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Paper Citation Record · LEDGER

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust

As of 20 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2608.12470.

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

pith.paper-citation-record.v1
2608.12470 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:12:52.097164Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

28 of 28 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3527a7f2-8104-4532-b51a-29280a220cb5 · outbound

This paper cites Metropolis, A.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Metropolis, A

Reference 1

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Observation 1b2424ba-a44d-482c-8355-abb15c28ac88 · outbound

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Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Unresolved cited work

Reference 2

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Observation 18d98738-e142-4d5d-b505-436c6f2856ef · outbound

This paper cites Pakmor, V.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Pakmor, V

Reference 3

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Observation b8914e91-07ff-4a9e-87ac-957241d0cd09 · outbound

This paper cites Siwek, R.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Siwek, R

Reference 4

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Observation 283095e3-84a3-4f99-a46d-35bfbed632ea · outbound

This paper cites Cranmer, J.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Cranmer, J

Reference 5

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Observation 4d3cfc5f-ef16-4f7c-ac71-bc2ae820a6a1 · outbound

This paper cites Fast likelihood-free cosmology with neural density estimators and active learning.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Fast likelihood-free cosmology with neural density estimators and active learning

Reference 6

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Observation 705174c3-1f92-473a-a9b5-f75f985bb998 · outbound

This paper cites Real-time gravitational-wave science with neural posterior estimation.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Real-time gravitational-wave science with neural posterior estimation

Reference 7

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Observation 9266eeb5-8cbb-4577-ac38-abd84f244568 · outbound

This paper cites MadMiner: Machine learning-based inference for particle physics.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust MadMiner: Machine learning-based inference for particle physics

Reference 8

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Observation 10b0a541-299f-4707-bd9a-feb70a0948f9 · outbound

This paper cites Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation

Reference 9

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Observation a9a0ff33-f7ee-4538-8069-91dd9dc3ed5b · outbound

This paper cites Flexible statistical inference for mechanistic models of neural dynamics.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Flexible statistical inference for mechanistic models of neural dynamics

Reference 10

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Observation de1f87a2-88b1-4a56-a917-08c483eed3df · outbound

This paper cites Neural posterior estimation for exoplanetary atmospheric retrieval.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Neural posterior estimation for exoplanetary atmospheric retrieval

Reference 11

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Observation c2d8b194-b9bd-4869-a05c-ddf29effa7f5 · outbound

This paper cites Neural Posterior Estimation with guaranteed exact coverage: the ringdown of GW150914.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Neural Posterior Estimation with guaranteed exact coverage: the ringdown of GW150914

Reference 12

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Observation 3b6ebdfb-e484-41d5-841a-bd5b1904db25 · outbound

This paper cites Villaescusa-Navarro, D.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Villaescusa-Navarro, D

Reference 13

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Observation 771f0a20-7e89-4a2e-ad25-1d3fe0907646 · outbound

This paper cites Villaescusa-Navarro, C.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Villaescusa-Navarro, C

Reference 14

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Observation 434eed64-a7b0-4766-8c4d-97a357eba940 · outbound

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Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Unresolved cited work

Reference 15

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Observation 2ce36681-f06d-4666-8284-2512cf306ae8 · outbound

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Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Unresolved cited work

Reference 16

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Observation 6986ac62-bb91-49c8-9d76-e1b589b16e02 · outbound

This paper cites Cornish, A.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Cornish, A

Reference 17

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Observation 585476f8-ccab-455e-8b83-f0c63af0cccb · outbound

This paper cites Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect

Reference 18

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Observation 20528078-04f6-4018-a660-f58333c24e57 · outbound

This paper cites Revisiting Classifier Two-Sample Tests.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Revisiting Classifier Two-Sample Tests

Reference 19

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Observation eed6cb3f-4cec-472f-a283-eb91ff6c415b · outbound

This paper cites Benchmarking Simulation-Based Inference.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Benchmarking Simulation-Based Inference

Reference 20

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Observation ebdb8a13-aa6b-41bc-88fe-e00f9b3858cc · outbound

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Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Masked Autoregressive Flow for Density Estimation

Reference 21

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Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust MADE: Masked Autoencoder for Distribution Estimation

Reference 22

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This paper cites LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology

Reference 23

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Observation 40019a4c-f9f1-46dd-8b5b-769d9a7e55bc · outbound

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Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Neural Spline Flows

Reference 24

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Observation 3b31752e-ad38-44d5-95e4-526c43e676cd · outbound

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Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Unresolved cited work

Reference 25

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Observation d3a21c42-27a3-4bb3-9b44-598ad0d92ba0 · outbound

This paper cites Dodelson and F.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Dodelson and F

Reference 26

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Observation 6066106d-6328-47b6-a9e1-c93932426330 · outbound

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Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust Peacock and S

Reference 27

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Observation 95de49f2-ab3d-4203-b8ae-2684da96c7f1 · outbound

This paper cites DegenDetector: Symbolic Recovery of Parameter Degeneracies in Bayesian Posteriors.

Don't Cut Corners: How Training Outside the Prior Makes Simulation-Based Inference More Robust DegenDetector: Symbolic Recovery of Parameter Degeneracies in Bayesian Posteriors

Reference 28

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Pith citing papers

No inbound Pith citation observations are available.