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Paper Citation Record · LEDGER

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2505.17623.

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

pith.paper-citation-record.v1
2505.17623 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:53:19.349258Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

32 of 32 outbound references displayed

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  • verified fuzzy29
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation ba4c2189-73fc-4906-8c5b-5c061f575058 · outbound

This paper cites Accountable magic: Ai alignment and governance, 2025.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Accountable magic: Ai alignment and governance, 2025

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:16.379618Z digest=sha256:1cc5759a338f617df45c6d39a8befe289e345ea1964caaba050e820bed0778df

Observation d787b075-d475-4db6-b0ce-afbda2212789 · outbound

This paper cites The sum-check protocol over fields of small characteristic.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party The sum-check protocol over fields of small characteristic

Reference 2

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raw_fallback, observed 2026-08-07T14:53:25.591092Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:16.449350Z digest=sha256:8e1e716e116b2ab9aca2725d31193ac1ea68905d2b10e351bd0bee6b6b98ca93

Observation d2e6fde2-ba41-4d7c-a279-3820770d561a · outbound

This paper cites Deep learning, volume 1.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Deep learning, volume 1

Reference 3

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:16.516837Z digest=sha256:304bd33e33852baa3059c5387e1474f2ed8858260011590db9bfd8938e3b11f6

Observation 52e3855b-a328-41c8-afa7-67349aecca6e · outbound

This paper cites Sumcheck arguments and their applications.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Sumcheck arguments and their applications

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:16.616694Z digest=sha256:060ad460c82a6db1494d4ddacbd0641032008db5cc45a38d6e21dd068bc3e3b9

Observation 8f033ce9-1723-4cd8-a82a-223bd4528000 · outbound

This paper cites Bulletproofs: Short proofs for confidential transactions and more.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Bulletproofs: Short proofs for confidential transactions and more

Reference 5

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raw_fallback, observed 2026-08-07T14:53:25.165601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:16.766976Z digest=sha256:2b07a869f2e25c12abe05f3af6bf7773eaef2e7a9c333f1977a99716878583fe

Observation 2b7b5b39-4b92-456c-bb53-7e4a3e214302 · outbound

This paper cites Interactive proofs for rounding arithmetic.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Interactive proofs for rounding arithmetic

Reference 6

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raw_fallback, observed 2026-08-07T14:53:24.988252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:16.862661Z digest=sha256:ba31c750e74269ec28474e04d2bca249d99bf90a2e421cc1fa65d69235e910bd

Observation 93b4975b-2608-4bab-8ac6-bd2451f0acae · outbound

This paper cites More optimizations to sum-check proving.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party More optimizations to sum-check proving

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:16.946825Z digest=sha256:36b8cf6fbd47b0a6de947d9b265d76184cf2fd81541003174c2b91139a1cdc9e

Observation 63a715ad-ebfb-4a5b-9813-f0d1f6512fa5 · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party The mnist database of handwritten digit images for machine learning research

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:53:17.076305Z digest=sha256:5f4101e9b0e2d79b7483162c59fbf342f49f783b25aa09e5253344f359aa824d

Observation 834abe0e-4e6b-4d64-9ede-2ee064c26949 · outbound

This paper cites EZKL: Zero-knowledge machine learning, 2025.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party EZKL: Zero-knowledge machine learning, 2025

Reference 9

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:17.170979Z digest=sha256:18729b692f688af9e19f57dcddbfe5a950d78e538e11aa2b014f65028fb63eef

Observation 5030d8cd-7666-4174-bb5e-855f50d2e205 · outbound

This paper cites Succinct zero knowledge for floating point computations.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Succinct zero knowledge for floating point computations

Reference 10

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:17.261610Z digest=sha256:51ea03be77f3e814c46e99c1ef26d7a24ad14bd2d78a4008dff1c9695fc555b9

Observation e22dd29c-668a-40cc-ab53-f75e0742d648 · outbound

This paper cites Experimenting with zero-knowledge proofs of training.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Experimenting with zero-knowledge proofs of training

Reference 11

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:17.331680Z digest=sha256:15eb7b6ee72bf14d50b17be3c700e25ea23c81f884a196bfa33f099ebb2d0434

Observation 0c0f5ccb-bb03-43ae-a368-df71b5864aed · outbound

This paper cites Safetynets: Verifiable execution of deep neural networks on an untrusted cloud.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Safetynets: Verifiable execution of deep neural networks on an untrusted cloud

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:17.426866Z digest=sha256:a78ab531e1532ffb2e0ca9bb6a35bb21566c05e33814a9a0ceb0f0e5398caf8d

Observation 834e2428-d655-4800-ab8d-e8c128d45bd5 · outbound

This paper cites Efficient sum-check protocol for convolution.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Efficient sum-check protocol for convolution

Reference 13

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T14:53:17.522737Z digest=sha256:1c178de1560d40d2dac539f9e1f13ea6ae3b2fdd74f5ac5f8e2204779a51e16e

Observation 21c0c345-f2cc-4138-aeec-433cf8783b0a · outbound

This paper cites Snargs and ppad hardness from the decisional diffie-hellman assumption.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Snargs and ppad hardness from the decisional diffie-hellman assumption

Reference 14

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source=pdf_text observed=2026-08-07T14:53:17.592605Z digest=sha256:600c24836e2cdab8eacb29a393e1afa7c6da1be7de22a3ab73668c623a9426a2

Observation 44dd1586-3300-47a1-bd33-1ec1b8b1d85f · outbound

This paper cites Sparsity-aware protocol for zk-friendly ml models: Shedding lights on practical zkml.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Sparsity-aware protocol for zk-friendly ml models: Shedding lights on practical zkml

Reference 15

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source=pdf_text observed=2026-08-07T14:53:17.651697Z digest=sha256:745ce7036b2aa8fa7f49c245f195377ae7b9f3ddd19d5c8c29dd8f5994004f55

Observation fedcc26d-d03b-412e-a56f-bd67894c85fb · outbound

This paper cites Artemis: Efficient Commit-and-Prove SNARKs for zkML.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Artemis: Efficient Commit-and-Prove SNARKs for zkML

Reference 16

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:53:17.728177Z digest=sha256:5c27d1bdd7697e418c3ae0e8831b1036cea9474beded3749e71c2c36f3e020ac

Observation 5bd5a817-7a76-4b3e-9f50-e0cfb49d0007 · outbound

This paper cites Noya: Ai-powered infrastructure for zero-knowledge proofs, 2025.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Noya: Ai-powered infrastructure for zero-knowledge proofs, 2025

Reference 17

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source=pdf_text observed=2026-08-07T14:53:17.785246Z digest=sha256:833fe6fcf928346f3e7005fc2d629df4b549c2faa4ce467a0dc5b312e4647442

Observation 8b08de96-e0c3-473b-afff-a08ae6d09c1e · outbound

This paper cites Pinocchio: Nearly practical verifiable computation.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Pinocchio: Nearly practical verifiable computation

Reference 18

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source=pdf_text observed=2026-08-07T14:53:17.855847Z digest=sha256:f1e59b3e9c70001dbbeb35d5b97830c4bbb0b932882ca4a797c16e2f89400ac0

Observation 29a06769-359b-495c-a103-7e672536f86a · outbound

This paper cites A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:53:17.908968Z digest=sha256:8cb403740a625ede1d22ba7a09def5a0432d1ebf598b10e4cb29d4ae19b12464

Observation ad6d2988-e51b-474b-81b6-84d199d40b84 · outbound

This paper cites Polygon zkEVM: Ethereum scaling with zero-knowledge proofs, 2025.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Polygon zkEVM: Ethereum scaling with zero-knowledge proofs, 2025

Reference 20

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source=pdf_text observed=2026-08-07T14:53:17.983600Z digest=sha256:3a58cbacaf14a1faa96409d846cfa6b8bae178b2c13f4b8d3ba113aef70ecf72

Observation ac8da78a-84eb-47a6-8a36-52fa2f1dc1c6 · outbound

This paper cites Provably: Trustless ai verification, 2025.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Provably: Trustless ai verification, 2025

Reference 21

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source=pdf_text observed=2026-08-07T14:53:18.048425Z digest=sha256:29f134c04306a688ecc469fa0c772c2b25553dec4c2806363bc6f1b7ff70adc5

Observation 057c027a-5702-430e-a87b-6f9c0a10a8b7 · outbound

This paper cites Fast probabilistic algorithms for verification of polynomial identities.Journal of the ACM (JACM), 27(4):701–717, 1980.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Fast probabilistic algorithms for verification of polynomial identities.Journal of the ACM (JACM), 27(4):701–717, 1980

Reference 22

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source=pdf_text observed=2026-08-07T14:53:18.147886Z digest=sha256:803dfa02af1a4084f6fb0f22287b81e45f0614da4a85ac0df73909d85add4b8b

Observation 2771fa69-99b5-4a0c-8967-1faaba081246 · outbound

This paper cites Proofs, arguments, and zero-knowledge.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Proofs, arguments, and zero-knowledge

Reference 23

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raw_fallback, observed 2026-08-07T14:53:21.076279Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:18.272142Z digest=sha256:41dae9297f71108f31c02fecdcec395f575fdfc6fe6d58fb2142fb093f054520

Observation d109cfd2-e3f7-4418-866a-62acaa427265 · outbound

This paper cites Mystique: Efficient conversions for {Zero-Knowledge} proofs with applications to machine learning.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Mystique: Efficient conversions for {Zero-Knowledge} proofs with applications to machine learning

Reference 24

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:18.403274Z digest=sha256:cf7de4bae8bbf6e204a05cc2fe829ad02d301e825717278f4411cc034ac6fdcc

Observation 2921e33c-0dbb-4e0c-9269-2db695cab384 · outbound

This paper cites pvcnn: Privacy- preserving and verifiable convolutional neural network testing.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party pvcnn: Privacy- preserving and verifiable convolutional neural network testing

Reference 25

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:18.507047Z digest=sha256:1d6ad2df3d6fe8b654f18cd767c494bc6a377f7d695443dd1213f032ab5fe3c1

Observation 559e62c5-8ad4-468d-860d-aa658cf15e1b · outbound

This paper cites Validating the integrity for deep learning models based on zero-knowledge proof and blockchain.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Validating the integrity for deep learning models based on zero-knowledge proof and blockchain

Reference 26

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:18.615574Z digest=sha256:5d6f22bd0cd007a806f036bed4fe5921f0f09289e75379784e1040b56849d6e2

Observation 6cf0021e-94b8-4cd7-b06e-ca835de6287e · outbound

This paper cites Probabilistic algorithms for sparse polynomials.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Probabilistic algorithms for sparse polynomials

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:18.742022Z digest=sha256:b0f7e6ad9495424b56186346104431cfd42ce4454502ee82bbc54e835c6ffdd3

Observation 7460ef40-f726-4cc3-846e-715dbce7a04a · outbound

This paper cites zkAGI: Zero-knowledge artificial general intelligence, 2025.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party zkAGI: Zero-knowledge artificial general intelligence, 2025

Reference 28

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:18.851268Z digest=sha256:a595cb73ad2abf96ce92340dde20541d78e2ba12febdac8d574fd2b66b67f531

Observation 6caf21b5-fd68-46e7-a64a-5c07708f9e93 · outbound

This paper cites ZKML: Zero-knowledge machine learning systems, 2025.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party ZKML: Zero-knowledge machine learning systems, 2025

Reference 29

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raw_fallback, observed 2026-08-07T14:53:20.219019Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:18.993979Z digest=sha256:e540d693bfb4c3e1592c16083427329f23c6bc579b644e9990daec560e92e2f2

Observation c10b230b-ca16-4f10-a2f0-660520bcae94 · outbound

This paper cites From these inequalities, we deduce that −2t+s+2 + 1≤ (e2 − e1) + 2s × (a′ 2 − a′.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party From these inequalities, we deduce that −2t+s+2 + 1≤ (e2 − e1) + 2s × (a′ 2 − a′

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:19.118901Z digest=sha256:2c47a619da252603e0771d5d35aa88747185d3f8a34a954fd41ab99515567513

Observation 992c89f8-fcea-49c1-86b5-5f5c742acdbe · outbound

This paper cites Since p has at least s + t + 3bits, we can conclude that 2s+t+2 ≤ p.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Since p has at least s + t + 3bits, we can conclude that 2s+t+2 ≤ p

Reference 31

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raw_fallback, observed 2026-08-07T14:53:19.821184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:19.224191Z digest=sha256:0deccf0b07b57a7b643ba4de31329b374d7674652e6d3da7aed5ce9f982cbbec

Observation aaab3724-5936-45a9-9a37-2c3cf1c32cc1 · outbound

This paper cites Given the bounds on e1 and e2 as well as the multiple of 2s, it follows that e1 = e2, and consequently, a′ 1 = a′ 2.

\texttt{Range-Arithmetic}: Verifiable Deep Learning Inference on an Untrusted Party Given the bounds on e1 and e2 as well as the multiple of 2s, it follows that e1 = e2, and consequently, a′ 1 = a′ 2

Reference 32

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raw_fallback, observed 2026-08-07T14:53:19.590882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T14:53:19.349258Z digest=sha256:5ce3f753b76ea158400c9faee16c4b996590c9ce98af52607f7a32a193c942c0

Pith citing papers

No inbound Pith citation observations are available.