{"ID":2832141,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.07046","arxiv_id":"2512.07046","title":"Minimizing Control Attention:The Linear Gauss-Markov paradigm","abstract":"We revisit the concept of `attention' as a technical term to quantify the effort in calibrating control action based on available data. While Wiener, in his work on Cybernetics, anticipated key principles on prioritizing task-relevant signals, it was not until the late 1990's when Brockett first formulated pertinent optimization problems that have inspired subsequent as well as the present work. `Attention,' as a technical term, is defined so as to quantify the dependence of the control law on the time and space/state coordinate; a control law that is independent of time and space, assuming it meets specifications, requires vanishing attention. In the present work we focus on Linear-Markovian dynamics with Gaussian state uncertainty so as to analyze the structure of minimal-attention control schemes that steer the dynamics between terminal states with Gaussian uncertainty profile.","short_abstract":"We revisit the concept of `attention' as a technical term to quantify the effort in calibrating control action based on available data. While Wiener, in his work on Cybernetics, anticipated key principles on prioritizing task-relevant signals, it was not until the late 1990's when Brockett first formulated pertinent op...","url_abs":"https://arxiv.org/abs/2512.07046","url_pdf":"https://arxiv.org/pdf/2512.07046v1","authors":"[\"Ralph Sabbagh\",\"Asmaa Eldesoukey\",\"Mahmoud Abdelgalil\",\"Tryphon T. Georgiou\"]","published":"2025-12-07T23:46:18Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[]","has_code":false}