ASPIRE: An Agentic System-Identification Platform for Iterative Reasoning and Exploration
Overview
This paper introduces ASPIRE, a framework for nonlinear system identification where a large language model agent autonomously selects and executes identification methods. The system constructs closed-form models from measured data, optionally supplemented by physical descriptions and requirements. Evaluated on three systems (mobile robot, rotational system with dry friction, laboratory fermenter) across 30 runs, ASPIRE produced valid models at a cost of $0.96 to $3.38 per run. An author assessment found that model interpretations and documentation satisfied 132 of 150 items for recommended models, though nine models contained inaccurate or unsupported claims about structure selection.
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Control: reported autonomous
“red data, optionally supplemented by a system description, the intended use of the model, and physical requirements expressed formally or in natural language. Each model includes its parameters, an estimation record, an interpretation of its terms, and an error evaluated on test data inaccessible to the agent. Formal physical requirements are verified independently. ASPIRE operates autonomously or interactively, allo”
Exact source · revision 1Source owner not reported
Reported quantity · dollars per run: 0.96 other · Basis not reported
“wing the agent to ask questions and the user to request further analyses and identification rounds. We evaluate ASPIRE on three measured systems: a mobile robot, a rotational system with dry friction, and a laboratory fermenter. Each system is tested with and without a description, in five runs per task. All 30 runs delivered valid models, at a cost of 0.96 to 3.38 US dollars per run. An assessment by the authors fou”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalASPIRE: An Agentic System-Identification Platform for Iterative Reasoning and Exploration
This paper introduces ASPIRE, a framework for nonlinear system identification where a large language model agent autonomously selects and executes identification methods. The system constructs closed-form models from measured data, optionally supplemented by physical descriptions and requirements. Evaluated on three systems (mobile robot, rotational system with dry friction, laboratory fermenter) across 30 runs, ASPIRE produced valid models at a cost of $0.96 to $3.38 per run. An author assessment found that model interpretations and documentation satisfied 132 of 150 items for recommended models, though nine models contained inaccurate or unsupported claims about structure selection.
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