Motivated by several mild excesses around 95 GeV, we investigate the prospects for a light scalar $S$ produced via Higgsstrahlung, $e^{+}{e}^{-}\to Z({\mu }^{+}{\mu }^{-})S$, at future $e^{+}{e}^{-}$ Higgs factories. We take the CEPC as a benchmark, with a center-of-mass energy of $\sqrt{s}=240\mathrm{GeV}$ and an integrated luminosity of $L=20{\mathrm{ab}}^{-1}$. We focus on the decay modes $S\to {\tau }^{+}{\tau }^{-}$ and $S\to b\overline{b}$. To maximize sensitivity, we employ the particle-level transformer networks particle transformer (ParT) and its more-interactive variant MIParT, which exploit the features of all reconstructed objects and their correlations. For a representative signal benchmark, this approach improves the expected statistical precision on the signal strength by factors of 2.3 in the ${\tau }^{+}{\tau }^{-}$ channel and 1.4 in the $b\overline{b}$ channel compared to a cut-based analysis. Within the flipped next-to-two-Higgs-doublet model (N2HDM-F), the CEPC can measure the signal strength with a statistical precision down to 1.0% in the ${\tau }^{+}{\tau }^{-}$ channel and 0.69% in the $b\overline{b}$ channel using MIParT. It can achieve a $5\sigma$ discovery for ${\mu }_{\tau \tau }^{ZS}>1.6\times {10}^{-2}$ or ${\mu }_{bb}^{ZS}>5.0\times {10}^{-3}$, and reach 1% precision for ${\mu }_{\tau \tau }^{ZS}>0.93$ or ${\mu }_{bb}^{ZS}>0.14$. These gains are expected to qualitatively carry over to other future lepton colliders such as FCC-ee and the ILC. Our results demonstrate the potential of particle-level machine-learning techniques to strengthen light Higgs searches at future $e^{+}{e}^{-}$ Higgs factories.

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• Extensions of Higgs sector
• Particle phenomena
• Hypothetical scalars
• Artificial intelligence
• Lepton colliders