Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:34:16.340289Z
Paper Citation Record · LEDGER
As of 11 August 2026, this Paper Citation Record lists 100 of 201 outbound references and 0 inbound Pith citation observations for arXiv:2608.07648.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:34:16.340289Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: cited_works
100 of 201 outbound references displayed
External citation measurements
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Leveraging generative models to assist Monte Carlo sampling Communications in Mathematical Sciences , volume =
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Leveraging generative models to assist Monte Carlo sampling and Brubaker, Marcus A
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Leveraging generative models to assist Monte Carlo sampling Communications on Pure and Applied Mathematics , volume =
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Leveraging generative models to assist Monte Carlo sampling Unresolved cited work
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Leveraging generative models to assist Monte Carlo sampling Advances in Neural Information Processing Systems , volume =
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Leveraging generative models to assist Monte Carlo sampling e3nn: Euclidean Neural Networks
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Leveraging generative models to assist Monte Carlo sampling Stochastic Processes and their Applications , volume =
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Leveraging generative models to assist Monte Carlo sampling Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
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Leveraging generative models to assist Monte Carlo sampling Boltzmann Generators:
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Leveraging generative models to assist Monte Carlo sampling Asymptotically unbiased estimation of physical observables with neural samplers
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Leveraging generative models to assist Monte Carlo sampling and Anders, Christopher J
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Leveraging generative models to assist Monte Carlo sampling Physical Review E , volume =
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Leveraging generative models to assist Monte Carlo sampling Efficient Estimation of Free Energy Differences from
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Leveraging generative models to assist Monte Carlo sampling The Journal of Chemical Physics , volume =
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Leveraging generative models to assist Monte Carlo sampling NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
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Leveraging generative models to assist Monte Carlo sampling Efficient Modelling of Trivializing Maps for Lattice $\phi^4$ Theory Using Normalizing Flows: A First Look at Scalability
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