Paper deep dive
SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data
Zenghui Zhou, Xiaoyang Li, Xiaoxuan Qiao, Zhilang Wei, Tianming Lei
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 91%
Last extracted: 7/24/2026, 1:53:40 AM
Summary
The paper introduces SenWorld, a digital-twin simulation framework designed to generate context-rich evaluation data for smartphone personal assistants. It utilizes deterministic, event-sourced simulations based on real-world map, weather, and network data to create personas that interact in a simulated environment. The system archives full-system snapshots to provide ground-truth labels for evaluation cases without relying on LLM judges or post-hoc annotation. Evaluated with 16 personas in Beijing, the generated data closely matches real-user benchmarks in distribution and rhythm, successfully exposing failures in a production assistant primarily related to call and SMS records.
Entities (6)
Relation Signals (9)
SenWorld → generates → Context-Rich Evaluation Data
confidence 95% · SenWorld... generates such data with ground truth fixed by construction.
SenWorld → evaluates → Smartphone Personal Assistants
confidence 93% · evaluating them requires context-rich evaluation data... SenWorld... generates such data
SenWorld → uses → Full-System Snapshots
confidence 92% · every observable signal is archived in full-system snapshots
SenWorld → exposes → Assistant Failures
confidence 90% · exposes 78 failures in a production smartphone assistant
SenWorld → uses → Real Map Data
confidence 90% · world built from real map, weather, holiday, and network data
SenWorld → uses → Real Weather Data
confidence 90% · world built from real map, weather, holiday, and network data
Assistant Failures → occursin → Call Records
confidence 88% · concentrating on call and Short Message Service (SMS) records
Assistant Failures → occursin → SMS Records
confidence 88% · concentrating on call and Short Message Service (SMS) records
SenWorld → avoids → LLM Judge
confidence 85% · with no LLM judge involved
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Abstract
Abstract:Smartphone personal assistants reason over longitudinal personal data, yet evaluating them requires context-rich evaluation data whose correct answers are known, and real device traces are too privacy-sensitive to share. To address this challenge, we present SenWorld, a physically grounded, deterministic, event-sourced digital-twin simulation that generates such data with ground truth fixed by construction. In SenWorld, personas live through a full day in a world built from real map, weather, holiday, and network data; every observable signal is archived in full-system snapshots; and each evaluation case is labeled by a pointer to an existing record rather than by post-hoc annotation or a large language model (LLM) judge. We evaluate this method with 16 personas in Beijing. The generated data closely matches the held-out real-user benchmark in category distribution (Jensen--Shannon divergence (JSD) 0.070) and in the daily rhythm of communication records (JSD below 0.1), though generated records remain shorter than real ones. Without scripted interaction, personas form a fully reciprocated dialogue subgraph and differentiated behavioral repertoires. Projected into 717 evaluation cases, the generated data exposes 78 failures in a production smartphone assistant, concentrating on call and Short Message Service (SMS) records while contacts, schedules, and alarms never fail. The snapshot pointer confirms each failure as an assistant-side retrieval error, with no LLM judge involved. Overall, SenWorld offers a privacy-safe, reproducible, and distribution-checked path to evaluation data whose labels are fixed by construction.
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- Source: https://arxiv.org/abs/2607.19949v2
- Canonical: https://arxiv.org/abs/2607.19949v2
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Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2607.19949v2 (cs) This paper has been withdrawn by Zenghui Zhou [Submitted on 22 Jul 2026 (v1), last revised 23 Jul 2026 (this version, v2)] Title:SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data Authors:Zenghui Zhou, Xiaoyang Li, Xiaoxuan Qiao, Zhilang Wei, Tianming Lei View a PDF of the paper titled SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data, by Zenghui Zhou and 4 other authors No PDF available, click to view other formats Abstract:Smartphone personal assistants reason over longitudinal personal data, yet evaluating them requires context-rich evaluation data whose correct answers are known, and real device traces are too privacy-sensitive to share. To address this challenge, we present SenWorld, a physically grounded, deterministic, event-sourced digital-twin simulation that generates such data with ground truth fixed by construction. In SenWorld, personas live through a full day in a world built from real map, weather, holiday, and network data; every observable signal is archived in full-system snapshots; and each evaluation case is labeled by a pointer to an existing record rather than by post-hoc annotation or a large language model (LLM) judge. We evaluate this method with 16 personas in Beijing. The generated data closely matches the held-out real-user benchmark in category distribution (Jensen--Shannon divergence (JSD) 0.070) and in the daily rhythm of communication records (JSD below 0.1), though generated records remain shorter than real ones. Without scripted interaction, personas form a fully reciprocated dialogue subgraph and differentiated behavioral repertoires. Projected into 717 evaluation cases, the generated data exposes 78 failures in a production smartphone assistant, concentrating on call and Short Message Service (SMS) records while contacts, schedules, and alarms never fail. The snapshot pointer confirms each failure as an assistant-side retrieval error, with no LLM judge involved. Overall, SenWorld offers a privacy-safe, reproducible, and distribution-checked path to evaluation data whose labels are fixed by construction. Comments: We are currently undergoing internal approval Subjects: Artificial Intelligence (cs.AI); Computers and Society (cs.CY) Cite as: arXiv:2607.19949 [cs.AI] (or arXiv:2607.19949v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2607.19949 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zenghui Zhou [view email] [v1] Wed, 22 Jul 2026 09:25:47 UTC (595 KB) [v2] Thu, 23 Jul 2026 03:42:11 UTC (1 KB) (withdrawn) Full-text links: Access Paper: View a PDF of the paper titled SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data, by Zenghui Zhou and 4 other authorsWithdrawn No license for this version due to withdrawn Current browse context: cs.AI < prev | next > new | recent | 2026-07 Change to browse by: cs cs.CY References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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