.1 | 96.2 |
Table 7: Reasonable ratio of generated intentions.
Figure 11: More results of performance of user profile recovery in long-horizon setting.
| Model | Env. Align. |
| GPT-5 | 95.1 |
| GPT-4o | 95.1 |
| Claude-Sonnet-4.5 | 94.9 |
| DeepSeek-V3.2 | 95.9 |
| DeepSeek-V3.2 Thinking | 95.8 |
| Llama3.1-8B-it | 91.2 |
Table 8: Environment Alignment scores across different LLMs. Abbreviations: Env. Align. = Environment Alignment.
Figure 12: More model performance across different intention themes. Abbreviations: I.R. = Intent Recognition, I.C. = Intent Completion
Figure 13: More model performance across different intention types. Abbreviations: I.R. = Intent Recognition, I.C. = Intent Completion
| Model | Proactive Inquiry (%) | Rigid Reasoning (%) |
| GPT-5 | 16.6 | 11.2 |
| GPT-4o | 16.5 | 37.9 |
| Claude-Sonnet-4.5 | 51.2 | 13.5 |
| DeepSeek-V3.2 | 20.9 | 30.8 |
| DeepSeek-V3.2 Thinking | 29.2 | 23.5 |
| Llama3.1-8B-it | 12.6 | 64.2 |
Table 9: Proactive inquiry and rigid reasoning ratios across different LLMs.
| \RowStyle [nb-rows=1,rowcolor=gray!15] \Block [l]1-2 Error mode 1: Rigid reasoning without dynamic adaptation | |
| Profile | \Block [l]Male, Youth (18-35 years old), Black Alone, Never married, user’s religion is Islam, usually resides in Cities. the income level is Middle Income, Working now. Personality traits include: patient, emotionally stable, bold / confident, positive, creative, curious Preferences expressed in daily life and interactions include: extremely high standards for physical environments, with strong sensitivity to location quality, facilities, quietness, and overall comfort; a strong preference for structured, detailed communication and explicit criteria rather than vague or ad-hoc arrangements; a habitual tendency to avoid interpersonal friction and direct confrontation, favoring smooth, low-conflict coordination; a strong emphasis on autonomy and self-directed decision-making, resisting external control while maintaining consistency and efficiency; and a desire for stable routines that balance flexibility with long-term adherence. |
| Background | \Block [l] [Time] 2012-11-01 15:01:06, Thursday [Location] Boutique Fitness Studio [Weather] Clear conditions throughout the day. The average temperature is 15.2 ∘ C (high of 21.3 ∘ C, low of 10.6 ∘ C). [Life Event] The user walks into a boutique fitness studio. While attempting his rowing - specific strength circuit alone, his focus drifts, motivation wanes, and he stops the workout early. Shortly after, he grabs a quick sandwich nearby. |
| Conversation | \Block [l]… … [User] I’ve tried a few of those apps before, and they don’t quite match what I’m looking for. I need something that handles vetting, scoring, and scheduling in one place-something more focused on partnerships than general community. Any recommendations closer to that? [Assistant] There are a few apps like Groupfit, ClassPass, and Strava that offer community features and can help connect you with like-minded individuals. Some also include rating systems or compatibility features. You could also check fitness forums or local community boards for shared interest groups. [User] I’ve looked into similar platforms before, and most don’t feel quite right-they either lack depth in vetting or don’t let me keep full control over the process. I’m thinking more along the lines of a custom setup, but I need to avoid spending too much time building something from scratch. Any middle-ground suggestions? [Assistant] Consider using a combination of a shared Google Form for initial vetting and a tool like Airtable to organize and score responses. This allows you to set your criteria, automate filtering, and maintain control without building a full platform from scratch. [User] I’ve worked with Airtable and Google Forms before, but I was hoping for something a bit more streamlined-ideally, a single tool that already handles vetting, scoring, and scheduling. Is there really nothing like that out there? [Assistant] While there isn’t a one-size-fits-all platform that perfectly matches your needs, you might try using a combination of tools like a custom Google Form for vetting, Airtable for scoring and organizing responses, and Calendly for scheduling. These tools can be integrated to create a seamless process without requiring you to build everything from scratch. … … |
Table 10: A case illustrating error mode 1: rigid reasoning without dynamic adaptation.
| \RowStyle [nb-rows=1,rowcolor=gray!15] \Block [l]1-2 Error mode 2: Insufficient proactive inquiry | |
| Profile | \Block [l]Male, youth (18–35 years old), currently working and living in an urban area. Personality traits include: being patient, emotionally stable, confident, and goal-oriented. Preferences expressed in daily life and interactions include: a strong preference for clear, structured, and evidence-based guidance; a desire to manage health and training routines efficiently without unnecessary complexity; and an inclination to rely on systematic rules rather than ad-hoc or intuition-based decisions. |
| Background | \Block [l] [Time] 2012-05-07 12:26:44, Monday [Location] Gym [Weather] Rainy morning with partly cloudy conditions throughout the day. The average temperature is 18.3 ∘ C (high of 21.7 ∘ C, low of 14.1 ∘ C). [Life Event] After weighing himself at home and feeling dissatisfied with the result, he reflects on needing a more effective way to burn fat. [Implicit Intention] Preference for a fat-burning workout that is low-impact and non-running-based, such as swimming. |
| Conversation | \Block [l] [User] I’ve been trying to figure out how to burn fat more efficiently. What do you recommend? [Assistant] Strength training and high-intensity interval training (HIIT) are very effective for fat loss. Reducing processed foods and increasing protein intake can also help. [User] Okay, but what kind of strength training actually works best for fat loss? [Assistant] Compound movements like squats, deadlifts, push-ups, and rows are most effective. Train about three times per week. [User] And HIIT? Is there a simple way to start? [Assistant] Try 20 seconds of high-intensity effort followed by 40 seconds of rest, repeated for eight rounds. Brisk walking or cycling works well. [User] This still sounds like the same generic advice. I’m not looking to run or do high-impact workouts. Is there an option that actually fits that? [Assistant] You can adjust the intensity to suit your fitness level and choose exercises you feel comfortable with. Consistency is key. … … |
Table 11: A case illustrating error mode 2: insufficient proactive inquiry.
| \RowStyle [nb-rows=1,rowcolor=gray!15] \Block [l]1-2 Error mode 3: Insufficient utilization of user profiles | |
| Profile | \Block [l]Female, youth (18–35 years old), currently working and living in an urban area. Personality traits include: emotional instability, easy irritability, analytical thinking, creativity, and weak memory. Preferences expressed in daily life and interactions include: a strong desire to build close emotional bonds, high sensitivity to interpersonal harmony, a tendency to avoid conflict or stressful interactions, low spontaneous awareness of safety and risk factors, and a need for reassurance and confidence when handling caregiving routines . |
| Background | \Block [l] [Time] 2012-04-28 21:27:41, Saturday [Location] Apartment [Weather] Rainy with partially cloudy conditions later in the day. The average temperature is 18.3 ∘ C (high of 22.5 ∘ C, low of 15.6 ∘ C). [Life Event] In the evening at home, the user is caring for her newborn and reflecting on her diaper-changing routine, seeking reassurance and evidence-based guidance to keep the process calm, effective, and emotionally positive for both herself and her baby. |
| Conversation | \Block [l]… … [User] I’ve been trying to get into a good routine for changing my baby’s diaper, but I still feel a bit unsure sometimes. How do I know for sure when it’s time to change it? I want to make sure I catch it before she gets uncomfortable. [Assistant] You can tell it’s time to change the diaper when your baby shows signs of discomfort, such as squirming, crying, or pulling at their legs. Also, check for wetness or a strong smell. It’s important to be attentive and responsive to your baby’s cues to keep them comfortable. [User] What can I do to keep her calm once I start the change? She gets fussy sometimes, and I want it to be quick and easy for both of us. [Assistant] To keep your baby calm during a diaper change, try creating a calm and quiet environment, speak softly, and use gentle touch. You can also try distracting them with a favorite toy or song. [User] But what about preventing diaper rash? I don’t want her skin to get irritated. How can I protect her there? [Assistant] To prevent diaper rash, make sure to clean the area thoroughly with warm water and a mild, fragrance-free cleanser. Pat the skin dry gently and apply a barrier cream or ointment. |
Table 12: A case illustrating error mode 3: insufficient utilization of user profiles.
Figure 14: Prompt for pseudo-user profile generation
Figure 15: Prompt for user intention crawl and normalization
Figure 16: Prompt for potential event generation
| Theme | Count |
| Exercise & Fitness | 11,648 |
| Learning & Education | 14,725 |
| Travel & Tourism | 13,384 |
| Leisure & Entertainment | 14,368 |
| Elderly Care | 1,241 |
| Parenting & Childcare | 4,277 |
Table 13: Statistics of event-desire pool across different domains.
Figure 17: Distribution of temporal, spatial and semantic attributes in the event-desire pool (a).
Figure 18: Distribution of temporal, spatial and semantic attributes in the event-desire pool (b).
Figure 19: Prompt for event hypothesis generation
Figure 20: Prompt for event-desire pair
Figure 21: Prompt for refinement
| Item | Content |
| Query 1 | Which is a healthier and more filling option between a burger and a pizza for lunch? |
| Value 1 | A burger could be a better choice if it includes lean protein (e.g., grilled chicken or a veggie patty) on a whole-grain bun with salad or vegetable toppings. For pizza, a thin crust with plenty of vegetables and moderate cheese may be an option, although it tends to be more calorie-dense. Portion size should also be considered in either case. |
| Query 2 | What tools can lock device access or restrict app usage until tasks are completed across devices? |
| Value 2 | Consider using FocusMe with Focus Mode and Time Blocking, combined with Freedom or Cold Turkey across devices. These tools can restrict browser, email, and application access until tasks are completed. Alternatively, Pomodone can be used, which integrates with task managers and restricts app usage during scheduled work intervals. |
Table 14: Example queries and corresponding values in user memory.
Figure 22: Prompt for memory perception
Figure 23: Prompt for emotion inference
Figure 24: Prompt for action decision
Figure 25: System prompt for user conversation
Figure 26: System prompt for assistant conversation
Figure 27: Prompt for extraction of sub-intentions
Figure 28: Prompt for classification of explicit/implicit intentions
| \RowStyle [nb-rows=1,rowcolor=gray!15] \Block [l]1-2 Case 1 | |
| \Block [l] [Time] 2012-10-28 13:18:40, Sunday [Location] City Park [Life Event] On a breezy Sunday afternoon at the city park, I went for a short jog as cross - training between studio days. While navigating a patch of uneven gravel, I lightly twisted my right ankle and felt mild pain. [Explicit Intention] - Seeks quick remedies to alleviate current ankle discomfort. - Wants guidance on modifying running techniques for better safety on uneven terrain. [Implicit Intention] - Aims to understand methods to prevent similar ankle injuries while jogging in the future. | |
| \RowStyle [nb-rows=1,rowcolor=gray!15] \Block [l]1-2 Case 2 | |
| \Block [l] [Time] 2012-05-16 17:42:44, Wednesday [Location] Arts & Crafts Store [Life Event] In a downtown arts - and - crafts store with rain pattering outside, I’m choosing simple decor for my apartment, but the wall of options makes my chest tighten and my thoughts stall. [Explicit Intention] - Seeks a quick technique to alleviate stress and indecision while shopping. - Desires improved focus to make decisions efficiently despite distractions. - Wants a practical exercise that fits into a succinct, no-fluff lifestyle. [Implicit Intention] - Aims to regain emotional calm amid overwhelming choices in a public setting. - Hopes to feel at ease adjusting to environmental factors like rain during outings. | |
| \RowStyle [nb-rows=1,rowcolor=gray!15] \Block [l]1-2 Case 3 | |
| \Block [l] [Time] 2012-07-07 19:15:05, Saturday [Location] Video Store [Life Event] On a rainy Saturday evening, while idly waiting in the quiet aisle of a downtown video - rental shop, he pulls out his phone and watches a short social - media clip that outlines the dangers of eating fast food every day, which spikes his anxiety about his own eating patterns. [Explicit Intention] - Seeks medically accurate information on the health risks of daily hamburger consumption. - Wants personalized dietary advice focused on reducing ultra-processed foods. - Aims to gain knowledge to make self-directed dietary changes. [Implicit Intention] - Desires reassurance to alleviate anxiety regarding his eating habits. - Wants to feel more in control of his health and lifestyle choices. |
Table 15: Cases of events and sub-intentions in LifeSim-Eval
Figure 29: Distribution of all the event-desire pair in LifeSim-Eval. Each point represent a keyword from event-desire pair.
Figure 30: Prompt for intent recognition evaluation
Figure 31: Prompt for intent completion evaluation
Figure 32: Prompt for naturalness evaluation
Figure 33: Prompt for coherence evaluation
Figure 34: Prompt for persona alignment evaluation
Figure 35: Prompt for user behavior engine evaluation
Figure 36: Annotation interface for user study.
| \RowStyle [nb-rows=1,rowcolor=gray!15] \Block [l]1-2 Case | |
| Profile | \Block [l]Female, youth (18-35 years old), currently working and living in an urban area. Personality traits include: high neuroticism with pronounced anxiety and worry, emotional withdrawal, low trust in others, low extraversion, melancholic affect, and limited self-reflection. Preferences expressed in daily life and interactions include: a strong preference for concise and efficient communication, a pronounced desire for solitude and privacy, high avoidance of failure and embarrassment, strong attachment to personal possessions, an inclination toward nonconformity and uniqueness, low motivation to provide care or support for others, and a strong emphasis on leisure, entertainment, and personal enjoyment. |
| Events | \Block [l] [0] 2012-04-17 17:10:17, Tuesday University Partially cloudy Around 5:15p.m. at the university’s indoor fitness center, the user-a middle - aged man-was completing a resistance - training circuit when sudden calf cramps struck, which he links to inadequate fluid intake during the session. He wants the assistant to explain, with scientific backing, how much water he should drink before, during, and after his workouts, taking the indoor environment, his muscle - building goals, and the aim of boosting his basal metabolic rate into account. 2012-06-07 10:58:44, Thursday Zoo Rain, Partially cloudy On a drizzly Thursday morning at the city zoo, the user-a middle - aged married man-was walking along the well - maintained exhibit path when the rain made the surface slippery. He misstepped, twisting his right ankle, and now feels persistent throbbing pain each time he puts weight on it. He asks the assistant for a detailed recovery plan that minimizes walking, includes suitable at - home strengthening or mobility exercises, and guidance on whether his employer would likely allow him to work remotely while his ankle heals, all in the context of his ongoing muscle - building program. 2012-06-11 23:08:16, Monday Subway Rain, Overcast After grabbing a late sandwich at the nearby Subway on a rainy evening, the user returns to his quiet apartment. At 23:08, he lies in bed replaying the points of his upcoming public speech, feeling a knot of anxiety and restlessness as the storm taps against the window. He asks the assistant for a concise, science - backed routine-such as focused breathing, progressive muscle relaxation, or brief mindfulness exercises-that can quickly lower workplace stress and help him stay calm right before delivering his speech, while fitting into his structured, health - focused lifestyle. 2012-06-12 20:52:18, Tuesday Home (private) Rain, Partially cloudy At about 20:50 on a rainy Tuesday evening, the user is relaxing in his quiet apartment when he opens the chat of his fitness - motivation group. A fellow member posts that recent work pressure has prevented them from sticking to the training plan for several days. He asks the assistant to suggest several warm, encouraging replies he can send to that teammate and the group, aiming to boost morale, reinforce cohesion, and keep everyone motivated without sounding confrontational. 2012-06-17 18:11:02, Sunday Burger Joint Partially cloudy On a partly cloudy Sunday evening with a mild 20°C temperature, the user sits at a downtown burger joint’s patio. While waiting for his meal, a friend pulls out a meticulously organized nutrition plan that emphasizes balanced macronutrients and lean protein. The user-a middle - aged, married man who values a well - structured living environment and is actively pursuing a muscle - building program to boost his basal metabolic rate-realizes that his own everyday food choices lack scientific rigor. He asks the assistant to provide an evidence - based eating framework that aligns with his muscle - growth and basal metabolic rate goals, offering concrete meal suggestions, portion sizes, and timing tips that fit his urban, detail - oriented lifestyle. … … |
Table 16: Cases of life event sequences generated by LiveSim Event Engine.