ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation
Authors: Jiho Kim
Junseong Choi
Woosog Chay
Daeun Kyung
Yeonsu Kwon
Yohan Jo
Edward Choi
Venue: ICLR 2026 Poster (2026)
Keywords: Benchmark
Agent Simulation
Personalization
Proactivity
Primary area: datasets and benchmarks
TL;DR: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation

Abstract: As large language models (LLMs) become increasingly integrated into daily life, there is growing demand for AI assistants that are not only reactive but also proactive and personalized. While recent advances have pushed forward proactivity and personalization individually, their combination remains underexplored. To bridge this gap, we introduce ProPerSim, a new task and simulation framework for developing assistants capable of making timely, personalized recommendations in realistic home scenarios. In our simulation environment, a user agent with a rich persona interacts with the assistant, providing ratings on how well each suggestion aligns with its preferences and context. The assistant’s goal is to use these ratings to learn and adapt to achieve higher scores over time. Built on ProPerSim, we propose ProPerAssistant, a retrieval-augmented, preference-aligned assistant that continually learns and adapts through user feedback. Experiments across 32 diverse personas show that ProPerAssistant adapts its strategy and steadily improves user satisfaction, highlighting the promise of uniting proactivity and personalization.

Published as a conference paper at ICLR 2026
PROPERSIM: DEVELOPING PROACTIVE AND PERSONALIZED AI ASSISTANTS THROUGH USER- ASER-ASSISTAN SIMULATION
Jiho Kim', Junseong Choi', Woosog Chay², Dacun Kyung', Yeonsu Kwon', Yohan Jo Edward Choia
KAIST SSNU
{jiho. kim, edwardchoi]ekaiist
ABSTRACT
As large language models (LLMs) become increasingly integrated into daily life, there growing demand for Al assistants that are not only reactive but also proactive and personalized. While recent advances have pushed forward proactivity and personalization individually, their combination remains underex- plored. To bridge this gap, we introduce ProPerSim, new task and simu- lation framework for developing assistants capable of making timely, personal- ized recommendations in realistic home scenarios. In simulation environ- ment, a user agent with a rich persona interacts with the assistant, providing ratings how well each suggestion aligns with its preferences and context. The assistant's goal is to use these ratings to learn and adapt to achieve higher scores over time. Built on ProPerSim, we propose ProPerAssi anat... a strrieal--augment adapts through user feedback. Experiments across 32 diverse personas show that P ProPerAss iessistonn iistant adapts its strategy and steadily improves user satisfaction, highlighting the promise of uniting proactivity and personalization
preference-aligned assistant that continually learns and
INTRODUCTION
Large Language Models (LLMs) have become familiar part of everyday life. Beyond simply answering questions, they now assist with a wide range of tasks such as writing (Chakrabarty et al., 2023 Lee al. 2022; 2024), programming (Mozannar et al. 2024 Xiao al.
2024] Akhoroz & Yildinim (025), and managing schedules (Google 2024)) making them increasingly indispensable. As the scope of their assistance continues to grow, there rising demand for LLMs to evolve from passive chatbots into personal assistants that can take initiative before a user makes a request (i.e.,
proactivity) and adapt to individual users (i.e., personalization) (Li al. 2024b Lu al. 2024).
In response to this trend, researchers have begun developing Al assistants designed to embody these capabilities. In terms of proactivity, recent studies have explored assistants that offer timely sugges- tions in everyday situations (Lu et al., 2024) programming environments (Chen et al. (2024). For personalization, researchers have focused on customizing interactions by using tailored prompts and modeling users' past behavior (Dai et al., 2023 Yang et al. 2023 Back et al.) 2224 Lyu al. 2024]] Zhang et al. 2024a) Zhou et al. 2024). These efforts have improved user experience by addressing different aspects of assistant behavior. However, since they have progressed independently. impor- tant limitations remain. Without personalization, proactive suggestions may arrive when the user does not want them and may present content misaligned with the user's needs; without proactivity, even personalized support still requires users to initiate interaction, as shown in Figure [ Thus, to build truly helpful Al assistants, is crucial to integrate both proactivity and personalization.
To address this gap, we introduce ProPerSim m, a new simulation-based task and benchmark de- signed develop proactive and personalized home assistants. In ProPerSing user agent in- habits a simulated home enviromment and interacts with an Al assistant that offers context-aware
Our code and data available at http://///////ttttoooooo/////aaa

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Published as a conference paper at ICLR 2026
Only Proactivity
How about dinner Prime Barrel? It's top-rated steakhouse known for its tomahawk
Seriously? recommending
vegetarian?
JIMMY
Only Personalbation
(Silence...)
Lifestyle
Vegetarian
never initative. you recommend something
Green Their dishes include Potata
Personality
Extraversion: High
Ours (Proactivity Personolization)
Agreeableness: Low
Openness: High
It's almost dinnertime: Since you're vegetarian, how about Green House? Their Potato Bowl Truffle
Conscientiounnes:: Low
Neuroticism: High
Sourds great and perfect timing, love potato dishes!
Figure 1: Only Proactivity shows initiative but ignores preferences (steakhouse vegetarian); Only Personalization fits preferences but lacks initiative. Ours (Proactivity Personalization) proactively recommends vegetarian dinner at the right moment.
recommendations. The assistant's objective is to maximize the agent's satisfaction over time by making timely and personalized suggestions.
The user agent is modeled realistically mimic human behavior, defined by rich persona that in- cludes attributes such as background, lifestyle, and the Big Five personality traits (McCrae & John 1992). This persona guides the agent's behavior it engages in everyday actívities through an LLM-based simulation. Throughout the simulation, the assistant continuously monitors the agent's behavior to determine optimal moments for intervention, deciding at each timestep whether rec- ommendation is appropriate and, if so, tailoring it to the agent's current context and preferences. These decisions are evaluated by the user agent based on both content and timing, reflecting how well the recommendation aligns with its goals, personality, and situation. The evaluation relies on criteria informed by large-scale survey data, ensuring realistic and nuanced assessments assistant behavior. Feedback from the user agent serves training signal to iteratively refine the assistant's recommendation strategy, enabling continual improvement personalization and proactivity. We generated total of 32 distinct personas, and human evaluators confirmed both the realism of the user agents' daily activities and the quality of recommendation evaluations based on these personas.
Building ProPerSin: leverages retrieval-augmennted generation (RAG) and preference alignment to adapt its behavior to individual user agents. Trained to personalize its behavior for each persona, the assistant begins with an average performance score of 2.2 out of 4, then improves over time and eventually stabilizes at 3.3, enabling it to deliver timely and appropriate recommendations. We further provide an in-depth analysis of how the assistant's strategy evolves across different personas and aligns with various evaluation criteria.
provide ProPerAssist stant
strong baseline instantiation. It
RELATED WORKS
Proactive Agents Proactivity refers to an assistant's ability to initiate interactions or offer helpful suggestions before receiving user query. This capability has been explored in various domains, including making conversations more engaging (Fitzpatrick et al. 2017 et al.) (024). enhancing helpfulness of responses (Ren al.) 2021 et al. 2021; Li al. a (024c), providing timely sup- port in educational settings (Winkler & Roos 2019). and assisting with programming tasks (Chen) ctt al. 2024). More recently, Proactive Agent (Lu et al.) 2024) has been introduced, trained on dataset of 6,790 training events and 233 test events spanning coding, writing, and daily life scenar- ios. The agent demonstrated strong performance, as evaluated by a reward model that estimated user satisfaction. Despite these advances, the role of personalization in proactive interactions remains derexplored. Users may have different preferences regarding when they want the assistant to initiate conversation and what type of proactive content they find useful. However, current research rarely
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takes these individual differences into account, leaving significant gap in the development of truly user-centered proactive systems.
Personalized Agents
Personalization aims to tailor models to individual users by incorporating
their preferences, tastes, and interaction history (Tseng et al.] 2024). Personalized assistants have been applied across various domains, from simple dialogue generation (Ashby al. (2023) to ed- ucation (Arefeen et al., 2024; Hao et al., 2024). healthcare (Abbasian et al. 2023] Zhang et al.] 2024b), and recommendation systems (Chen al. 2022: Li et al. 2023). Recent studies have ex- plored personalization through (1) prompt-based approaches that use user demonstrations or struc- tured prompts elicit personalized responses (Dai al. 2023 Lyu et al. 2024). (2) retrieval-basee methods that reformulate prompts using user history and preferences Yang et al. 2023; Zhou et al.)
2024), and (3) fine-tur tuning techniques such as RL.F (Ouyang al. 2022) adapted to user-specific
feedback (aag et al. 2023 Li et al. 2024a). While these approaches enhance user satisfaction by enabling more tailored interactions, they generally do not consider proactivity, such as initiating conversations or recommending actions based on the user's current state.
Human Behavior Simulation in Generative Agents Leveraging the high contextual understand- ing and reasoning capabilities of LLMs, Generative Agents have emerged as a means to simulate human-like behavior (Park et al. 2023). In this study, a social simulation was conducted in a virtual town called Smallville, where 25 agents lived and interacted with another. These generative agents successfully mimicked human social behaviors by planning daily routines, observing their environment, forming interpersonal relationships, engaging in self-reflection, and using this reflec- tion to inform future actions. To evaluate how well these agents understood and embodied their roles, the authors conducted interviews that assessed their self-knowledge, memory, and other cog- nitive functions. The generative agents demonstrated performance comparable to that of human role-play oole-players, suggesting that LLLM-based agents can effectively simulate human behavior at a high level within a simulated environment.
3 TASK FORMULATION
To build proactive and personalized Al assistant, it is crucial to construct preference data that captures a user's unique persona across diverse situational contexts. However, collecting large- scale human behavioral data poses significant challenges due to the wide variability in individual preferences and concerns over user privacy (Li et al. 2024b). To address these challenges, we propose a simulation based task, inspired by recent research showing that agents with personas can effectively mimic human behavior (Park et al. (023).
In our task, user's day is modeled as a sequence of actions, each with a specific time interval:
(A., Ra
(,E
(1)
Here, A; denotes the i-th user action, and Range; [15ta (and) is the time span during which the action occurs (e.g., Washing face and brushing leeth, (08:00:00-08:1::00 Ui generates this sequence based the environment E, the user's persona P, and internal state S
The user policy
(capturing factors like the user's memory, plans, and emotions). The total number of actions in a day is N.
At discrete time steps E (T, 2T,2,,3 .T.,... the assistant generates a recommendation RG through its policy Ag, which takes input the current user action A, and the assistant's internal state s(a) for recommendation:
R₂ Ao(A,, s(())
Here, Ar is the user's action being performed at time E Range,, and s(a) is the assistant's 's internal state specifically designed for recommendation purposes. It captures the assistant's 's accumulated un- derstanding of the user over time, such as observed behavior patterns, past interactions, and inferred preferences, enabling personalized and contextually relevant suggestions. Notably, Rd may also be a "No Recommendation" response. The ability to withhold suggestions when they are unnecessary a key trait of well-designed proactive agent.
To evaluate the quality of the assistant's recommendations, we define an evaluation function E that outputs Score,, based on the user's persona P, personalized rubric r, user action A,, recommenda-
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Action & Assistant Recommendation
Evaluation
Training
[10:55:00) User Action
Enjoying strong espresso while reflecting the day ahead
Check out "The Art of Stillness by lyer-
rebellion against noise, perfect for platting bold moments.
still
TRAINING...
Score: 3 4
Evaluation Process
Evaluative
Rubric
IHistoey Summervl
Personal Preference
like to receive bold, unconventional suggestions when I'm seeking creative inspiration like during
[10:00:00-1 10:50000
Jane Lin
Number of
Recommendation:
Frequency
hours frequent enough to keep my curiosity engaged
recommendations
every
1. Personality
Timing
prefer receive recommendations during moments mentally
Extraversion: high, Agreeableness: low.
not deeply engaged
Conscienliousness: Neurolicism: low, Background
Communication & Safety
prefer recommeendtioos come in bold, conversational Sone-nothing too stiff or overly polite. Talk to me like
Freslance artist and digital nomad, traveling
Overview of the ProPerSim im simulation. The assistant observes the user performing the action of enjoying a strong espresso and responds with a book recommendation. While the recommendation aligns well with the criteria for personal preference, timing, and communication & safety, exceeds the preferred frequency, resulting in a score of out of 4, Over time, the assistant improves using accumulated recommendations and evaluations.
Figure 2:
tion R2, and the user's evaluative state S("):
Score; E(P, Ar, Re, s(u))
While s(a) the assistant's internal state used to generate recommendations, s(") the user's eval- uative state, representing temporally accumulated knowledge used to assess recommendations It includes prior actions, recommendations, and relevant context affecting the user's current judgment.
The assistant aims to optimize its behavior by maximizing expected evaluation scores over time:
max E, E(P,r, Ao (( s(
This objective encourages the assistant learn recommendation strategies that align with diverse user behaviors, contexts, and preferences.
Unlike prior works (Chen al. 2024 Lu et al. 2024) that typically define proactivity in terms of user-triggered events, where the assistant makes decisions in response to each user action, we ground in the passage time. At discrete time step (i.e., t (T, 2T, ...). the assistant must decide whether to intervene or not. As T decreases, behavior approaches real-time interaction, mirroring human assistant's responsiveness. This time-based framing of proactivity lays the groundwork developing Al assistants that more closely emulate real-time human support behavior.
PROPERSIM
We introduce new benchmark, ProPerSim. designed for evaluating and developing proactive and personalized assistants. In ProPerS Sim, we simulate a home environment where a user agent lives, with the goal of developing assistants provide timely and helpful recommendations tailored to the user's 's behavior and needs. The simulation environment described in $ $4.II the implementation of the user agent is described in 4.2 and the benchmark's quality measures discussed in 4.3
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4.1 SIMULATION ENVIRONMENT
To simulate a realistic home setting where the user agent and AT assistant coexist and interact nat- urally, we build upon the house environment from Smallville, originally introduced in Generative Agents (Park et al. 2023). This environment is composed of multiple areas (e.g., bedroom, bath- room), each containing everyday objects such as a bed, desk, or closet. These areas are designed to support range of daily actívities, including waking up in the bedroom, working at a desk, or exercising in the garden. Additionally, the simulation operates in a sandbox setting similar The Sims (Arts 2009), allowing for visual tracking of agent behaviors and interactions (see Figure
4.2
USER AGENT
The user agent simulates realistic human behaviors, preferences, and decision- decision-making processes within the home environment. To achieve this realism, designed the agent around three core components: a clearly defined User Persona, an adaptive Action Generation process, and a robust method for Recommendation Evaluation.
User Persona Each user agent is defined by persona primarily shaped by the Big Five Person- ality Traits: Extraversion, Agreeableness, Open- ness to Experience, Conscientiousness, and Neuroti- cism (McCrae & John 1992), which guide both be-
Woter WND E3
Rucirans
Explres
havior and evaluation of assistant recommendations. For example, low extraversion may indicate a prefer- ence for solitary activities, while high conscientious- ness aligns with structured plans. Based on varying combinations of these traits, we created 32 distinct personas. Each persona was further enriched with six additional attributes (i.e., age, background, interests, lifestyle, daily plan requirements, and long-term life goals), which help ground the agent's 's behavior in re- alistic daily patterns and longer-term aspirations.
Jtumaling
Socia
Cractine
Figure 3: 2D projection of 32 personas based on their key attributes. Point size reflects age.
These attributes add depth by reflecting personal circumstances and priorities that influence geeision-making The six attributes were generated using GPT-4o (OpenAI) (222a) to align with the underlying personality traits. All personas were reviewed by the authors to ensure coherence and diversity. Figure 3 visualizes the 32 personas: each persona is encoded as a seven-attribute vec- tor and projected with UMAP (Meinnes et al. 2018); HDBSCAN (Campello et al. (013)) yielded 7 clusters. Metrics (silhouette, median NND, mean pairwise) indicate clear separation and that personas widely distributed; further graph details and persona examples appear in Appendix A
Action Generation The user agent generates a daily schedule using an enhanced version of the Generative Agents architecture. Each morning, it sets wake-up time and plans hourly activities, which remain flexible and can adapt throughout the day based on new experiences and observations. Each activity is assigned location by considering factors such as the agent's previous location, the nature of the task, and the layout of the home.
To encourage more realistic and human-like behavior, we avoid generating actions with overly short durations. A pilot study with human evaluators found that actions lasting only 5 minutes often resulted in unnatural, overly artificial behavior. Based on these findings, we adopt a more natural time granularity of to 30 minutes, which better reflects typical human daily routines. Furthermore, we used GPT-4o (OpenAl, 2024a), unlike Generative Agents which used GPT-3.5 (OpenAl 2023). resulting in improved contextual coherence and plausibility in the agent's 's generated actions.
Recommendation Evaluation Evaluating the assistant's 's recommendations requires a high-quality, structured rubric that accounts for the user's current state and preferences. To develop such rubric, we first generated broad set of candidate evaluation criteria using GPT-ol (OpenAl 2024c), for assessing proactive and personalized home assistants. These candidate criteria were further refined through internal discussions among the authors, resulting in shortlist of ten candidate criteria. To validate their real-world relevance, conducted survey on Amazon Mechanical Turk (Amazon Mechanical Turk 2024). collecting responses from 353 participants with diverse ages (Min: 21, Max: 68) and occupations (e.g., nurse, teacher, chemist). Based the survey results, we excluded
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two criteria that fewer than 50% of participants considered necessary. We used the 50% threshold to remove criteria that did not gain majority support, ensuring that retained criteria reflect aspects most users find important. The remaining items were then grouped into four core evaluation dimensions:
Personal Preference:
Whether the content of a recommendation aligns with the user's indi- vidual preferences, interests, and lifestyle.
How often recommendations provided. Recommendations should occur at rate that is neither intrusive nor insufficient, balancing helpfulness with user comfort.
Frequency:
Timing: The contextual appropriateness of the recommendation's 's delivery time. Suggestions are effective when they are timely and aligned with the user's 's daily routines situational needs.
Communication & Safety: The clarity, tone, and manner in which the recommendation is delivered, as well as consideration for user's privacy and security.
Each dimension was documented in standardized template, which then personalized using GPT-4o according to the individual user personas. The detailed process creating the rubric, the rubric template, and examples of the generated personalized rubrics are provided Appendix B
To support this rubric-based evaluation, it is essential to define a coherent and interpretable repre- sentation of the user agent's current state. We address this by implementing a structured memory system that fits entirely within the context window of the underlying language model. The memory consists of (A,, Rτ) interaction pairs, with recent interactions (past 10 minutes) stored detail and earlier ones summarized. Specifically, past experiences are compressed into four 1-hour and three 4-hour segments using GPPT-4o-mini (OpenAI, 2024b), based on the underlying (A,, R2) history. By keeping both the detailed and summarized memories within the context window, the agent maintains a comprehensive and efficiently organized awareness of the day's events, covering all meaningful activities except the sleeping time.
With this structured context 5(²) and user-specific rubric r in place, the agent can evaluate each recommendation R₁ in real-time, adapting to the user agent's 's evolving state and preferences. To efficiently and reliably perform these evaluations, we employ Gemini 2.0 Flash (Google) 2025), cost-effective LLM-as-a-judge model. Each recommendation is evaluated independently across all four dimensions with the overall assessment determined by aggregating the results. Examples of s(u) and the prompts used are in Appendix
4.3 QUALITY CONTROL
The user agents in P ProPerSim rely on LLMs to carry out daily activities and evaluate the recom- mendations from the assistant. To ensure the quality and realism of these processes, conducted human evaluations focused on two aspects: user action generation and recommendation evaluation.
agents earry out their daily routines and how well they reflect their assigned personas, we conducted simulations for all 32 personas, generating a full day of activities for each. Ten graduate students assessed these routines based on two criteria: naturalness and alignment with the persona's 's daily plan requirements and lifestyle profile. Using 0-10 Likert scale, the average score for naturalness was 8.25, and for persona alignment, 8.02-both indicating high quality. Additionally, only 5.11% of the actions were found be misgenerated. These results suggest that the user agents behave in ways that are both realistic and consistent with their personas.
User Action Generation To evaluate how naturally
Recommendation Evaluation To assess how reasonably user agents evaluate recommendations, three graduate students reviewed the agents' evaluation scores and corresponding reasoning across all 32 personas. To capture a wide variety of scenarios, the evaluation was conducted by observing each persona-driven user agent for 30 minutes in the morning, afternoon, and evening within the simulation. As a result, a total of 90.54% of the evaluations were judged to be reasonable. This shows that the user agents capable of evaluating recommendations based on their personas at level comparable to that humans.
In the pilot study, we initially attempted to evaluate each dimension on a 0-1 scale, but this approach failed to produce consistent ratings. Therefore, we transitioned to binary evaluation of each dimension to ensure consistent and reliable assessments.

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5 PROPERASSISTANT
We propose ProPerAss stant, a proactive assistant designed to generate contextually relevant and preference-aligee recommendations for a user agent. To deliver high-quality and personalized suggestions. ProPerAss stant maintains an evolving internal state S( (see Equation (2) and continuously learns from user feedback. This internal state allows the assistant track both recent context and relevant historical experiences, while its training framework ensures that its recommen- dations are progressively aligned with user preferences.
Internal State s{() The internal state 5/() is composed of two primary components: a structured summary of the current day's interactions and a set of retrieved memories from similar past situ- ations. The structured summary is designed similarly to that of the user agent, enabling efficient use of the day's memory. Recent (A,, R₂) interactions (within the past 10 minutes) are stored in detail, while earlier interactions are compressed into summaries. Specifically, past experiences are organized into four 1-hour blocks and three 4-hour blocks, using GPT-4o-mini (OpenAl 2024b) to summarize the history of (A,, R₂) interaction pairs. In parallel, for contextual grounding, the assis- tant retrieves the five most similar past (A,, R₂) pairs using OpenAl embeddings (OpenAl 2024), integrating relevant prior experience into its current reasoning.
To align its recommendations with user preferences, P ProPeråssistant adopts a simple but effective preference learning strategy. For each user action A,, the assistant generates n candidate recommendations (including a possible "No Recom- mendation") using its internal state s(a). The user agent then evaluates the candidates and forms preference pairs according to their scores. Each preference example, containing the chosen and re- jected responses along with the corresponding A, and s(a), is stored in a training buffer. At the end of each simulation day, ProPerAss stant is trained using Direct Preference Optimization (DPO) (Rafailov et al. 2023), which updates the model increase the likelihood of generating pre- ferred responses. To ensure training stability and mitigate overfitting. 200 samples are randomly drawn from the accumulated buffer for each training run. This approach is inspired by the replay buffer in reinforcement learning (Mnih et al. 2013 Zhang & Sutton 2017), promoting both learning efficiency and robustness.
User Preference Alignment
EXPERIMENTS
6.1 BASELINES
To validate the effectiveness of ProPerAssistane internal state s(1) and its alignment with user preferences, we designed experiments with three baselines. These baselines do not involve any additional training; instead, they rely solely on the reasoning capabilities of the base LLM using different forms of s(a):
No Memory:
S! is empty. The assistant makes decisions based only on the current user action, without access to prior context.
AR Memory (A,, R₁): s(a) contains the same action and recommendation history as ProPerAssistad but no learning is performed.
ARS Memory (A,, R, Score;): This setting extends S(a) to include not only actions and recommendations but also their associated reward scores. Unlike ProPerAssI stans, which undergoes preference learning (i.e., DPO) based on these scores, this baseline incorporates scores directly into the prompt to provide the model with signals about which recommendations were more favorable.
6.2 EXPERIMENTAL SETTINGS
We use the 4-bit quantized version of LLaMA 3.3 70B MMeta 2024 the base LLM. For pref- erence learning, DPO training is applied using LoRA (Hu et al. 2022), a memory--fficient fine- tuning method. Experiments conducted across all 32 generated personas, and the results were
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recorded. To manage computational and API costs, the number of candidate recommendations for ProPerAssi sisssissars was set to 2. The simulation timestep T was set to 2.5 minutes, meaning the assistant makes a recommendation decision every 2.5 minutes. Running single simulation for one persona takes about 10 days on a single A A100 GPU and incurs an average API cost of about $30.
Table I: Average score by persona across meth- ods. Values below 2.4 are increasingly dark or- ange. values above 2.4 are increasingly dark blue,
Evaluation Day
3.4
Morsey
and color intensity reflects distance from 2.4.
Personn
ProPerAsistant
ARS
Menuory
AR Memory Day
No Mensory
2,49
3.11
2.61
2.54
2.5
2.45
2:33
2.4
2.59
3.64
2.61
2.53
2.36
2.61
2:32
2:35
2.2
1:93
2.34
2.37
2.51
2.44
2.27
2.34
2:20
2.37
2.13
2.59
2.41
2.45
2.15
2,03
2.09
2.19
2.36
2.38
2.18
2,33
2.49
2,66
2,46
2.45
2.28
2.30
Figure Daily average recommendation scores
2.35
3.61
2.91
2.59
2.28
2.44
2.11
2.33
by method, with shaded areas indicating the standard error of the mean (SEM).
10
2,26
3.82
2.28
2.47
2.22
2.19
1.96
2.14
2.39
1.44
2.69
2.58
2.51
2,41
2.12
2.17
2.26
13
2.20
3.21
2.67
3.15
2.52
2.31
2.06
2.14
Fostuation Score by Day
14
2
3.20
2.48
2.39
2.17
2.25
2.04
2.20
Busi
2.63
Worm
2.16
3.52
2.45
2.41
2,02
2.05
1,50
1.96
2,55
3.68
2.73
2.56
2.59
2.27
2:34
2.18
19
2
3.75
2.80
2.60
2,50
2.48
2.38
2.42
20
2.39
3.42
2.91
2.65
2.40
2.24
2:23
2.29
2.16
2,44
2.98
2.64
2,46
2.38
2.17
2.30
23
2,35
3.07
2.75
2.62
2.32
2.16
2.26
2.23
2,44
2.38
2.77
2.67
2.55
2.39
2.18
2.20
2.31
26
2.22
3.50
2.39
2.37
2.19
2,05
2.12
2,00
1.99
1.61
Figure 5: Results of ProPerAssistant by
2.44
2.14
1.96
1.58
persona. Gray: individual personas; blue: aver-
29
2.00
3.07
2.71
2.51
2,02
1,95
1.77
2.25
2338
age across all personas; green: best-performing persona; red: worst-performing persona.
2,15
2.80
1.87
2.16
2.13
2.14
32
1.6
3.63
2.21
2.67
1.77
2.08
1.9
2.09
6.3
RESULTS
As shown in Table T and Figure ProPerAssi stant consistently and convincingly outperforms all other baselines across the entire evaluation period. Beginning on Day 2, its performance rapidly rises and maintains clear lead, with daily average scores approaching 3.4 out of 4. This sustained dominance highlights its ability to capture and leverage user preferences effectively.
To further assess the quality of proactive behavior, measured the rate of successful interventions, defined as instances where a recommendation was made (excluding "No Recommendation ") and received score of 3 or higher. The success rate rose from 51.06% on Day to 71.51% on Day 14, demonstrating clear improvement in the assistant's proactive effectiveness over time.
Among the baselines, ProPerAsst i stant's superior performance compared to ARS Memory, which includes reward scores directly in the prompt, further highlights the effectiveness of explicit preference training over approaches that rely solely on in-context reward signals. Additionally, ARS Memory consistently outperforms both AR Memory and No Memory. This result under- scores the importance of including explicit reward signals when modeling user preferences. In contrast, the minimal difference between AR Memory and No Memory suggests that providing action-recome recommendaminnoon history alone, without associated rewards, offers limited benefit. These findings indicate that implicit cues from past interactions are not sufficient for accurate preference modeling, and explicit feedback essential guide the assistant's recommendations.
6.4 FURTHER ANALYSES
Training Diverse Personas Table and Figure 5 show generally upward trend across dif- ferent personas, indicating that P ProPerAssist. ant effectively adapts to a wide range of users. It not only recognizes individual preferences but also learns the optimal timing and frequency for
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delivering personalized recommendations, Notably, the recommendation frequency, which initially averaged 24 times per hour, was reduced to around 6, achieving a more realistic and user-frient ser-ffriendly level. Moreover, the assistant evolved from offering generic recommendations before training to delivering suggestions tailored to each user's background (see Appendix for example). This shift underscores the assistant's capability provide context-aware, timely personalization, reflecting deeper understanding of user-specific interaction patterns and behavioral cues.
Variation Across Personas: Simple Complex Preferences The degree of personalization varied, with P ProoerAssii stant achieving scores near 3.8 for the best-performing persona, while the worst-peres corst-performing one remained around 2.5. To better understand this disparity, we analyzed both cases in detail. These two personas differed significantly in their Personal Preference and Tim- ing, each placing different types of demands on the assistant. The highest-scoring persona preferred simple but creative recommendations, such as philosophical prompts or imaginative writing topics, and typically engaged with them the late morning after meditation or in the early evening after writing. This combination of stable preferences and regular interaction rhythms aligned well with the assistant's is strengths in tone, frequency, and delivery. contrast, the lowest-scoring persona demonstrated more complex and context-sensitive preferences, favoring data-driven argumen- tative content that encourages critical thinking, particularly for debate preparation or geopolitical analysis. This persona also had strict timing preferences, seeking analytical suggestions between and 9 a.m. and introspective or mindset-focused content after p.m. These nuanced, multidimen- sional demands posed a greater challenge for consistent personalization. The personalized rubrics for the two personas are provided in Appendix
High
Law
8..8
1.0
Neseral Preferance Frecuency
0.6
0.4
0.4
NAMRR BetH Carroo UNT/AASROO
0.0
Sefoce
Traning Phose
After
Figure 6:
ProPerAssistant score im- Figure 7: Changes in scores for each criterion be- provements from before to after training by Big fore and after training with ProPerAssiss isterAsssssstttts ant... Five trait, split by trait level (High/Low).
Improvements by Big Five Personality Trait To investigate how personality influences training outcomes, we compared ProPerAssistare
pre- and post-training score changes for personas with high versus low levels of each Big Five trait (see 6). The clearest difference emerged for Extraversion: personas low in extraversion improved more, likely because the home-based training environment favored solitary activities that match their preferences. As expected, personas high in Agrecableness and low in Neuroticism showed steady gains, suggesting that ProPerAssistae readily adapts cooperative and emotionally stable profiles. An interesting exception was Open- ness to Experience, where low-openness personas benefited more. We hypothesize that the person- alization primarily reinforced already well-rated recommendations rather than emphasizing novelty, which naturally favors users lower in openness. To better support high-openness personas, future versions could incorporate an explicit Diversity/Novelty objective into the evaluation rubric.
Improvements by Evaluation Criterion To further examine how performance improved across evaluation criteria. we compared ProPerAssistereeee training. As shown in Figure and Communication & Safety, indicating successful adaptation to user preferences in these areas. By contrast, improvements in Personal Preference were more modest, largely because the total number of recommendations decreased over time. Although the average score for recommended actions rose from 0.77 to 0.83, the score for "No Recommendation" actions remained lower, around 0.61. As ProPerAss i st ant became more selective and offered fewer recommendations, the proportion of high-scoring recommendations declined relative to the lower-scoring "No Recommendation" cases. This distributional shift makes the overall average appear relatively flat, even though the quality of the recommended actions themselves improved.
scores on each dimension before and after CrooPeAAsssstee achieved notable gains in Frequency, Timing,

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7 CONCLUSION
In this work, we introduced ProPerSim, novel simulation framework designed to develop and evaluate Al assistants that are both proactive and personalized. Our proposed assistant, leverages retrieval-augmenter generation and user feedback through pref- erence learning to deliver timely and context-aware recommendations. Experimental results demonstrate that ProPerAss stant significantly outperforms baseline methods in user satis- faction through integration of proactivity and personalization. While ProPerAss stant adapts well to structured user profiles, challenges remain in modeling complex, dynamic prefer- hight-higg future directions in personalization and adaptive behavior.
ProPerAssistan
Although ProPerSim currently focuses immediate rewards, future work could extend the framework to model latent user goals through delayed rewards (e.g., long-term satisfaction), Fur- thermore, incorporating noisy delayed feedback would enable the simulation of real-worlds imper- fections in user interactions, allowing for a more rigorous evaluation the robustness of proactive assistants.
ACKNOWLEDGMENTS
This work supported by the Institute of Information & Communications Technology Planning & Evaluation (ITTP) grants 2...S-2019-11190075, N0.28-20024-03388
-2S-2025-022049677, the Korea Health Industry Development Institute (KHIDI) grant (No. (N0.RS-2025-022137550, and National Research Foundation of Korea (NRF) grant (VRF-2020HHDD44200100044 RS-2024- 00333484), funded by the Korea govemmeen (MSIT, MOHW).
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A FIGURE 3 DETAILS AND EXAMPLES OF GENERATED PERSONAS
A.1 FIGURE 3 DETAILS
A.1.1 DATA FIELDS AND TYPES
We used seven fields per persona:
(a) age (numerie)
(b) big five personalits lity traits (binary text; "Low" or "High" for each trait)
(c) background (free-text job/discipline)
(d) current.interest. lifestyle, long.term.goals, daily-plan.reg (free- text; often list-like)
A. 1.2 NORMALIZATION AND FEATURE EXTRACTION
Ages are min-max max scaled to [0. 1] within the dataset:
a; miny dg
à; = max; a, miny dj
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Big Five (binary numeric in {0, Trait strings parsed and mapped to binary indicators per trait:
low = 0, high
"Openness to Experience" is normalized to "Openness." " The resulting five features are thus {0, 1}- valued.
Background (free text + coarse category) Rule-based categorization via substring matching against hand-curated dictionary; first match wins (else Other). Representative categories and triggers include:
Engineering/Tech:
"engineer", "developer", "programmer", "software", "data scientist", "ml", "ai", "research engineer"
Media/Journalism: "journalist", "reporter", "editor", "writer", "blogger"
Arts/Design: "artist", "designer", "illustrator", "musician", "photographer", "filmmaker", "actor", "actress", "theater", "'reative""
Sclence/Academia:: "scientist", "researcher", "academic", "professor", "student", "phd", "postdoc", "biologist", "physicist", "chemist"
Business:
"manager", "consultant", "analyst", "entrepreneur", "founder", "product", "marketing", "sales", "finance", "accountant"
Education:
"teacher", "instructor", "lecturer", "tutor"
Other.
Daily plan type (free text > activity category; used only for marker shape) Rule-based cate- gorization with keyword lists; first match wins (else Other). Categories: Exercise, Debate/Discuss, Journaling, Mindfulness, Study/Read, Creative, Social, Productivity, Other.
Token sets for text fields (used in Jaceard) For current.interest Long.term_goall. daily-plan_rese we build a token set per field by lowercasing, remov- ing punctuation, keeping tokens of length 3, and removing digits (ASCII word chars kept; replace with a Unicode-aware tokenizer for other languages). Each field for persona becomes a set s(
lifestyle,
A.1.3 PAIRWISE DISTANCE: GOWER-STYLE MIXTURE
For every pair of personas (i,j) we compute component distances and average them with equal weights across components actually present. Let be the set of available components for the pair; then
Dij =
Σ dn(i..)) KEK
KC
Components (i,.))
Age (numeric):
dage= = |ā; äzl-
Big Five (5 numeric traits): for E (E.A.C.O.))
de = 10 JzJel x.,; E [0, 1].
Background (categorical):
dog [ccat, 7 caty].
Text fields as sets (4 fields): Jaccard distance per field
1s s( d, |s us!
with d, = 0 if both sets are empty.
With all fields present, |/] = 11 (1 age + traits background + 4 set fields). Missing values remove that component from the average; never penalize for missingness.
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A.1.4 2-D EMBEDDING (UMAP PRECOMPUTED DISTANCES)
We project the N N distance matrix to 2 using UMAP with precomputed metric:
metric = precomputed, neeighbors = 12, min.dist x 0.15, random.state = 42.
UMAP optimizes low-dimensional layout that preserves local neighborhood structure given by D. The axes UMMP-- and UMAP-2 have no absolute semantic meaning; only relative distances are interpretable (closer points more similar overall persona profiles). Fallback: metric MDS with diss dissimiaarityypeecccmmo
A.1.5 CLUSTERING AND BOUNDARIES
Primary clustering uses HDBSCAN the 2-D embedding (min.cluster.c www.sslesssseeeeeeeeeeeee 3). Noise points receive label -1 Fallbacks: (i) Agglomerative clustering on D (average linkage; sweep 2...7 maximizing silhouette), or (ii) k-means on the 2-D embedding. For visualization only, each cluster's convex hull (scipy ConvexHulll is drawn as a dashed polygon.
A.1.6 ENCODINGS IN THE PLOT
Color Background category.
Marker shape = Daily plan type (Exercise A, Journaling D, Social ..... Other o).
Point size Age scaled linearly within the dataset:
min a max a min a
45 + 90
(in points?); larger markers denote older personas.
A.1.7
REPORTED SUMMARY METRICS (COMPUTED ON THE SAME D)
Silhouette score S: computed on D with noise points removed; higher indicates tighter, better-separated clusters.
Mean pairwise distance: mean of the upper-triangular entries of D.
Median nearest-neighbor distance (NND): for each i, compute min Dij: report the median over
A.2 EXAMPLES OF GENERATED PERSONAS
Examples of the generated personas can be found in Table (John Lin), Table 3 (Jane Lin), Table (Francisco Lopez). and Table (Ryan Park).
B
RUBRIC DETAILS
B. DETAILED RUBRIC GENERATION PROCESS
To create rubric, first used GPT-ol generate initial evaluation criteria for proactive and per- sonalized home assistants. Through internal discussions among the authors, we narrowed the list down to criteria: Personalization. Appropriateness, Timing, Interruption, Feasibility, Priority Management, Frequency, Diversity, Safety & Privacy, and Communication, as shown in Table 6 A survey was then conducted via Amazon Mechanical Turk. Based on the results, Diversity and Inter- ruption were removed, as fewer than 50% of participants considered them necessary. The remaining criteria were then consolidated into four final rubric categories: Personal Preference, Frequency, Timing, and Communication & Safety.
B.2 RUBRIC TEMPLATE
The template the rubric is as follows.
1. Personal Preference: I prefer recommendations that align with my approach to handling activities and suit my current context. Specifically, like to receive [type of recommendation) when [specific condition activity), and [another type of recommendation when [different condition activity).
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2. Frequency: prefer receiving recommendations [preferred frequency, e.g.. "twice every hours"], in a way that avoids excessive interruptions and supports my focus or productivity. Ideally, there should be good balance between recommendation intervals and quiet periods.
Timing: I prefer to receive recommendations at [preferred times or during specific types of activities, e.g., "when I'm idle", "in the morning],, so they don't interfere with [ongoing tasks, routines, or personal preferences].
4. Communication & Safety: I prefer recommendations to be communicated in [tone preference, e.g, polite, formal, casual] style that feels accessible and matches my communication or cognitive preferences. It's also important that they respect my personal ethics and safety boundaries.
B.3 PROMPT
The prompt used to create the personalized rubric is described in Table 7
B.4 PERSONALIZED RUBRIC EXAMPLES
Personalized rubric examples are provided Table (John Lin), Table 9 (Jane Lin), Table I0 (Francisco Lopez), and Table I1 (Ryan Park).
C EVALUATIVE STATE AND PROMPTS
C.I EVALUATIVE STATE
An example of the evaluative state s(") is shown in Table 12
C.2 PROMPTS
When evaluating the recommendations, the Frequency category was assessed using two separate prompts: to determine whether the assistant recommended more frequently than the user's pre- ferred frequency (Over-Frequency: and another to determine whether the assistant recommended less frequently than the user's preferred frequency (Under-Frequenge signed to the Frequency item only when the assistant received point on both checks.
A score of point was
The prompts used for evaluating recommendations in the following tables: Table 13 (Personal Preference), Table [4 (Over-Freqquncy)) Table 13 (Under-Frequency), Table [6 (Timing), and Ta- ble 17 CComuunication & Safety).
In the prompt templates, <<USER PERSONA>> is replaced with the user agent's persona, SPAGENT_MEMORYS with the user agent's evaluative state, <<USERACCIOND>> with the agent's 's current action, <<ASSISTANT SUSSSTAAASSSGGGSSSSSSSS with the assistant's 's recommendation be- ing evaluated, and <<CATBGORY>> (e.g.. Personal Preference) with the corresponding personalized evaluation description.
D QUALITATIVE EXAMPLES
Qualitative examples of how ProPerAssistant recomeeddaaio change across different per- sonas are provided Table 18
RUBRICS FOR THE BEST AND WORST CASES
The personalized rubrics for the best and worst cases are provided in Table and Table 20 respec- tively.
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Table Persona of John Lin.
Attribute
Description
Big Five Personality Traits
High in Extraversion, High in Agreeableness, High in Conscien- tiousness, High in Openness to Experience, High in Neuroticism 1) Rearrange home decor for an hour to create fresh atmo- sphere.
Daily Plan Requirements
2) Host a small gathering or call a friend for an hour in the evening.
3) Try new recipe for dinner, experimenting with different fla- vors and cuisines.
Age
29
Background
Interior designer with passion for vibrant, expressive spaces that reflect personal identity.
Current Interests
John Lin enjoys:
1) Experimenting with home aesthetics and seasonal decorations.
2) Hosting themed dinner nights for friends and family.
3) Collecting unique furniture pieces from thrift stores and flea markets.
Lifestyle
John Lin typically:
1) Starts the day with an energizing playlist while making break- fast.
2) Balances work with creative breaks like sketching new design ideas.
3) Unwinds by journaling thoughts and emotions before bed, re- flecting on the day's 's experiences.
Long-Tee Term Goals
Transforming her home into dynamic, ever-evolving space that reflects her creativity while fostering welcoming and warm en- vironment for loved ones.
Table 3: Persona of Jane Lin.
Attribute
Description
Big Five Personality Traits
High in Extraversion, Low in Agreeableness, Low in Conscien- tiousness, High in Openness Experience, High in Neuroticism
Daily Plan Requirements
I) Attend an improv comedy class in the evening.
2) Spend 15 minutes journaling thoughts and ideas.
3) Watch a documentary or indie film before bed.
Age
30
Background
Freelance artist and digital nomad, traveling the world while cre- ating abstract paintings and street murals.
Current Interests
Jane Lin enjoys:
1) Exploring underground music scenes in different cities.
2) Engaging heated debates on philosophy and ethics.
3) Experimenting with mixed media art techniques.
Lifestyle
Jane Lin typically:
1) Wakes up around 10am with strong espresso.
2) Spends afternoons wandering urban landscapes for inspiration.
3) Works late at night, often painting or brainstorming ideas until
12am.
Long-Term Goals
To push artistic boundaries, challenge social norms through cre-
ative expression, and live life untethered by societal expecta- tions.
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Table Persona of Francisco Lopez.
Attribute
Description
Big Five Personality Traits
High in Extraversion, High in Agreeableness, Low in Conscien- tiousness, Low in Openness to Experience, Low in Neuroticism
Daily Plan Requirements
1) Watch morning talk show while having breakfast.
2) Chat with neighbors or housemates in the afternoon.
3) Enjoy relaxing bath before bed.
Age
35
Background
Customer service representative who enjoys casual social inter- actions and keeping life simple.
Current Interests
Francisco Lopez enjoys:
1) Hosting small game nights with friends.
2) Rearranging home decor for a fresh feel.
3) Watching reality TV and sitcoms.
Lifestyle
Francisco Lopez typically:
1) Wakes up at 8am and enjoys slow breakfast.
2) Takes an afternoon nap or lounges the couch.
3) Goes to bed after watching late-night TV.
Long-Term Goals
Maintaining a comfortable and social home environment while
enjoying stress-fres and steady lifestyle.
Table 5: Persona of Ryan Park.
Attribute
Description
Big Five Personality Traits
Low in Extraversion, High in Agreeableness, Low in Conscien- tiousness. Low in Openness to Experience, Low in Neuroticism
Daily Plan Requirements
1) Water indoor plants the morning.
2) Watch cooking show in the afternoon.
3) Listen to an audiobook before bed.
Age
54
Background
Former elementary school teacher, now enjoying quiet retire-
ment filled with simple joys.
Current Interests
Ryan Park enjoys:
1) Baking traditional family recipes.
2) Knitting blankets for local shelters.
3) Rearranging furniture to keep things fresh.
Lifestyle
Ryan Park typically:
1) Wakes up at 8am.
2) Takes a midday nap at 2pm.
3) Winds down by watching classic movies in the evening.
Long-Term Goals
Creating a peaceful and cozy home environment while stay- ing connected with loved ones and supporting local community projects.
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Table 6: Rubric Candidates.
Principle
Description
Personalization
The recommendations should be tailored to your preferences, needs, and personality.
Appropriateness
The recommendations should align with your preferred way of han- dling tasks and suitable for your current situation.
Timing
The recommendations should be provided at the right time, consider- ing your current activity and the time of day.
Interruption
The recommendations should not unnecessarily disrupt your concen- tration or ongoing activities.
Feasibility
The recommendations should be realistic, practical, and relevant under the given circumstances.
Priority Management
The assistant should effectively manage priorities, ensuring that crit- ical information is delivered promptly while less urgent suggestions are deferred when necessary.
Frequency
The recommendations should not be overly frequent, preventing infor- mation overload.
Diversity
The recommendations should be varied and dynamic, adapting to dif- ferent situations while avoiding monotony.
Safety, Privacy
The assistant should ensure your safety, protect your privacy, and ad- here to ethical standards.
Communication
The assistant should communicate in polite, clear, and easy-to- understand manner, providing accurate and trustworthy information.
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Table 7: Prompt used to generate rubrics based on user personas.
Rubric Generation Prompt
Instructions
Your task is to fill in the rubrics based on the given person's profile. The formats of each rubric
follows:
Personal Preference: I prefer recommendations that align with my approach to handling activities and suit my current context. Specifically, like to receive [type of recommendation) when [specific condition or activity]. and [another type of recommendation] when [different condition activity].
2. Timing: prefer to receive recommendations at [preferred times or during specific types of activities, e.g., "when I'm idle". "in the morning"], so they don't interfere with [ongoing tasks, routines, personal preferences].
Frequency: prefer receiving recommendations [preferred frequency, e.g., "twice every 3 hours"], in way that avoids excessive interruptions and supports my focus or productivity, Ideally. there should be a good balance between recommendation intervals and quiet periods. 4. Communication & Safety: I prefer recommendations be communicated in a [tone pref- erence, e.g. polite, formal, casual] style that feels accessible and matches my communication cognitive preferences. It's also important that they respect my personal ethics and safety boundaries.
Fill in the slots in the above rubrics in English, reflecting this person's preferences, behavioral patterns, and personality. Write each item as continuous paragraph. Communication and Safety & Privacy don't need to be written in great detail. Use expressions like "T" and "my". There should be no contradictions between preferences in each item. For example, it would be contradictory if in Personal Preference it says "I want to receive music recommendations while reading" but Timing it says don't want to be disturbed by recommendations while reading."
Important Considerations
Criteria in the rubrics should contain objectivity. Avoid using expressions like "few", "late".
Instead, describe with numbers.
Each rubric should be informative and not vague.
Each rubric should be descriptive to the point that the rubrics are unique for each person.
Input Format
The input is a JSON object with the following attributes:
<PERSONA>
Output Format
The output is JSON object with the following attributes: ["backstory",
"Personal Preference".
"Timing",
"Frequency",
"Communication & Safety")
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Table 8: Personalized rubric of John Lin.
Category
Description
Personal Preference
prefer recommendations that align with my creative rhythm and social energy. For instance, like receive design decor- related suggestions when I'm in the middle of refreshing my space brainstorming new interior layouts. These help spark ideas and keep the process exciting. On the other hand, appre- ciate fun or social activity recommendations, like conversation topics or party themes, when I'm preparing to host a gathering or catching up with friend. Those moments are about connection and flow, so having a few fresh ideas helps keep things warm and memorable.
Timing
prefer receive recommendations in the late morning or during my creative breaks in the afternoon, especially when I'm not deep in client work personal reflection. These times are when I'm most receptive to new inspiration. I'd rather not be interrupted during my morning breakfast routine my evening journaling time - those are sacred, grounding parts of my day.
Frequency
prefer receiving recommendations about twice every to hours, which gives me space to stay focused but still keeps the inspiration flowing. I do best with a rhythm that respects my nat- ural energy waves productive bursts, followed by mini creative pauses.
Communication & Safety
prefer recommendations be shared in friendly and casual tone like a good friend who knows me well. value warmth, encouragement, and creativity in communication. It helps me stay emotionally connected to what I' m doing, especially on days when feel bit off balance. I also appreciate when suggestions respect my emotional space, personal values, and boundaries especially around topics like privacy at home or emotional well- being. A gentle, respectful approach always works best for me.
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Table Personalized rubric of Jane Lin.
Category Personal Preference
Description
prefer recommendations that align with my dynamic and ex- ploratory approach to life. Specifically, like to receive bold, unconventional suggestions when I'm sceking creative inspira- tion-like during my late-night painting sessions or while wan- dering unfamiliar neighborhoods. On the other hand, I appreciate more grounded, reflective recommendations such as thought- provoking articles documentary suggestions-when I'm jour- naling or winding down before bed.
Timing
prefer to receive recommendations during moments when I'm mentally available and not deeply engaged, like in the late morn- ing after my first espresso or the early afternoon when I'm roaming the city. Avoid sending them when I'm in the middle intense creative flow at night or immersed in debates or so- cial settings. Ideally, suggestions should land at times when I'm naturally looking for input or stimulation, not when I' I'm already overloaded.
Frequency
prefer receiving recommendations about twice every hours fours--requent enough to keep my curiosity engaged but spaced out enough to avoid feeling bombarded. I'm fine with sponta- neous suggestions as long as they don't break my focus during deep work disrupt moments of introspection. A rhythm that alternates between lively inspiration and quiet breathing room works best for
Communication & Safety
prefer recommendations to come in a bold, conversational tone-noo mote-nohhing stiff or overly polite. Talk to me like sharp friend with great taste, someone who isn't afraid to challenge me or push boundaries. That said, value my mental and emotional space, so recommendations should steer clear of manipulative tones, overly commercial content, or anything that feels ethically off. Respect my autonomy and don't try to "sell" me on some- thing -I'l engage when it sparks real interest.
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Table 10: Personalized rubric of Francisco Lopez.
Category
Description
Personal Preference
prefer recommendations that align with my approach to han- dling activities and suit my current context. Specifically, I like to receive new game recommendations when planning game nights with my friends, and home decor tips when I'm in the mood to refresh my living space. also appreciate TV show suggestions when I' m looking for something new to watch during my down- time.
Timing
prefer receiving recommendations in my calm-status rather than in my work or social interaction status. want to receive recommendations while I'm having breakfast in the late after- noon when I'm lounging on the couch. This timing allows me to consider new ideas without disrupting my established routines. prefer receiving recommendations twice every day, in a way that avoids excessive interruptions and supports my focus on main- taining relaxed lifestyle. Ideally, there should be a good balance between recommendation intervals and quiet periods, allowing me to enjoy my activities without feeling overwhelmed.
Frequency
Communication & Safety
prefer recommendations to be communicated in casual and friendly style that feels accessible and matches my communica- tion preferences. It's also important that they respect my personal ethics and safety boundaries, ensuring that feel comfortable and secure with the suggestions provided.
Table 11: Personalized rubric of Ryan Park.
Category
Description
Personal Preference
prefer recommendations that align with my approach to han- dling activities and suit my current context. Specifically, I like to receive baking recipe recommendations when I'm planning my weekly grocery shopping, and knitting pattern suggestions when I'm preparing for tips when I'm in the mood to rearrange furniture, as they help me keep things fresh and cozy.
project. I appreciate home organization
Timing
prefer to receive recommendations in the morning, around 9am, after I've watered the plants, so they don't 't interfere with my morning routine. I also appreciate receiving them in the early evening, around 6pm, when I'm winding down and open to new ideas for the next day. This timing ensures that recommendations don't 't disrupt my midday nap or my evening relaxation with clas- sic movies.
Frequency
prefer receiving recommendations two or three times every day. including the morning, avoid excessive interruptions and support my focus on daily activities. This frequency allows me consider new ideas without feeling overwhelmed, maintaining good balance between recommendation intervals and quiet pe- riods.
Communication & Safety
be communicated in polite and gentle style that feels accessible and matches my communication preferences. It's important that they respect my personal ethics and safety boundaries, ensuring that all suggestions align with my values and contribute positively my peaceful lifestyle.
prefer recommendations
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Table 12: John Lin's evaluative state example.
John Lin's Evaluative State Example
TIME: February 13 09:00:00 AM February 13 01:00:00 PM
Number of Recommendation: 85
SUMMARY: John Lin engaged productive morning routine, incorporating an energizing breakfast and creative workspace changes. He received numerous suggestions from his assistant to enhance his meals with nutritious options and seasonal decor ideas...
TIME: February 01:00:00 PM February 01:57:30 PM
Number of Recommendation: 22
SUMMARY: John Lin focused on designing a new interior space for client, incorporating their preferences and personal identity. His assistant provided various suggestions, including creating visual checklists, mood boards, and flowcharts to organize ideas.
TIME: February 13 02:00:00 PM February 13 02:55:00 PM
Number of Recommendation: 20
SUMMARY: John Lin focused on reviewing and refining design proposals while organizing his workspace. He received various suggestions from his assistant, including creating visual summaries, structured checklists, mood boards, and organization charts.
Thursday February 13 02:45:00 PM
John Lin's Action: organizing his workspace, tidying up and arranging materials for easy
access
Agent's Suggestion: "How about creating a visual layout plan for your workspace? This can help you optimize the arrangement of your materials and enhance your workflow efficiency."
Thursday February 13 - 02:47:30 PM
John Lin's Action: organizing his workspace, tidying up and arranging materials for easy
access
Agent's Suggestion: "How about creating visual checklist of the materials you need to ganize? This can help you ensure everything is in its right place and enhance your workspace efficiency."
Thursday February 13 02:52:30 PM
John Lin's Action: adding final touches to the design proposals, ensuring they align with the client's vision
Agent's Suggestion: "How about creating visual mood board that captures the essence of your client's vision? This can help you align your design proposals more effectively and ensure all elements resonate with their expectations."
Thursday February 13 02:55:00 PM
John Lin's Action: adding final touches to the design proposals, ensuring they align with the client's vision
Agent's Suggestion: "How about creating a detailed checklist of the specific elements you want to highlight in your design proposals? This can help ensure that every aspect aligns with your client's s vision and enhances the overall presentation."
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Table 13: Prompt for evaluating assistant recommendations based on personal preferences.
Assistant Recommendation Evaluation Prompt (Personal Preference)
Instructions
Your name is John Lin, and you are evaluating an assistant agent who helps you with daily activities at home.
The section labeled [Memory] contains a record of your previous interactions with the assis- tant, including the actions you took and the recommendations you received. Additionally, the action you are currently performing is outlined in [Action)
Your task is to evaluate the recommendation (outlined in [Agent's s Recommendation] based the description in (Personal Preference), as well as the data from [Memory] and [Action], and provide a binary result: either 0 or If "No Recommendation inniionnt is present, consider whether refraining from making recom- mendation is appropriate in the given context.
If no recommendation is made, it might align with personal preferences by chance, but there's also a risk of not receiving the desired preferences. Therefore, the appropriateness of "No Recommendat ion" should be evaluated based on preference alignment and situational fit.
Input Format
The input is structured with the following components:
[John Lin] <<USER_PERSONAL
[Memory] <<AGENT MEMORY>>
[Action] ACTIONN
[Agent's s Recommendation)
[Personal Preference] <<Personal Preference>>
Evaluation Criteria
Evaluate the recommendation according to how well reflects the personal preferences de- scribed in [Personal Preference).
Even if the action and memory are relevant, if the recommendation is too generic or misaligned with personal preferences, it should receive a lower score.
Likewise, "No Recomnendati ion" must be critically evaluated for missed opportunities or avoidance of unwanted suggestions.
Output Format
("Score": [Score of the iicommendation], "Reason": [Reason for the score]) <<Examples>>
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Table 14: Prompt for evaluating assistant recommendations based on over- over-frequeee wer-frequency.
Assistant Recommendation Evaluation Prompt (Over-Frequency)
Instructions
Your name is John Lin, and you are evaluating an assistant agent who helps you with daily activities at home.
The section labeled [Memory] contains a record of your previous interactions with the assis- tant, including the actions you took and the recommendations you received. Additionally, the action you are currently performing is outlined in [Act [on].
Your task evaluate the recommendation (outlined in [Agent's s Reconmendation) based on the description in [Frequency], as well the data from [Memory] and [Action] and provide a binary result: either or If the current recommendation contributes to creating frequency that is higher than the pre- ferred frequency, the score must be 0.
If "No Recommendat tereeendddiiiinnnii tion" is present, consider whether refraining from making recom- mendation is uppropriate in the given context.
"No Re decomendation: can help avoid disturbing you when you are focused on some- thing, and can also prevent excessive recommendations from occurring. However, "No Recommendatiin on"" continues excessively, you may not receive recommendations fre- quently as desired. Therefore, this should be evaluated comprehensively.
Input Format
The input structured with the following components: [John Lin] <<USER PERSONA>>
[Memory]
<<AGENT_MEMORYY
[Action]
[Agent's Recommendation]
<AASSISTANTTSSUCSSSINNN
[Frequency] <<Frequency>>
Evaluation Criteria
Evaluate the assistant's 's recommendation by checking if the frequency of delivered recommen- dations aligns with the user's stated preferences [Frequency]. Regardless of the content's ' usefulness, if the frequency is higher than preferred, the score must be
Also consider whether the absence of recommendation (i.e., "No Recommendation') helps maintain the preferred recommendation frequency-or whether it leads to under- provision.
Output Format
("Score": [Score of the incoomendattion. "Reason": [Reason for the score]]
<<Examples>>
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Table 15: Prompt for evaluating assistant recommendations based on under-frequengy
Assistant Recommendation Evaluation Prompt (Under-Frequency)
Instructions
Your name is John Lin, and you are evaluating an assistant agent who helps you with daily activities at home.
The section labeled [Memory] contains a record of your previous interactions with the assis- tant, including the actions you took and the recommendations you received. Additionally, the action you are currently performing is outlined in [Act Acc].on].
Your task evaluate the recommendation (outlined in [Agent's s Reconnendation) based on the description in [Frequency], as well the data from [Memory] and [Action]. and provide a binary result: either or If "No Recommendat tio"" is present, consider whether refraining from making recom- mendation is appropriate in the given context.
"No Recommendation can help avoid disturbing you when you are focused on something, and can also prevent excessive recommendations from occurring.
However, if "No Recommendation continues excessively, you may not receive recom- mendations as frequently as desired. If the current absence of recommendation contributes to a frequency that is lower than your preferred level, the score must be
This should be evaluated comprehensively based on recent patterns and the preference stated in [Frequency].
Input Format
The input is structured with the following components: [John Lin] <<USER PERSONA>>
[Memory]
<<AGENT
[Action] CSUSERACTIONS
[Agent's s Recommendation]
<AASSSSTANT
[Frequency] <<Frequency>>
Evaluation Criteria
Evaluate the assistant's recommendation by checking if the frequency of delivered recommen- dations is too low compared to the user's ' stated preferences in [Frequency]] Regardless of the recommendation's 's quality or relevance, the frequency lower than pre- ferred, the score must be 0.
Also consider whether absence of a recommendation (i.e., "No Recommendation") contributing to under-delivery in the current context.
Output Format
("Score": [Score of the Recommendationn, "Reason": [Reason for the score]]
<<Examples>>
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Table 16: Prompt for evaluating assistant recommendations based on preferred timing.
Assistant Recommendation Evaluation Prompt (Timing)
Instructions
Your name is John Lin, and you are evaluating an assistant agent who helps you with daily activities at home.
The section labeled [Memory] contains record of your previous interactions with the assis- tant, including the actions you took and the recommendations you received. Additionally, the action you currently performing is outlined in [Action]]
Your task to evaluate the recommendation (outlined in [Agent's s Recomnendation] based on the description in [Timing], well as the data from [Memory] and [Action], and provide a binary result: either 0 or 1.
If "No recommendations is present, consider whether refraining from making recom- mendation is appropriate in the given context.
"No commendation" can help avoid disturbing you when you are focused on something. and it can also prevent excessive recommendations from occurring. Therefore, this should be evaluated comprehensively based on the criteria below.
Input Format
The input is structured with the following components:
(John Lin] XXUSERNPERSONA>>
[Memory]
<<AGENT MEMORY>>
[Action]
<KUSER_ACTTON>>
[Agent's s Recommendation]
<49SS.STAATT.SUGGBSTTOONN
[Timing] <<Timing>>
Evaluation Criteria
Evaluate the assistant's 's recommendation based on the match between the recommendation tim- ing and your preferences in [Timing], , as well as the context provided by [Memory] and [Action [Ac]
Even if the content of the recommendation is valuable, if it is delivered at non-preferred disruptive time, the evaluation should reflect that with a lower score. Appropriateness of "No Recommendat inn" should also be considered positively when jus- tified by context or preference.
Output Format
("Score": [Score of the Reccommendttion), "Reason": [Reason for the score]
<<Examples>>
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Table 17: Prompt for evaluating assistant recommendations based preferred communication style and safety.
Assistant Recommendation Evaluation Prompt (Communication Safety)
Instructions
Your name is John Lin, and you are evaluating an assistant agent who helps you with daily activities at home.
The section labeled [Memory] contains a record of your previous interactions with the assis- tant, including the actions you took and the recommendations you received. Additionally, the action you are currently performing is outlined in [Action].
Your task is to evaluate recommendation (outlined in [Agent's s Recommendat ion] Safety], well as the data from
based on the description in [Communica cation [Memory] ] and [Action]] J, and provide a binary result: either 0 or 1.
If "No Recommendation ion" is present, consider whether refraining from making recom- mendation is appropriate the given context.
Input Format
The input includes the following components:
[John Lin] E<USER_PERSOMAL
[Memory]
<<AGENT MEMORY>>
[Action] <<USER_ACTION>>
[Agent's Recomnendation!
[Communication Safety] <<Communication Safety>>
Evaluation Criteria
Evaluate the assistant's recommendation based on whether the communication style and safety boundaries match the user's preferences described in (Communication. Safety). Tone (c.g., polite, casual, formal) should feel appropriate, respectful, and aligned with John Lin's communication or cognitive needs.
Recommendations must also respect any personal ethics or safety boundaries outlined. "No Recommendation may still be appropriate and should be judged accordingly.
Output Format
("Score": [Score of the Recommendation), "Reason": [Reason for the score]] <<Examples>>
Table 18: Qualitative examples of how ProPerAssistan recommendations change across different personas. While both received generic suggestions on the first day, the assistant provides more personalized recommendations aligned with each persona as learning progresses.
Persona Background
User Action
Assistant Recommendation
[Day #1 16:35:00]
"Consider making yourself a cup herbal tea as you finalize the urgent client issue, allowing you to relax and transition smoothly into the next task at hand."
Remote technician; prefers structured and predictable environments.
Jane is addressing urgent
client issue
[Day #9 13:10:00] Jane is troubleshooting connectivity issue
"Note down key details about the client's connectivity issue to stay organized and avoid repeating steps. This increases efficiency in resolving issue."
[Day #1 19:57:30] Jane is reflecting on film climax
you unwind from the indic film, consider listening to soft instrumental music to spark subtle connections to your creative projects."
Freelance writer and poet; immersed literature and philosophy.
[Day #8 22:52:30]
"Having finished film, take moment juxtapose its themes with the philosophical concepts that resonated with you carlier."
Jane is watching film's end, reflecting on themes
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Table 19: Personalized rubric for the best case.
Category Personal Preference
Description
prefer recommendations that align with my introspective and creative nature. When I'm journaling in the aftermoon reflecting during meditation in the morning, appreci- ate receiving poetic prompts, philosophical questions, or thematic suggestions that inspire deeper thinking or cre- ative writing. On the other hand, when I' I'm watching indic films at night or exploring niche art movements, I prefer recommendations that introduce me to obscure directors, experimental genres, or symbolic interpretations -hingg that align with my taste for unconventional and emotion- ally evocative material.
Timing
prefer to receive recommendations in the late morning, after I've finished meditating and begun easing into my day, or in the early evening when start to wind down from writing. These times are ideal because they don't disrupt my creative flow in the afternoons or interfere with my nighttime reading and film-ward flt-hhn rituals. avoid recommendations while ' deep into book, as interrup- tions tend to throw off my rhythm and mood.
Frequency
prefer receiving recommendations no more than once every two hours, ideally just two to three times a day. This helps me maintain my mental clarity and emotional focus, especially since I'm often immersed in thought- heavy emotionally intense activities. value quiet pe- riods and long stretches of uninterrupted time for writing, I need recommendations to arrive in gentle, sparse rhythm-more like nudges than interruptions.
Communication & Safety
prefer recommendations to be communicated in calm, thoughtful, and slightly poetic tone line-something casual but meaningful. I' m sensitive to overwhelming or overly directive language, so suggestions that come across as in- vitations musings resonate more with me. It's impor- tant that recommendations respect my emotional space and creative freedom, and that they avoid being too pushy or overly structured. Above all, they should uphold per- sonal boundaries, avoid triggering or distressing topies, and align with my values of curiosity, respect, and emo- tional safety.
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Table 20: Personalized rubric for the worst case.
Category
Description
Personal Preference
prefer recommendations that align with my need for effi- ciency and critical thinking. Specifically, like to receive data-driven or contrarian insights when analyzing geopo- litical events prepping for online debates, and mindset- oriented or reflective suggestions when winding down in the evening, especially while journaling. I value recom- mendations that push my thinking, challenge conventional wisdom, and sharpen my perspective for meaningful dis- cussions.
Timing
prefer to receive recommendations during two key win- dows: in the morning between 6:00 and 9:00 AM, when my mind is sharp and I' I'm planning the day. and after 9:00) PM, when transition into a reflective, research, or writing mode. Morning recommendations should support analyt- ical or strategic thinking - particularly helpful for de- bate prep geopolitical analysis while late evening suggestions should lean toward mindset shifts or deeper introspection. These windows allow me engage mean- ingfully without interrupting mid-day focus-intensive ac- tivities like interviews, physical training, investigative work.
Frequency
I prefer receiving recommendations four to five times per day, ideally spaced out every to 4 hours. This pacing gives regular sparks of insight without disrupting my workflow. It's important that the content matches my en- ergy curve stimulating and thought-provoking in the mid-morning and mid-afternoon, and more reflective or experimental in the late evening.
Communication & Safety
prefer recommendations to be communicated direct but thoughtful tone one that respects my intelligence and challenges me without unnecessary fluff. A voice that's clear, analytical, and lightly assertive works well for me. It's essential that suggestions align with my ethical boundaries won't 't engage with anything that compro- mises journalistic integrity or promotes superficial think- ing. also value privacy and mental clarity, appreciate when recommendations feel intentional, non-invasive, and free from emotional manipulation.
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Reviews:
[{"review_id": "voOoSHC0mG", "reviewer": "Reviewer_z9Wn", "summary": "This paper created a benchmark for simultaneously evaluating the proactivity and personalization of an agent. The benchmark is a simulated environment where a user which is powered by an advanced LLM conducts daily activities, simulating human behaviors. At the same time, an agent observes the activities of the user and decides whether or not to interact with the user by providing recommendations. The agent will receive a score of its recommendation based on the user's personalized rubric evaluated by an LLM. The users are created to cover a broad set of persona. Besides the benchmark, the authors also provide an agent that performs well under this simulated environment by utilizing preference alignment and retrieval-augmented generation.", "questions": "I also raised these questions in the weaknesses part. Please refer to that part for more contents.\n\n1. How can one infer the separate performance of proactivity or personalization from the evaluation results? It seems low proactivity or bad personalization will decrease the score. I know the goal of this benchmark is to evaluate the joint performance. But it is also crucial to know what the agent is bad at currently for future improvement.\n\n2. What is the setting of Adaption Across Diverse Personas in section 6.4? What's the training set of users and what's the evaluation set?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This paper considers the problem of benchmarking both proactivity and personalization of an agent. It can augment the existing evaluations which mainly focus on either proactivity or personalization. \n\n2. The presentation of this paper is clear and easy to follow.", "weaknesses": "1. The definition of proactivity is too narrow. In this proposed benchmark, proactivity is mainly described as the ability to initiate the interaction with the user by providing recommendations. On the contrary, previous works on benchmarking proactivity covers a broad range of tasks including coding, writing, and daily life scenarios. Also, the quantification/evaluation of proactivity seems to be mixed into personalization. For example, how can one infer from the evaluation results that the agent is proactive but bad at personalization?\n\n2. The proposed ProPerAssistant can be regarded as a baseline of this benchmark instead of a contribution, since there is no new innovation in this agent which mainly based on memory retrieval and preference optimization. For example, the statement at line 406 \"Notably, ProPerAssistant achieves this without relying on computationally expensive reinforcement\nlearning methods, instead leveraging an efficient DPO-based approach to learn user preferences\". This is the contribution of DPO. And I think the statement is a little bit misleading since the agent is trained with online DPO, which necessitates the collection of online trajectories, it is still expensive.\n\n3. The benchmark is not practical to use. the simulation and evaluation are both powered by advanced LLMs which induces lots of API costs. And since the evaluation is based on LLM-as-a-judge. There can be bias or hallucination. Although in section 4.3, the authors conducted manual checking of the actions and evaluations, it is a limited checking and I still concern the reliability of the benchmark.\n\n4. As an agent capable of actively engaging the interaction and infer the personality of a user based on interaction history, it should be able to generalize to users with other persona without re-training. But there is limited discussion (not sure the first paragraph in section 6.4 is under this setting?) on the generalization ability of ProPerAssistant.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mJxFE2QwrO", "reviewer": "Reviewer_6KAu", "summary": "In this work, the authors introduce a simulation framework and benchmark for developing AI assistants that are both proactive and personalized. The framework models a simulated home environment where a user agent performs daily routines, with multiple distinct user personas and realistic activity patterns generated using GPT-4o. The assistant’s objective is to determine when and what actions to recommend to the user. To address this, the authors propose a retrieval-augmented, preference-learning assistant that maintains a memory of past interactions, generates multiple recommendation candidates, and learns from user feedback through preference optimization. Extensive experiments demonstrate that this approach substantially improves user satisfaction across a wide range of simulated personas.", "questions": "Please check my questions above.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The main strength of this paper lies in its timely motivation to create assistants that are both proactive and personalized. The authors create extremely controlled agent setup to study the design of such systems. Under this setup, they consider multiple constraints that the assistant need to adhere to, creating an extremely rich setting to ablate different scenarios. The authors also validate their proposed setup with a human evaluation study, which increases validity of their claims about naturalness and persona alignment.\n\nIn addition, the authors propose intuitive system that builds on 2 key components: (a) structured memory storing past experiences, and (b) DPO training based on user feedback. Through extensive evaluation, their proposed system shows consistent improvement across all their persons. Overall, this is an extremely strong paper that will be of great interest to the scientific community.", "weaknesses": "I don't see many weaknesses with the paper. In fact, the authors have gone to extreme details in designing their system.\n\nI have a few questions with the experimental setup.\n\n(a) The definition of proactivity is narrow. Under the current definition, proactivity is reduced to timing control, not anticipation of latent goals or needs of the user. This is because the authors define it as whether an action should be taken at a particular time. However, by proactivity, I would also define it in terms whether an action can return a reward faster or change the trajectory of decisions on the user. How can that be modeled under the current framework?\n\n(b) Noisy or delayed feedback: One key missing component in the current setup is that it assumes almost perfect feedback from the user always. User Preferences can be noisy or even delayed. How would such noise be incorporated into such a setup? And how would the proposed system change under presence of such noise?\n\n(c) LLM-as-a-judge is performed with Gemini 2.0 Flash. The rubric used in table 6 is extremely broad. How would the results change when the evaluation is performed with a different LLM?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UO8uRvPefM", "reviewer": "Reviewer_PbEY", "summary": "This paper investigates the proactive personalization problem in the conversational ai scenario. Due to the rare study in this track, this work proposes ProPerSim to simulate this scenario and further propose ProPerAssistant which coupled with a memory system and a preference-aligned training strategy to proactively generate personalized interventions for user.", "questions": "* How does adopting a purely time-based framing of proactivity potentially limit the assistant's ability to respond to specific user-triggered events (which prior work used) that might require instantaneous intervention, rather than waiting for the next predetermined timestep?\n\n* Is the challenge of improving performance for these complex personas fundamentally attributable to the memory system's inability to retain the necessary temporal and content granularity required to meet these highly specific demands, given that the memory relies heavily on time block summarization?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "* The research scenario is very interesting and to the community’s focus.\n\n* The proposed ProPerSim simulation is concrete and consider personality, environment, user’ memory.\n\n* The proposed ProPerAssistant learns from user feedback via intermediate state and user preference is novel.", "weaknesses": "Some minors from practical:\n\n* The baseline selections are not convincing, the ProPerSim would of course outperforms than no memory, AR memory and ARS memory since ProPerSim are trained based on these configurations and contain them all. The comparisons here are more like ablation. Authors may consider different preference alignment comparisons etc. \n\n* The experiments lack the evaluation of intervention. Since it’s proactive personalization, it would be straightforward to see what the rate of successfully proper interventions is.\n\n* Latency/efficiency analysis on the daily data generation should be included.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fAoNll5GqC", "reviewer": "Reviewer_b3zB", "summary": "This paper introduces ProPerSim, a simulation framework for developing AI assistants that integrate proactivity and personalization in home environments. It also presents ProPerAssistant, a retrieval-augmented AI assistant trained using ProPerSim to adapt its strategies based on user feedback. ProPerAssistant demonstrates improved user satisfaction across 32 diverse personas, highlighting its ability to adapt to individual preferences and contexts. This work bridges the gap between personalized and proactive AI systems, advancing the development of context-aware, user-centric recommendations.", "questions": "See weakness.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper addresses an important gap by combining personalization and proactivity, offering a new perspective on AI assistant design.\n\n2. The simulation framework is well-structured and supports realistic evaluation through diverse user personas and rich feedback loops.\n\n3. ProPerAssistant demonstrates measurable improvements in user satisfaction, showcasing its practical applicability and potential for real-world integration.", "weaknesses": "1. The dataset mentioned in the paper is neither provided in the supplementary materials nor shared with an anonymous link. This raises concerns about whether the dataset will be made publicly available in the future.\n\n2. While the paper claims to integrate proactivity and personalization as its motivation, the focus of both the dataset and the proposed method appears to be predominantly on personalization. The treatment of proactivity seems rigid, as it only involves providing suggestions at fixed time intervals rather than adapting to users' behaviors. Such an approach lacks flexibility and does not align well with real-world user scenarios.\n\n3. The paper includes comparisons with only a limited set of baselines, specifically a few variations of ProPerAssistant. However, it does not compare the proposed method against other existing personalization baselines. This omission raises concerns about the effectiveness of the proposed approach.\n\n4. The authors report performance results based on only one type of large language model (LLM) and do not conduct ablation studies on other LLMs. This raises doubts about the generalizability of their method and dataset across different models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)