Pith. sign in

Paper Citation Record · LEDGER

Leveraging generative models to assist Monte Carlo sampling

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.

pith.paper-citation-record.v1
2608.07648 v1

Coverage vector

measured 100 of 201 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:34:16.340289Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 201 outbound references displayed

  • verified exact11
  • verified fuzzy0
  • unresolved87
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2494f53b-a29b-4938-a273-201c23c8ae53 · outbound

This paper cites Communications in Mathematical Sciences , volume =.

Leveraging generative models to assist Monte Carlo sampling Communications in Mathematical Sciences , volume =

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.836187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.836187Z digest=sha256:7e28b83b035565138d0abb24f671381120d8351c89a386b985bbc7c325c3af93

Observation 5ae7489d-594d-4f63-8c6b-f5cc54de3ed5 · outbound

This paper cites Normalizing.

Leveraging generative models to assist Monte Carlo sampling Normalizing

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.842103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.842103Z digest=sha256:cd5a8bedcf8a82f726ba141a1a61eb3316ba2cb3f2ddf0f7653ce3efa399bf90

Observation ddae9be9-1bbf-4fc8-b1d8-32d4d4179ee6 · outbound

This paper cites and Brubaker, Marcus A.

Leveraging generative models to assist Monte Carlo sampling and Brubaker, Marcus A

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.847337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.847337Z digest=sha256:5d9d8ea7c7ac000e69efdd0c8f6265f4e162d14d29bc48184b59d68a09fe3833

Observation 23ea1bba-2ff6-4821-8e45-5010b4d16564 · outbound

This paper cites Density Estimation Using.

Leveraging generative models to assist Monte Carlo sampling Density Estimation Using

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.852273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.852273Z digest=sha256:304c8ee532c9357c1a6fac8b3cc23d42b998dc667f1e2dc5cf5291d9e7ff92f2

Observation dedef4fc-f9d4-47cf-bb11-ee9ec2594592 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.857570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.857570Z digest=sha256:96092d14826f62bd7ad04e9f74640c82235611c1b3d33b08c91f425ebe687eb8

Observation d6840786-5877-44f1-b6e3-21ef36d5a9cc · outbound

This paper cites Communications on Pure and Applied Mathematics , volume =.

Leveraging generative models to assist Monte Carlo sampling Communications on Pure and Applied Mathematics , volume =

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.862640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.862640Z digest=sha256:f52d891c51469472ae54780659cd28fd4e5427aaed0228b24fb8814fa0c64896

Observation d9b5764a-2d25-492f-9203-efdb31582830 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.868356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.868356Z digest=sha256:dcb15a4fe0563c4d8bcf76648ae1843b3e09254997c72d3fe4b4a412165f2954

Observation 463aa206-8081-4639-80e6-32223929e51b · outbound

This paper cites Equivariant.

Leveraging generative models to assist Monte Carlo sampling Equivariant

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.873278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.873278Z digest=sha256:17a95d94306459b17183feb1fc2469d3f953eb8899a1d3e4f950cd6af3f0235f

Observation b0255faa-a0af-41aa-b687-5b268061a323 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.878169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.878169Z digest=sha256:4bb8e5bf3ab021951c36cd2441e09c06466091ddf32a68ad20ee583489e974e3

Observation 2a2b0124-22bc-41f8-853a-661c138bd559 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

Leveraging generative models to assist Monte Carlo sampling Advances in Neural Information Processing Systems , volume =

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.883704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.883704Z digest=sha256:08ede0a7410ce7f80b85f1d3181df7beaf6c7761f8f4c78c0816a42929954de7

Observation 519aa6e8-460e-4f3d-8839-f4cbc8d73fbb · outbound

This paper cites e3nn: Euclidean Neural Networks.

Leveraging generative models to assist Monte Carlo sampling e3nn: Euclidean Neural Networks

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.888805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.888805Z digest=sha256:18b1bdcd19e86d3ef161a2f3e6834f4a37089963b7a642d0b5e64fe4059f9239

Observation abd895a0-e552-4edb-892a-85c68bbe7ed2 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.895190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.895190Z digest=sha256:ec143df6dedf68ea72c36b74f6c4da4bc422baf9c9e275b78d1b860f625dc39d

Observation 126217c0-34c5-44d5-bdf3-b8933a3d839c · outbound

This paper cites Proceedings of the 32nd.

Leveraging generative models to assist Monte Carlo sampling Proceedings of the 32nd

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.900837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.900837Z digest=sha256:93b69e0db0b7dd52c179a8a8bbd62e1b712be6796ea2e0ab1b045d66eedd2048

Observation 08faca79-d121-4575-9f77-2c488152d61e · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.905725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.905725Z digest=sha256:89bcdeccbc89b115fc955ce66d83b484d4d55dfdcac11a892ce5720d89e407c2

Observation b81c1906-30f6-4f92-891a-03732f5f6c63 · outbound

This paper cites Denoising.

Leveraging generative models to assist Monte Carlo sampling Denoising

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.910790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.910790Z digest=sha256:42333d4b7266a0953e7192af802a4a6fea92c3f8ee37f16709dc93d04f89dd91

Observation 78a2a222-41fd-45e7-93de-ac9e3f8b92cf · outbound

This paper cites Stochastic Processes and their Applications , volume =.

Leveraging generative models to assist Monte Carlo sampling Stochastic Processes and their Applications , volume =

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.915528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.915528Z digest=sha256:b393c2d02626a550e01e43f9d708c4784f8a8ff5c708fc46980f6088c374ae69

Observation 0823c742-c467-4871-b968-27cc90c6d5d9 · outbound

This paper cites Building.

Leveraging generative models to assist Monte Carlo sampling Building

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.922042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.922042Z digest=sha256:4a915e5230ec363f203cb424aafd56d1343ace60452a15682352b9dc8e19b2a1

Observation bb909c40-8959-463e-97f4-373a5004ee6b · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.927396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.927396Z digest=sha256:c42b7aad570e22b02876471f454d3439bcf5127b1bda7de4ba33e4a9eb8a17d4

Observation f2f6580a-d35e-4308-8847-a22bbb43a8be · outbound

This paper cites Stochastic Interpolants: A Unifying Framework for Flows and Diffusions.

Leveraging generative models to assist Monte Carlo sampling Stochastic Interpolants: A Unifying Framework for Flows and Diffusions

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.932516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.932516Z digest=sha256:f14cddad1ab818e3848211d4b8fe861445ebf975b6e031f84a15f6eb12913d65

Observation fe893f3e-da77-4994-bcbc-74b5e46b0291 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.937795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.937795Z digest=sha256:28b4f0a4f37481a27f9282ad861a17ea82d98e100a927603fda9dc59bba05c59

Observation 7a655703-2203-4ef5-9544-68c2a0d4a53a · outbound

This paper cites Boltzmann Generators:.

Leveraging generative models to assist Monte Carlo sampling Boltzmann Generators:

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.942670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.942670Z digest=sha256:2a93121dc13260ce1a53db5d512b9c772cf5f49b62c79273709d2034b6719a4d

Observation b11f579c-a15a-4381-ae3b-a08fb9a85c11 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.947390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.947390Z digest=sha256:9b7a1ddf5c3f748389e75ee02b34f8b7a3cb3f48960ba7c83bfb32c36355a73a

Observation d0393ff3-8d1e-4e71-9eab-b904d21b4539 · outbound

This paper cites Asymptotically unbiased estimation of physical observables with neural samplers.

Leveraging generative models to assist Monte Carlo sampling Asymptotically unbiased estimation of physical observables with neural samplers

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.952466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.952466Z digest=sha256:da9e36c1aaf3670d01646d3bc9d266999fd75fbd38b0abd140adf6da21993849

Observation 9fe228c0-a5e4-4219-8f04-d9cbf7226004 · outbound

This paper cites and Anders, Christopher J.

Leveraging generative models to assist Monte Carlo sampling and Anders, Christopher J

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.957809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.957809Z digest=sha256:b352e0b245037d881d41cdc402928c85510708c655f176246a958f0b51e477c4

Observation d74fb629-ccc2-4e31-8b57-1f97ef7c411a · outbound

This paper cites Physical Review E , volume =.

Leveraging generative models to assist Monte Carlo sampling Physical Review E , volume =

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.962613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.962613Z digest=sha256:10a1daadef1dcddcc8d2988d233e6ed96fd85613c51cb9ca0a8a2af6eafaa446

Observation 42758265-3be7-4348-bceb-d991d73a30ab · outbound

This paper cites Efficient Estimation of Free Energy Differences from.

Leveraging generative models to assist Monte Carlo sampling Efficient Estimation of Free Energy Differences from

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.967472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.967472Z digest=sha256:a880e1c1bd65d99341fdd71f5cf02569989ae2742b6302d7da524d629aed140e

Observation 55dff3cc-1dbe-4cea-9b07-f08ab64b5566 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 27

Resolution
verified exact
doi, observed 2026-08-11T00:34:18.029859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:15.972438Z digest=sha256:af82266618b05cdaa8766df52fc74629942bba89319140f98916571957f9196d

Observation 5e8cd210-b273-4853-b8e9-df3d4e7fd4ad · outbound

This paper cites Normalizing.

Leveraging generative models to assist Monte Carlo sampling Normalizing

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.977551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.977551Z digest=sha256:a803128099fd873522a8d3d3e8619eaf9229b2a40e7f7ebe19f4ab63bc6c0c77

Observation 349daceb-e817-4fb2-ac00-685efa325d81 · outbound

This paper cites The Journal of Chemical Physics , volume =.

Leveraging generative models to assist Monte Carlo sampling The Journal of Chemical Physics , volume =

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.982476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.982476Z digest=sha256:ed1dc83dd87fc28a47aa847af5da9ffcf1b521d0bfe8a09951d5acb104b44378

Observation 63fa25dc-c4c2-4f80-86d5-61c1503f0158 · outbound

This paper cites Normalizing flows for atomic solids.

Leveraging generative models to assist Monte Carlo sampling Normalizing flows for atomic solids

Reference 30

Resolution
metadata mismatch
local_arxiv, observed 2026-08-11T00:34:19.074761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:15.987320Z digest=sha256:434e0cc1e8878b87c7e8c1de01f4a23aae180606cd1b3c7cfc68a15442aa597d

Observation 401e23f1-06ff-4155-a974-b261cf45f7bb · outbound

This paper cites Estimating.

Leveraging generative models to assist Monte Carlo sampling Estimating

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.992630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.992630Z digest=sha256:d91d49e8f40a373a3f4e822b0e4c87a15662565ee6d32f5e33c6000a57564a2a

Observation f0120348-c1b3-433e-ade9-024ff5af8c7d · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:15.997517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:15.997517Z digest=sha256:f6fe2d67d888dae30091850846a62bee16c401bfa715036e871d8df11500ed61

Observation 2410fead-663c-4407-bef7-93a83c8076ad · outbound

This paper cites Scalable.

Leveraging generative models to assist Monte Carlo sampling Scalable

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.002271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.002271Z digest=sha256:5a96538da7ab55ee4b40db0d45c55825bcc8142d2f334dcbd0862b814baf0bb8

Observation 93891cdb-5370-44e8-802b-69f65ac5238d · outbound

This paper cites doi:10.48550/arXiv.2512.23930 , archiveprefix =.

Leveraging generative models to assist Monte Carlo sampling doi:10.48550/arXiv.2512.23930 , archiveprefix =

Reference 34

Resolution
verified exact
doi, observed 2026-08-11T00:34:18.000519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.007201Z digest=sha256:8fe4969e64f8b41b5401b93200c27a3af3589f69f833b7da0bc837fe95930bae

Observation 859c2d8f-d44a-417d-a81a-02f11d9a44af · outbound

This paper cites Estimating.

Leveraging generative models to assist Monte Carlo sampling Estimating

Reference 35

Resolution
verified exact
doi, observed 2026-08-11T00:34:17.903837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.012732Z digest=sha256:6577e1ca27b904a30e1953a5a3caec3e3a3784ad830ae57a0345926293eac5c9

Observation 530bf166-2819-4ad1-bd8f-1974a1b52802 · outbound

This paper cites and Shirts, Michael R.

Leveraging generative models to assist Monte Carlo sampling and Shirts, Michael R

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.018178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.018178Z digest=sha256:d2c9a06708e12a9b62a3bc91e2023e83601f8c60451d740409a156886a133506

Observation 1ad15708-0256-4dd1-8c41-9600e9afa1b5 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.023523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.023523Z digest=sha256:882f8017954ed0d505bdfbdc071bea2aa90327c3e423273b38a78e1fbc740dfc

Observation ce1a0c25-4953-4863-b3ea-6b532d5f13f7 · outbound

This paper cites Particle.

Leveraging generative models to assist Monte Carlo sampling Particle

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.028328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.028328Z digest=sha256:bc0d1f4c81e5de562f32a27fc52848f0277ff6208f87993bbe19fcfc0c653e6d

Observation a9ef752e-6129-43c2-81ae-9170163e8857 · outbound

This paper cites Performance of Machine-Learning-Assisted.

Leveraging generative models to assist Monte Carlo sampling Performance of Machine-Learning-Assisted

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.033297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.033297Z digest=sha256:f1407c5f6118fc2f68ef36b6e42cbe3874489359db56c200f1191d751ea0e985

Observation cc9b8af0-21cc-4ce2-b268-bd2cc49da2bc · outbound

This paper cites NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport.

Leveraging generative models to assist Monte Carlo sampling NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.038383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.038383Z digest=sha256:899efa85d4c8469e6c6eccd0fdaa64b9690ca3ece02eff9d56ef59c4d4b17fd7

Observation cb59084e-211b-4b15-aafe-e951bfc5f387 · outbound

This paper cites Transport.

Leveraging generative models to assist Monte Carlo sampling Transport

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.043996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.043996Z digest=sha256:5db48fea872b9a669bd89155752fb22cdc02c5a4905bd3b421767f426c442384

Observation a8d03d84-57a5-4bcd-96f5-ea03266fd256 · outbound

This paper cites and Marzouk, Youssef M.

Leveraging generative models to assist Monte Carlo sampling and Marzouk, Youssef M

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.049154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.049154Z digest=sha256:38a715a15411c8bac11509c80f36b5110205d4046f3b8e781aede951448de092

Observation 71e14b85-f8a4-4724-a2be-2dbe2411db21 · outbound

This paper cites and Papaspiliopoulos, O.

Leveraging generative models to assist Monte Carlo sampling and Papaspiliopoulos, O

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.054419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.054419Z digest=sha256:7dd0057d2722ff092faaad3f7277efdcf836c4e7fca50a09591258fa4c367bb3

Observation ba8b2d1a-0bd9-485f-a4fe-a056aac076b3 · outbound

This paper cites Efficient Modelling of Trivializing Maps for Lattice $\phi^4$ Theory Using Normalizing Flows: A First Look at Scalability.

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

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.059660Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.059660Z digest=sha256:be1f1fe6b7601bb5df16b247afee97c8ab45f16109a12f0dab0e9f307fc7575f

Observation 1c588ea1-e69a-4cf7-b0a2-57ceaa0eb00e · outbound

This paper cites Statistics and Computing , volume =.

Leveraging generative models to assist Monte Carlo sampling Statistics and Computing , volume =

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.065099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.065099Z digest=sha256:7a881125ef2ad3795994993964f7960e8d210414ceef0f0ba2bbec8d5e14606e

Observation 61c00fe0-46d3-4d36-a82b-541285b1de04 · outbound

This paper cites and Wang, Jian-Sheng , year = 1986, month = nov, journal =.

Leveraging generative models to assist Monte Carlo sampling and Wang, Jian-Sheng , year = 1986, month = nov, journal =

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.070438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.070438Z digest=sha256:91bb0c60b7f968391e7c95885ec9f6076e6c5851cc9c3795ab0e3ec1002eb17b

Observation fdfaa986-fda8-4340-850c-d0f540666dac · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.075538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.075538Z digest=sha256:49e6d5de35d7e6513ae24c4a7b29465b5779aae7b3d7462e2ae382401e90943f

Observation 5d2992ac-3234-4746-9b64-3f1ccdce8b0b · outbound

This paper cites Exchange.

Leveraging generative models to assist Monte Carlo sampling Exchange

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.080330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.080330Z digest=sha256:8a5f50c6a4c09a6d6170ec51391269959f3b1e4ecd209e903d8fae63d62f70c7

Observation a2613bef-3b1e-4c62-b11c-cbf1ca4ea098 · outbound

This paper cites Sequential.

Leveraging generative models to assist Monte Carlo sampling Sequential

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.085412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.085412Z digest=sha256:9c5b79bb0baf59a0e67c8f5e6ec1164c4e923e53cc0658daadec57e0537852fa

Observation dfbad4bc-da48-47a8-9d0e-e1d4eb0ed2d8 · outbound

This paper cites and Iba, Y.

Leveraging generative models to assist Monte Carlo sampling and Iba, Y

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.091154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.091154Z digest=sha256:7119029821a08cf45dbf92d20ddc93e4bb31ed6c2b12da9436f133dd6de531ce

Observation 4036187e-c2d8-4554-b4d8-5849534c3418 · outbound

This paper cites The European Physical Journal C , volume =.

Leveraging generative models to assist Monte Carlo sampling The European Physical Journal C , volume =

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.096099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.096099Z digest=sha256:1cabe1e66302df239edbe6c6c85055277af9db9754dd995af671de449174bf51

Observation 500811cd-6646-4fdc-ae6c-77cc68971a0d · outbound

This paper cites Efficient Mapping of Phase Diagrams with Conditional.

Leveraging generative models to assist Monte Carlo sampling Efficient Mapping of Phase Diagrams with Conditional

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.100781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.100781Z digest=sha256:363c78b39b876093116b27ea35eabede7887b54a0ba8efe98af5f30088d1b243

Observation d2b1814f-0ec5-456b-b35b-5ef453bb4044 · outbound

This paper cites Machine Learning: Science and Technology , volume =.

Leveraging generative models to assist Monte Carlo sampling Machine Learning: Science and Technology , volume =

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.105513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.105513Z digest=sha256:e76d6254f2ff82b3bab41110a702e983baeb5bb7f95b38e44d3fd8b8feca748f

Observation 157509f6-e609-4215-835a-757b9b344f4e · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.110168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.110168Z digest=sha256:6673044ebd952b70de74054db8403cb8a893060faebfad3200e4efa4feae2ac6

Observation dd822d56-abfd-40f1-905f-6515fae567ac · outbound

This paper cites and Bose, Joey and Lin, Chen and Klein, Leon and Bronstein, Michael M.

Leveraging generative models to assist Monte Carlo sampling and Bose, Joey and Lin, Chen and Klein, Leon and Bronstein, Michael M

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.115111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.115111Z digest=sha256:2ebad0335c3e0896a13b19a9cf1726b855bbc0b12caf2d2c9e093955340b56c1

Observation c4a3118e-816e-42c0-8d21-7928f4830b86 · outbound

This paper cites Stochastic.

Leveraging generative models to assist Monte Carlo sampling Stochastic

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.119892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.119892Z digest=sha256:3bf9b3a26902af0496c7b8b997ed848f58cbc3a96da2d92e70073dceef343035

Observation ab06bde5-837a-471d-9746-294445e74ecb · outbound

This paper cites Annealed.

Leveraging generative models to assist Monte Carlo sampling Annealed

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.124656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.124656Z digest=sha256:7d500a81186a4ec473315983257d5842a9d39b805e011023584624c11e5c9430

Observation 1538ed5a-bb17-4249-bc4f-1b21f1abe720 · outbound

This paper cites Continual.

Leveraging generative models to assist Monte Carlo sampling Continual

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.129539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.129539Z digest=sha256:d4d9ffa217fffda42a9f80ea523f29f91e099d2be96d5c41f9a3502342c02700

Observation 094fdb44-f214-4b78-a9be-1df13e6fd142 · outbound

This paper cites Journal of High Energy Physics , volume =.

Leveraging generative models to assist Monte Carlo sampling Journal of High Energy Physics , volume =

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.134465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.134465Z digest=sha256:1bdf96a79723bce00265a132afe662df979223ce1443bb0a76a05aa348d32263

Observation 1fbe2859-b7da-4a2f-a4bc-1920f2b9d03f · outbound

This paper cites , year = 1997, month = apr, journal =.

Leveraging generative models to assist Monte Carlo sampling , year = 1997, month = apr, journal =

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.139591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.139591Z digest=sha256:4b5f4963e9875f641ce98862e1a085d51ac303e55f4f645a468960273504156b

Observation f7cdf7f2-7dba-47a4-a32f-b7e5ec2340a4 · outbound

This paper cites , year = 1998, month = mar, journal =.

Leveraging generative models to assist Monte Carlo sampling , year = 1998, month = mar, journal =

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.144264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.144264Z digest=sha256:c959f7f357759b14fc91955270e9a05153d4100189793b5ab3574d319dd9b31c

Observation fc647c9a-f779-47df-ad07-e1a06ac95692 · outbound

This paper cites and Crooks, Gavin E.

Leveraging generative models to assist Monte Carlo sampling and Crooks, Gavin E

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.149520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.149520Z digest=sha256:d65021bb4c5dd777eeaa82a532532355994ba18d7a34712f36844ae2e7f57afd

Observation fd6061a7-e838-4708-a7e2-7d77646005d2 · outbound

This paper cites Sampling the Lattice.

Leveraging generative models to assist Monte Carlo sampling Sampling the Lattice

Reference 63

Resolution
verified exact
doi, observed 2026-08-11T00:34:17.751139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.154516Z digest=sha256:a0132b8d76e04afcbc63d2289ada2f316eaf9e50e52dbd2428f985204f7636fc

Observation 06027471-63f1-484a-8355-de466958022c · outbound

This paper cites Numerical Determination of the Width and Shape of the Effective String Using.

Leveraging generative models to assist Monte Carlo sampling Numerical Determination of the Width and Shape of the Effective String Using

Reference 64

Resolution
verified exact
doi, observed 2026-08-11T00:34:17.726423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.159610Z digest=sha256:686f95458adc646297e35984f324b21576049c0bce66e976f44255fa3646eca1

Observation a1b1fbf3-7f06-487d-93d9-e6a25f8912fa · outbound

This paper cites Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo.

Leveraging generative models to assist Monte Carlo sampling Accelerating astronomical and cosmological inference with Preconditioned Monte Carlo

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.164702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.164702Z digest=sha256:e13c6c330e9d123e4e9c848c666659ba5ea7851495d64ec02f39c23f01a9a43c

Observation a9a74839-da0b-4068-9e9e-bea2c157e12b · outbound

This paper cites Accelerated.

Leveraging generative models to assist Monte Carlo sampling Accelerated

Reference 66

Resolution
verified exact
doi, observed 2026-08-11T00:34:17.702849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.169936Z digest=sha256:c33fac9d8b45ba31776d06430d6b62939c0567d5e9e5a3ec0ba2303dac2e15d7

Observation cfb7f63a-bae6-4848-8454-6368c67aeb91 · outbound

This paper cites Skipping the.

Leveraging generative models to assist Monte Carlo sampling Skipping the

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.174905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.174905Z digest=sha256:9daf560e72649eec4064e1378e90132f0ced9f0cd8f66aa083422f81634163e3

Observation eb5ff01f-bb05-47c5-9ce5-2cd2ba480c71 · outbound

This paper cites Sampling.

Leveraging generative models to assist Monte Carlo sampling Sampling

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.179638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.179638Z digest=sha256:aa38800d46fafca30609a28b60eda5cb584838ec02725ff92617763f829e877c

Observation 9f4b53e3-6732-4121-b024-35b2c5a384db · outbound

This paper cites Flow Perturbation to Accelerate.

Leveraging generative models to assist Monte Carlo sampling Flow Perturbation to Accelerate

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.184519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.184519Z digest=sha256:eed8b42eeb9729bff04dcc9edbd81c0c8afeba6cbf29e22f3a1c8375c3af7b9c

Observation 37006d3a-504e-47ca-8760-8000f3d0f062 · outbound

This paper cites Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks.

Leveraging generative models to assist Monte Carlo sampling Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks

Reference 70

Resolution
verified exact
local_arxiv, observed 2026-08-11T00:34:18.990137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.190156Z digest=sha256:e21ba78dd466cffed7d54591a13c7ddbb3e40d6a008cf28cc1d89395484f59a1

Observation 25cbe961-5810-4b79-91e7-e7d54ca4704e · outbound

This paper cites Enhanced.

Leveraging generative models to assist Monte Carlo sampling Enhanced

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.195655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.195655Z digest=sha256:d89e22543dfdca08dccf0062bb1bd3473b2c0c196160dbaf9382d60c6f6c97f8

Observation 39545cfa-f5dd-46ec-ad9e-0b0e3014be08 · outbound

This paper cites Speed-up of.

Leveraging generative models to assist Monte Carlo sampling Speed-up of

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.200609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.200609Z digest=sha256:28151601d11499d153bb23ade517be65a6e76d1abe4b750542ada60fd9e929e5

Observation aca2f4d1-d238-4641-a36d-cf978d3f504b · outbound

This paper cites Generalized.

Leveraging generative models to assist Monte Carlo sampling Generalized

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.205997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.205997Z digest=sha256:18a594fa74ef0cb97796e973b6457de71263f79abb838ca440b0fe14817d8148

Observation 818830f3-91bc-4a06-ab15-879e3ea696ff · outbound

This paper cites Statistics and Computing , volume =.

Leveraging generative models to assist Monte Carlo sampling Statistics and Computing , volume =

Reference 74

Resolution
verified exact
doi, observed 2026-08-11T00:34:17.589613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.210921Z digest=sha256:9cface03e23a82eb608a3df0602987008d0e9aa716cc3a5e2b945e2e9523c807

Observation f4b0c2e7-8f54-4e34-beac-cbdbbf798b09 · outbound

This paper cites Physical Review E , volume =.

Leveraging generative models to assist Monte Carlo sampling Physical Review E , volume =

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.215996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.215996Z digest=sha256:2755b07d5a911ad4ba7c6d16db0886b05f5159164a72fcf76401776f4b3d691d

Observation 24b362e3-3030-424c-8564-759255df76b7 · outbound

This paper cites and Kucukelbir, Alp and McAuliffe, Jon D.

Leveraging generative models to assist Monte Carlo sampling and Kucukelbir, Alp and McAuliffe, Jon D

Reference 76

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.220789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.220789Z digest=sha256:24c31e43dfde293f3dfd9fe5ff2b05a0fabe3d717d7e57e67b4f3be55ff938fa

Observation b41993d3-d9a9-424d-b27e-d72f5fd92691 · outbound

This paper cites and Ghahramani, Zoubin and Jaakkola, Tommi S.

Leveraging generative models to assist Monte Carlo sampling and Ghahramani, Zoubin and Jaakkola, Tommi S

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.225509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.225509Z digest=sha256:ef67ba76126e541f3e9d84d9b68d1033a3ca57508769013f32ef9784ac761bd6

Observation e8447618-29d7-431a-b18d-caf5e9012de2 · outbound

This paper cites Variational.

Leveraging generative models to assist Monte Carlo sampling Variational

Reference 78

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.230403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.230403Z digest=sha256:cac3184aff6c836616656a47279388707e89898e0e63483a93cfaadc7646c7d3

Observation 675a562b-9743-44bf-9e88-b00ebe5e510b · outbound

This paper cites Solving Statistical Mechanics Using Variational Autoregressive Networks.

Leveraging generative models to assist Monte Carlo sampling Solving Statistical Mechanics Using Variational Autoregressive Networks

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.235356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.235356Z digest=sha256:0b55428103fe861efb4a2b04c85620ada217fb33d584ee9e9ce5261f1bf16962

Observation 0536c030-d6ff-498c-9a8e-4bcd2ad1e763 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.240436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.240436Z digest=sha256:c661393571b9d52078bfc4472cb5dad912c7d2d04d1dcf734bf1ed2a8effa1ff

Observation 9183074d-8633-43fb-b359-1cad85fc367b · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.245294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.245294Z digest=sha256:f1b95a8317d52f7bad7604f37368e0a12136d0d878e2bff4dd4c0c2c0416fb2e

Observation 9a425a2f-a6f4-4835-9a1a-f29c03387943 · outbound

This paper cites Physical Review D , volume =.

Leveraging generative models to assist Monte Carlo sampling Physical Review D , volume =

Reference 82

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.250031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.250031Z digest=sha256:0053e20e62242da78d7d14222e74bf822ed3323855136d3bcb13b387d86ad205

Observation 5136d038-e7ba-4783-aabf-ceb7cba8cb1b · outbound

This paper cites Forty-Second.

Leveraging generative models to assist Monte Carlo sampling Forty-Second

Reference 83

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.254723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.254723Z digest=sha256:3155e28bb3668de23e73337d0a837f28c0d386bc1e6ad83598e4bf6bec470c24

Observation a26008dc-38c4-4a10-b618-18faa70e5b3e · outbound

This paper cites Improving.

Leveraging generative models to assist Monte Carlo sampling Improving

Reference 84

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.259482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.259482Z digest=sha256:ee881eb85f9a9600d6115bd30643e1755c6f349f825f6391735a5e350aed3e34

Observation e28f214a-12dc-4f35-8443-d09983e692f5 · outbound

This paper cites Mitigating mode collapse in normalizing flows by annealing with an adaptive schedule: Application to parameter estimation.

Leveraging generative models to assist Monte Carlo sampling Mitigating mode collapse in normalizing flows by annealing with an adaptive schedule: Application to parameter estimation

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.264107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.264107Z digest=sha256:604406816dd0c8c573702d7feb298045b8ef47b4fe7cf8caf0cf8ad984a8a007

Observation 7c908c86-ffee-4015-ae95-0d6583c8d7a2 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 86

Resolution
verified exact
doi, observed 2026-08-11T00:34:17.536960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.269506Z digest=sha256:a4e78694c3b63b0d2ca7b8085d7feb77d8cee005b6694a9ddef0de043ee1778e

Observation 294ea0c0-4db6-4528-973c-0d383078b0f4 · outbound

This paper cites Nature Machine Intelligence , volume =.

Leveraging generative models to assist Monte Carlo sampling Nature Machine Intelligence , volume =

Reference 87

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.274445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.274445Z digest=sha256:d34552fbffaa4bba7b10d830bc1c22c1e4fea472ee3fd785c6bb971f6a01a708

Observation 28285f11-02c2-40e9-8578-b04de3a510a2 · outbound

This paper cites Machine Learning: Science and Technology , volume =.

Leveraging generative models to assist Monte Carlo sampling Machine Learning: Science and Technology , volume =

Reference 88

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.279313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.279313Z digest=sha256:98bd743d4e20e5d046faf61e545e5649fab109edbde8d48e2ea08e97395ea567

Observation d89944b7-2d9b-42cc-bf41-f7be5841834d · outbound

This paper cites Markovian.

Leveraging generative models to assist Monte Carlo sampling Markovian

Reference 89

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.284011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.284011Z digest=sha256:dd4a6a39e01c35b73617b4ea1b206c518047106aac6d5f11f86b20d54498d33f

Observation 213b8131-61c4-4421-ad01-b45b9ce3719d · outbound

This paper cites and Lederman, Roy R.

Leveraging generative models to assist Monte Carlo sampling and Lederman, Roy R

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.288902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.288902Z digest=sha256:3a8172b2727b615c23f8f11991bf87df2af6b79854632fd359ec9d13fa6e4e0a

Observation 050cfc14-65c4-4478-933a-f81020b49285 · outbound

This paper cites Flow-based sampling for multimodal and extended-mode distributions in lattice field theory.

Leveraging generative models to assist Monte Carlo sampling Flow-based sampling for multimodal and extended-mode distributions in lattice field theory

Reference 91

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.294118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.294118Z digest=sha256:6b5cc4d06c034107e563288550213a7800b64d0c58279050da7e992c6218f72b

Observation 8429b734-6a01-4938-b147-9945c664766c · outbound

This paper cites Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks.

Leveraging generative models to assist Monte Carlo sampling Boosting Monte Carlo simulations of spin glasses using autoregressive neural networks

Reference 92

Resolution
verified exact
local_arxiv, observed 2026-08-11T00:34:18.834973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.299418Z digest=sha256:d7a6dcc11bd35df6738c77bfb3a74a8e01901faf33ab7ada9d8724be62286de5

Observation fb9d335e-4b3d-4fa7-b2da-9b6e7f1b6241 · outbound

This paper cites Machine-Learning-Assisted.

Leveraging generative models to assist Monte Carlo sampling Machine-Learning-Assisted

Reference 93

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.304796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.304796Z digest=sha256:a182765e0c20b214a5ab6f411c8b5a55fa7bacdf22bf9be2acc7371177fe0841

Observation fa982a13-8b37-453a-818d-850dcee7e914 · outbound

This paper cites Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization.

Leveraging generative models to assist Monte Carlo sampling Demonstrating Real Advantage of Machine-Learning-Enhanced Monte Carlo for Combinatorial Optimization

Reference 94

Resolution
verified exact
local_arxiv, observed 2026-08-11T00:34:17.439925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-08-11T00:34:16.310707Z digest=sha256:914618135e00efae164fbe0fa4f523f9960fd061a6e0a3d22ea9a350069431ff

Observation cc0fed40-a4dd-413e-8220-3a9216778d61 · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.316323Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.316323Z digest=sha256:728dbbdfffeba988d5d8805b8855a6dad0445596b37a90950ea56cb58ede0d42

Observation 1967c4e5-9692-4c9d-9e3d-8e0ff92f43f1 · outbound

This paper cites Forty-First.

Leveraging generative models to assist Monte Carlo sampling Forty-First

Reference 96

Resolution
parse uncertain
no resolver link, observed 2026-08-11T00:34:16.321175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.321175Z digest=sha256:ac699963f8519e01577a7ce087c9ee12e2f7a038a27eb992b5cafb4267063bfe

Observation 1e43ee1b-6a34-418e-be55-ff0d77a3673e · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 97

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.325991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.325991Z digest=sha256:f8bb470125956b296b259df025013409fb67f2b48c9549532c9f552bfb339f83

Observation 5f5d306e-e348-4e91-b190-aa37f1724766 · outbound

This paper cites Relaxing.

Leveraging generative models to assist Monte Carlo sampling Relaxing

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.330849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.330849Z digest=sha256:36f323ee02cc0fc300704ec26b455fe072eeb7396d7cdb10a6f6c8fc1979f796

Observation 4ab8afe3-dcd1-4d2d-a29d-7d56364abea7 · outbound

This paper cites Theoretical Guarantees for Sampling and Inference in Generative Models with Latent Diffusions , booktitle =.

Leveraging generative models to assist Monte Carlo sampling Theoretical Guarantees for Sampling and Inference in Generative Models with Latent Diffusions , booktitle =

Reference 99

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.335572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.335572Z digest=sha256:73944d2a3b4e22bcd2442f188ad80a6730c72f23ea6b086b228ac25116c72aa5

Observation 255c88e0-a3ce-42e1-b2f7-5cdeca15d3ff · outbound

This paper cites an unresolved cited work.

Leveraging generative models to assist Monte Carlo sampling Unresolved cited work

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-11T00:34:16.340289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:34:16.340289Z digest=sha256:237e8a9791b7877974cec569f1feb0be1756b621455060ca2803884d816e0cf3

Pith citing papers

No inbound Pith citation observations are available.