Paper Citation Record · LEDGER

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling

As of 10 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2607.06841.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.06841 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-10T20:13:00.037357Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

21 of 21 outbound references displayed

verified exact8
verified fuzzy6
unresolved4
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metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38c78633-199f-45fa-9e0a-f4309faee3b5 · outbound

This paper cites NETS: A Non-Equilibrium Transport Sampler.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling NETS: A Non-Equilibrium Transport Sampler

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:17:33.782909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dbb7e467-4bf5-4bde-aee7-b786a357c5cd · outbound

This paper cites Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part I.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Approximation Theory of Tree Tensor Networks: Tensorized Univariate Functions -- Part I

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:17:33.790258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2c0a9500-193f-4a53-a5db-6bfbc71c1bc9 · outbound

This paper cites Approximation by tree tensor networks in high dimensions: Sobolev and compositional functions.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Approximation by tree tensor networks in high dimensions: Sobolev and compositional functions

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:17:33.791749Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ad6f44be-1efa-4574-8c5d-f886f8a79b39 · outbound

This paper cites Blessing, D., Berner, J., Richter, L., Domingo i Enrich, C., Du, Y ., Vahdat, A., and Neumann, G.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Blessing, D., Berner, J., Richter, L., Domingo i Enrich, C., Du, Y ., Vahdat, A., and Neumann, G

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-10T20:17:33.798172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d19af229-912e-4d98-be29-3ae03fac6b60 · outbound

This paper cites Sequential Controlled Langevin Diffusions.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Sequential Controlled Langevin Diffusions

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:17:33.787181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a6c19818-e699-4836-9ae3-800ec81b842f · outbound

This paper cites Hierarchical singular value decomposition of tensors.SIAM journal on matrix analysis and applica- tions, 31(4):2029–2054,.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Hierarchical singular value decomposition of tensors.SIAM journal on matrix analysis and applica- tions, 31(4):2029–2054,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:17:33.989919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8d53be3-2a8c-44cc-8c2b-4193f7943948 · outbound

This paper cites MCMC for multi-modal distributions.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling MCMC for multi-modal distributions

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T20:17:33.784471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation de03ba3a-3e77-4ba6-b701-50c22905f992 · outbound

This paper cites Flow Annealed Importance Sampling Bootstrap.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Flow Annealed Importance Sampling Bootstrap

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T20:17:33.800835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2371965f-af8a-4741-a0ce-aa91611949e6 · outbound

This paper cites Backward stochastic differential equations and viscosity solutions of systems of semilinear parabolic and elliptic PDEs of second order.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Backward stochastic differential equations and viscosity solutions of systems of semilinear parabolic and elliptic PDEs of second order

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:17:33.997805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a30caa8-20d0-4de1-82b2-763a12a16cf3 · outbound

This paper cites Diffusion-PINN Sampler.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Diffusion-PINN Sampler

Reference 10

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T20:17:33.793452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bb4073d6-5f6a-4fd8-b4ca-a45a46ec874f · outbound

This paper cites Dynamical Measure Transport and Neural PDE Solvers for Sampling.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Dynamical Measure Transport and Neural PDE Solvers for Sampling

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:17:33.802497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bdbd0903-2de1-4ae3-8972-d78a6c906b05 · outbound

This paper cites Fp64 is all you need: rethinking failure modes in physics-informed neural networks.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Fp64 is all you need: rethinking failure modes in physics-informed neural networks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-10T20:17:33.805579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a319b4be-ae1c-4839-990e-e2be189e6287 · outbound

This paper cites Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-07-10T20:17:33.799786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 862cca25-0ed1-4355-9347-b300a03526d9 · outbound

This paper cites an unresolved cited work.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-07-10T20:17:34.000722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4db6e70b-5814-4772-a8d3-ae9827f7a1d0 · outbound

This paper cites The classical representation of a TT from(20) is prone to rounding errors, when trying to accessC[α] with α= (α 1,.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling The classical representation of a TT from(20) is prone to rounding errors, when trying to accessC[α] with α= (α 1,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:17:33.992451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 42d77815-f9cd-4d53-b1af-4f2d77b8e987 · outbound

This paper cites This is expected to provide better control over the magnitude of ∥ · ∥2 H, thereby reducing the sensitivity of the algorithm to the choice of τn.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling This is expected to provide better control over the magnitude of ∥ · ∥2 H, thereby reducing the sensitivity of the algorithm to the choice of τn

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:17:33.985605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 03c79b53-8e19-4747-a178-9c1725fc32f6 · outbound

This paper cites the multi-modal setup from Section 4.1 with d= 1.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling the multi-modal setup from Section 4.1 with d= 1

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:17:33.994725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 522cf975-6c35-40cd-82ae-769ce238ec66 · outbound

This paper cites an unresolved cited work.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-07-10T20:17:33.991832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af168b4d-7fe6-48de-8d67-7fc44654e72d · outbound

This paper cites an unresolved cited work.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-07-10T20:17:33.993799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97cf711b-7181-4041-9906-7a3793dfe238 · outbound

This paper cites an unresolved cited work.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-07-10T20:17:33.978978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 49dcfcbd-002f-4ca2-b40d-ff877bf16d65 · outbound

This paper cites By design, our algorithm produces one result per chosen number of steps N (shown as blue and orange dots), whereas DIS and PIS can improve over training time.

Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling By design, our algorithm produces one result per chosen number of steps N (shown as blue and orange dots), whereas DIS and PIS can improve over training time

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T20:17:33.980978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.