Bolliger et al.'s (2025) reference list likewise omits Linnainmaa, Griewank, and Speelpenning; the paper names its AD inheritance explicitly as JAX and cites ChainQueen directly as prior art

linnainmaagriewankspeelpenningautomatic-differentiationbolligerjaxchainqueencitation-historyhistory-of-mlbridge-investigation

Bolliger, Fadini, Bambach & Rupenyan's 20-item reference list (ending at [20], Sánchez-Banderas & Otaduy 2018 on strain-rate dissipation) contains no citation of Seppo Linnainmaa, Andreas Griewank, or Bert Speelpenning. The paper is explicit about where its automatic differentiation comes from: "several computational frameworks have been developed to compute the gradients algorithmically through automatic differentiation (AD)," naming PyTorch, TensorFlow, Taichi-Lang, and JAX as candidates, before stating "Among these AD alternatives, we decided to use JAX [16] because it is mature, under active development, and can be easily integrated with other machine learning libraries." Reference [16] is the JAX GitHub/paper (Bradbury et al., 2018) — a software library, not a numerical-analysis AD source.

Reference [12] is ChainQueen itself, cited directly as one of two prior differentiable-MPM simulators ("[12], [13] introduce differentiable MPM simulators"). This is the paper's own documented citation back to ChainQueen — already established by claim-bolliger-2025-second-independent-paper-confirms-standing-differentiable-mpm-technique as citation-continuous rather than a parallel rediscovery — confirming that the chain from SAND-91-7095 through ChainQueen to Bolliger et al. runs MPM-paper to MPM-paper and library-to-library throughout, never touching the AD-discovery literature at any link.

This is the second claim-note resting on this preprint alone (see root_shared above), one below the vault's three-note single-source concentration cap; the cap permits at most one more claim-note on Bolliger et al. alone before independent corroboration is required.