OpenNovelty.org

ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
BenchmarkAgent SimulationPersonalizationProactivity
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.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces ProPerSim, a simulation framework combining proactive and personalized AI assistance, and ProPerAssistant, a retrieval-augmented system that learns from user feedback. It resides in the 'Simulation-Based Proactive Assistant Development' leaf alongside two sibling papers. This leaf sits within the broader 'Proactive AI Assistant Architectures and Interaction Paradigms' branch, which contains four leaves totaling approximately nine papers. The taxonomy reveals this is a moderately populated research direction, neither highly crowded nor entirely sparse, suggesting active but not saturated exploration of simulation-driven proactive assistant design.

The taxonomy structure shows neighboring leaves addressing dialogue policy planning, trust-aware systems, and anticipatory agent behavior within the same parent branch. Adjacent branches explore personalized systems through adaptive learning and digital twins, plus domain-specific applications in healthcare and smart environments. ProPerSim bridges the proactive architectures branch with personalization themes from the adjacent branch, occupying a boundary position. The scope notes clarify that this leaf specifically covers simulation-based development and evaluation, excluding purely reactive systems and domain-specific implementations without proactive mechanisms, positioning the work at the intersection of simulation methodology and anticipatory interaction design.

Among thirty candidates examined through semantic search, none clearly refuted any of the three contributions. Contribution A (ProPerSim framework) examined ten candidates with zero refutable matches; Contribution B (ProPerAssistant system) similarly found no overlapping prior work among ten candidates; Contribution C (time-based proactivity formulation) also showed zero refutations across ten examined papers. These statistics suggest that within the limited search scope, the specific combination of simulation-based development, personalization through user feedback, and time-sensitive proactive recommendations appears relatively unexplored. However, the analysis explicitly acknowledges examining only top-K semantic matches rather than exhaustive literature coverage.

Based on the limited thirty-candidate search, the work appears to occupy a distinctive position combining proactivity and personalization through simulation. The taxonomy context reveals moderate activity in simulation-based proactive assistant development, with the paper's boundary position between proactive architectures and personalization frameworks potentially explaining the absence of direct prior work in the examined candidates. The analysis covers semantic similarity and citation expansion but does not claim comprehensive field coverage.

Taxonomy

Core-task Taxonomy Papers
36
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: developing proactive and personalized AI assistants through simulation. The field encompasses diverse approaches organized around five main branches. Proactive AI Assistant Architectures and Interaction Paradigms explore how systems anticipate user needs and initiate helpful actions, often leveraging simulation environments to train assistants before real-world deployment. Personalized AI Systems and Adaptive Learning Frameworks focus on tailoring assistance to individual users through techniques like memory augmentation and adaptive models. Domain-Specific AI Assistant Applications demonstrate how these concepts materialize in healthcare, education, cybersecurity, and other specialized contexts, with works like Digital Twins Healthcare[1] and Dental Education AI[5] illustrating sector-specific implementations. AI-Driven Simulation and Digital Twin Integration emphasizes creating virtual replicas of users or environments to enable safe experimentation and predictive modeling, as seen in AI Digital Twins[10] and In Silico Twins[23]. Finally, Theoretical and Conceptual AI Assistant Frameworks provide foundational perspectives on ambient intelligence and proactive assistance paradigms, including early visions like Ambient Intelligence[9]. Within the simulation-based proactive assistant development cluster, several contrasting themes emerge around how simulation serves assistant design. Some works emphasize using simulated environments primarily for training and evaluation, creating synthetic scenarios where assistants learn to anticipate needs before encountering real users. Others focus on building digital twins of actual users to enable personalized prediction and intervention. ProPerSim[0] sits squarely within this simulation-centric branch, sharing conceptual ground with Proactive Assistants Simulation[16] in its emphasis on using simulated interactions to develop anticipatory capabilities. Compared to Generative Agent Platform[25], which provides broader infrastructure for agent-based simulation, ProPerSim[0] appears more focused on the specific challenge of proactivity through personalized user modeling. The central tension across these works involves balancing simulation fidelity with computational feasibility, and determining how insights from simulated interactions transfer to authentic user experiences.

Claimed Contributions

ProPerSim: A simulation framework for proactive and personalized AI assistants

The authors present ProPerSim, a simulation-based task and benchmark that combines proactivity and personalization for AI assistants. The framework features a user agent with a rich persona interacting with an assistant in a home environment, where the assistant learns from user ratings to improve recommendations over time.

10 retrieved papers
ProPerAssistant: A retrieval-augmented, preference-aligned assistant

The authors introduce ProPerAssistant, an assistant that uses retrieval-augmented generation and Direct Preference Optimization to align with user preferences. It maintains an evolving internal state and continuously learns from user feedback to deliver contextually relevant and personalized recommendations.

10 retrieved papers
Time-based formulation of proactivity in AI assistants

The authors formalize proactivity based on discrete time intervals rather than user-triggered events. This time-based approach enables the assistant to make intervention decisions at regular intervals, more closely mimicking real-time human assistant behavior.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ProPerSim: A simulation framework for proactive and personalized AI assistants

The authors present ProPerSim, a simulation-based task and benchmark that combines proactivity and personalization for AI assistants. The framework features a user agent with a rich persona interacting with an assistant in a home environment, where the assistant learns from user ratings to improve recommendations over time.

Contribution

ProPerAssistant: A retrieval-augmented, preference-aligned assistant

The authors introduce ProPerAssistant, an assistant that uses retrieval-augmented generation and Direct Preference Optimization to align with user preferences. It maintains an evolving internal state and continuously learns from user feedback to deliver contextually relevant and personalized recommendations.

Contribution

Time-based formulation of proactivity in AI assistants

The authors formalize proactivity based on discrete time intervals rather than user-triggered events. This time-based approach enables the assistant to make intervention decisions at regular intervals, more closely mimicking real-time human assistant behavior.

Table of Contents