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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:16.379618Z digest=sha256:fd9d8bf3f8b805893b2a8d3236fab18f172c1d3fb2bd0e364d083a98f2629d41

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:16.449350Z digest=sha256:a896d825efe4285124589bef09bc4d15f3a3529a6dcecb674580ffe2c8e5f567

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:16.516837Z digest=sha256:3852a73e4ef8ed73bb8fea08e0152edbc13604e5b59c27c8407f735b6163744c

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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raw_fallback, observed 2026-08-07T14:53:25.312706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:16.616694Z digest=sha256:260dec1f95184e09b8be325f4d04ccc1817dfb6e19288e5640984f002942d85b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:16.766976Z digest=sha256:5cea1e6183171589586685d6be49f0591985bf661652200e9a85b55ea85ac1fe

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:16.862661Z digest=sha256:29dd53764cfc6dbab8f37a2b76e67b38d35218aa1efb016fcd5607590b869069

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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:16.946825Z digest=sha256:143a7f8d9da9b63b0620bd3a3915b8a1d0300d246605ace3b167e04cc952ea6e

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=sha256:970d9d6b0ef4b6005ec1e3f2eb83acf03ac3123f0d99c907a72b00a4f9425a26

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:17.170979Z digest=sha256:d52960a262c68fdfca68d034d6ef70180108d4967c07cf005b391c342e7b7954

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:17.261610Z digest=sha256:bbd6423764213f4fb3e4beb95b1c8b1509bd54659de3babb4c1d4e5a9142b734

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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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=pdf_text observed=2026-08-07T14:53:17.426866Z digest=sha256:7cbef2abb7625ab8c40df91506a6112f8611af44550b80624dfc03cc7ac32c85

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=pdf_text observed=2026-08-07T14:53:17.522737Z digest=sha256:a03333a36df5e98b2f10cd383f790a7a5732f6672badc968676d51c5a728b1fb

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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raw_fallback, observed 2026-08-07T14:53:23.491177Z

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source=pdf_text observed=2026-08-07T14:53:17.592605Z digest=sha256:7f2b46ad24345195d9d20204bebc61436201d4da67b8ecdaca452c5147685dc4

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=sha256:5b44dc08095528a03852f07c956001d4ca4b5a6512e2dd7109708bb1b7edf40f

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:d622cd7f8b4824b22a227ddd45ac93d74ea979fb04b65b3f8725f921779fef5b

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:c87eb95aea5ea53b8b75338ee73e8f27137b3493a592c68f05a238c22fdac6ed

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=sha256:6b8d13c7d8dc6211d654ad9d2318e7101713ffe2db40c31b3b27de5ba7663fa4

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=sha256:07289b59eec12906f4ec36c8e5653b252d04a4d2fc7b09da13494a8e9c598782

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=sha256:6826d69c0f8bbb84266f3d33add63c73738e83ba76574ab85dc2d6b902764e0c

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:dd17b23ce017b604cda3f2373d30f602f8286ae92db3c285f1b6798c15612b35

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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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:18.147886Z digest=sha256:4b9acbb2f54623e2ad11b2de361f0a5c0e41a1f0b3508a211467db4c5e8a739d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:18.272142Z digest=sha256:26942c1baa50f180ae610cd31b18393127c794b649c75bcc42836aa33dee1a74

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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raw_fallback, observed 2026-08-07T14:53:20.958526Z

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source=pdf_text observed=2026-08-07T14:53:18.403274Z digest=sha256:19f1bc47752f5e74505b64580ad1309cbac2ad47334c828c8c9d522599e62e53

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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raw_fallback, observed 2026-08-07T14:53:20.784485Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:18.507047Z digest=sha256:4b77b5e176e9d2e4d151f0dd3f4a8f1f7e9076e57cc991d7aaa8afa40e9f8b8f

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:18.615574Z digest=sha256:518f1b26c645073734d1fb181c9731b6ded5cfb420c7f0445310d7030b1e40af

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:18.742022Z digest=sha256:6a9ad636513d39c9b3d0aecff0dd3f96ce6ec7a9fe63a9dd1881ebbca8276219

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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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:18.851268Z digest=sha256:ffca1603978d40ba56195f59ecb5cd111cd7b8c2c2c89883641e69143cfe8f54

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:18.993979Z digest=sha256:04b757e0ba7adf86f49629d7e6133a95110e555a349a0dbb02ec740e764c765b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:19.118901Z digest=sha256:e4e4e23a8edc87135aefa134213f506c513afe46c41c05eb654a84e55aab6954

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:19.224191Z digest=sha256:4963da5cd147458845c5e4c46773176a9a08917d7af0c55c56e51be8129f65d8

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-07T14:53:19.349258Z digest=sha256:4e65b40511b2229dda54f07b0ca6ad8a16637528d4ffe044dc8f93fad5f292ec

Pith citing papers

No inbound Pith citation observations are available.