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Methodol."],"published-print":{"date-parts":[[2026,7,31]]},"abstract":"<jats:p>\n                    With the increasing use of neural policies in control systems, ensuring their safety and reliability has become a critical software engineering task. One prevalent approach to ensuring the safety of neural policies is to deploy programmatic runtime shields alongside them to correct their unsafe commands. However, the programmatic runtime shields synthesized by existing methods are either computationally expensive or insufficiently permissive, resulting in high overhead and unnecessary interventions on the system. To address these challenges, we propose\n                    <jats:sc>Aegis<\/jats:sc>\n                    , a novel framework that synthesizes lightweight and permissive programmatic runtime shields for neural policies.\n                    <jats:sc>Aegis<\/jats:sc>\n                    achieves this by formulating the seeking of a runtime shield as a sketch-based program synthesis problem and proposing a novel method that leverages counterexample-guided inductive synthesis and Bayesian optimization to solve it. To evaluate\n                    <jats:sc>Aegis<\/jats:sc>\n                    and its synthesized shields, we use eight representative control systems and compare\n                    <jats:sc>Aegis<\/jats:sc>\n                    with the current state-of-the-art. Our results show that the programmatic runtime shields synthesized by\n                    <jats:sc>Aegis<\/jats:sc>\n                    can correct all unsafe commands from neural policies, ensuring that the systems do not violate any desired safety properties at all times. Compared to the current state-of-the-art,\n                    <jats:sc>Aegis<\/jats:sc>\n                    \u2019s shields exhibit a 2.2\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    reduction in time overhead and a 3.9\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    reduction in memory usage, suggesting that they are much more lightweight. Moreover,\n                    <jats:sc>Aegis<\/jats:sc>\n                    \u2019s shields incur an average of 1.5\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\times\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    fewer interventions than other shields, showing better permissiveness.\n                  <\/jats:p>","DOI":"10.1145\/3773034","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T14:48:01Z","timestamp":1761576481000},"page":"1-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Synthesizing Efficient and Permissive Programmatic Runtime Shields for Neural Policies"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0799-5018","authenticated-orcid":false,"given":"Jieke","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-8585","authenticated-orcid":false,"given":"Junda","family":"He","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5938-1918","authenticated-orcid":false,"given":"Zhou","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada  and Alberta Machine Intelligence Institute, Edmonton, Alberta, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4681-1699","authenticated-orcid":false,"given":"\u0110or\u0111e","family":"\u017dikeli\u0107","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4367-7201","authenticated-orcid":false,"given":"David","family":"Lo","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"SciPy. 2025. approx_fprime; SciPy v1.15.3 Manual\u2014docs.scipy.org. 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