TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
TwinBI is an agentic digital twin framework that couples an LLM-based agent system with an executable BI dashboard state to unify conversational interaction, dashboard manipulation, semantic grounding, and provenance tracking. In A/B tests, it improved exact-match accuracy from 43.3% to 63.3%, partial-credit accuracy from 48.3% to 70.8%, and reduced timeout rate from 40.0% to 10.0%. A usability study confirmed benefits in task accuracy and workload.
[2606.13731] TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
[Submitted on 11 Jun 2026]
Title:TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
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Abstract:Business intelligence (BI) increasingly combines dashboard interaction with LLM-based assistance, but these two modes often fall out of sync during multi-step analysis. As users switch between direct dashboard manipulation and natural-language queries, it becomes difficult to preserve a consistent analytical state across filters, hierarchies, metrics, and chart context. We present TwinBI, an agentic digital-twin framework that couples an LLM-based agent system with an executable BI dashboard state. TwinBI unifies conversational interaction, dashboard manipulation, semantic grounding, and provenance tracking through a shared analytical state reconstructed from a unified interaction log. It also exposes artifacts such as schema views, SQL, logs, and an /insights command for state-grounded analytical summaries. We evaluate TwinBI in two complementary ways. In a controlled A/B benchmark with the same backbone agent, TwinBI improves exact-match accuracy from 43.3% to 63.3%, partial-credit accuracy from 48.3% to 70.8%, and substantially reduces timeout rate from 40.0% to 10.0% relative to Dashboard alone. In a usability study, participants benefited from the integrated dashboard-and-chat workflow, with high task accuracy, moderate workload, and favorable ratings for state-aware interaction mechanisms. These results suggest that TwinBI improves both agent-level analytical reliability and user-facing analytical support by turning visible dashboard state into richer actionable context. Our dataset and source code are available at: this https URL
Subjects:
Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2606.13731 [cs.AI]
(or arXiv:2606.13731v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.13731
arXiv-issued DOI via DataCite
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From: Jisoo Jang [view email] [v1] Thu, 11 Jun 2026 11:49:31 UTC (1,502 KB)
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