Paper Citation Record · LEDGER

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms

As of 23 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2502.08932.

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

pith.paper-citation-record.v1
2502.08932 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T23:14:42.033243Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T18:04:09.528381Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T18:06:43.047116Z

Reference resolution

42 of 42 outbound references displayed

verified exact1
verified fuzzy27
unresolved14
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c638fdb3-4cef-4edf-aa52-4e1eaba37b45 · outbound

This paper cites Addison- Wesley Reading, 1995.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Addison- Wesley Reading, 1995

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.500514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 80a6136d-9347-4726-8b56-24ee0e545714 · outbound

This paper cites Measuring trustworthiness in neuro-symbolic integration.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Measuring trustworthiness in neuro-symbolic integration

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.486080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 325d8ea2-d3a1-4168-9ead-78187c3fce91 · outbound

This paper cites Neurosymbolic reinforce- ment learning with formally verified exploration.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Neurosymbolic reinforce- ment learning with formally verified exploration

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.470289Z

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Observation a7fcf8f5-9349-4456-a75c-292cdb2fdf5f · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms wav2vec 2.0: A framework for self-supervised learning of speech representations

Reference 4

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Observation c8dc63d2-ed3a-4365-8128-f97814e3ba6b · outbound

This paper cites A survey on artificial intelligence assurance.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms A survey on artificial intelligence assurance

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.420000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 594b533c-f94c-4859-9bd3-de86cc9f07a0 · outbound

This paper cites Rotational equivariance for object classification using xview.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Rotational equivariance for object classification using xview

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.371099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7de72641-7514-4aa2-add2-4a702c8a1fad · outbound

This paper cites Remote Timing Attacks on Efficient Language Model Inference.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Remote Timing Attacks on Efficient Language Model Inference

Reference 7

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Source-reported events for the cited work

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Observation 68d3ca2d-98c6-40d8-87a8-09f0185bc370 · outbound

This paper cites Neurosymbolic programming.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Neurosymbolic programming

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.355912Z

Source-reported events for the cited work

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Observation 88c7fff9-71e8-42ad-9d3f-b70bcab84eec · outbound

This paper cites Certified adversarial robustness via randomized smoothing.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Certified adversarial robustness via randomized smoothing

Reference 9

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Source-reported events for the cited work

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Observation d8689811-2feb-42ab-b7e7-0921b5a9af26 · outbound

This paper cites Very deep convolutional neural networks for raw waveforms.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Very deep convolutional neural networks for raw waveforms

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.331756Z

Source-reported events for the cited work

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Observation 5741f69f-1777-4361-abe2-458b18344359 · outbound

This paper cites an unresolved cited work.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Unresolved cited work

Reference 11

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Source-reported events for the cited work

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Observation a825a9c6-b90b-490e-8040-4545187790c0 · outbound

This paper cites advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch

Reference 12

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Observation 2d65c904-446e-48b5-b54a-097d6503adb0 · outbound

This paper cites Towards Deep Neural Network Architectures Robust to Adversarial Examples.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Towards Deep Neural Network Architectures Robust to Adversarial Examples

Reference 13

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Observation 63c30fcf-e936-4b0b-bc26-23b50b978bfe · outbound

This paper cites Deliberative Alignment: Reasoning Enables Safer Language Models.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Deliberative Alignment: Reasoning Enables Safer Language Models

Reference 14

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Observation d8216dcd-9096-430c-9a5d-df16e0ff1ce3 · outbound

This paper cites On calibration of modern neural networks.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms On calibration of modern neural networks

Reference 15

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Source-reported events for the cited work

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Observation e9eb9ada-5036-4042-ac0b-577fdbd388c2 · outbound

This paper cites Deep residual learning for image recognition.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Deep residual learning for image recognition

Reference 16

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Observation 2683cd4f-f2e2-45f3-a1e3-9cf66da59c95 · outbound

This paper cites Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

Reference 17

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Source-reported events for the cited work

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Observation a1fdeb71-a360-4b18-8fdc-962d73623b20 · outbound

This paper cites Scallop: From probabilistic deductive databases to scalable differentiable reasoning.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Scallop: From probabilistic deductive databases to scalable differentiable reasoning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.159592Z

Source-reported events for the cited work

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Observation 3d4714ba-20c5-4c26-8432-f4358f05e978 · outbound

This paper cites An empirical study of rich subgroup fairness for machine learning.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms An empirical study of rich subgroup fairness for machine learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.108520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 81b044dc-61ad-40a7-83ab-d2e46c34949b · outbound

This paper cites Deepproblog: Neural probabilistic logic programming.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Deepproblog: Neural probabilistic logic programming

Reference 20

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Source-reported events for the cited work

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Observation 47857212-f105-481b-a7a5-34726fc36a17 · outbound

This paper cites Not all neuro- symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Not all neuro- symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:43.080344Z

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Observation f1eca002-3128-4538-bad0-8f6aaabe0c6e · outbound

This paper cites Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Reference 22

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Observation e899e7e2-6b91-4e6e-a576-cdacc1947c3c · outbound

This paper cites Neuro-symbolic methods for trustworthy ai: a systematic review.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Neuro-symbolic methods for trustworthy ai: a systematic review

Reference 23

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Observation 7ded53f0-ad96-4f8c-9500-2289bf629ff8 · outbound

This paper cites MNIST-C: A Robustness Benchmark for Computer Vision.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms MNIST-C: A Robustness Benchmark for Computer Vision

Reference 24

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Observation cc8e99f7-7557-44ab-9bde-0e0a8ae028c7 · outbound

This paper cites Do machine learning models learn statistical rules inferred from data? In International Conference on Machine Learning, pages 25677–25693.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Do machine learning models learn statistical rules inferred from data? In International Conference on Machine Learning, pages 25677–25693

Reference 25

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Observation da271dcc-1cd9-43bb-8fa3-a26a9acdaabf · outbound

This paper cites Fairness in machine learning.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Fairness in machine learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:42.777123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9d2f1545-9bcc-4fb6-8dbb-c246a59ee859 · outbound

This paper cites The limitations of deep learning in adversarial settings.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms The limitations of deep learning in adversarial settings

Reference 27

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Source-reported events for the cited work

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Pytorch: An imperative style, high-performance deep learning library

Reference 28

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Observation 3510b1f4-ad85-4cfc-b0dd-3fd64af6f03e · outbound

This paper cites Formal Explanations for Neuro-Symbolic AI.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Formal Explanations for Neuro-Symbolic AI

Reference 29

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Observation dc5f63d5-dc18-40bb-ae4b-a9d01ddd8b3e · outbound

This paper cites An empirical study on the robustness of knowledge injection techniques against data degradation.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms An empirical study on the robustness of knowledge injection techniques against data degradation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:42.715332Z

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Observation c359bf40-a7e6-4eba-bc53-89d1a3dbeff8 · outbound

This paper cites Certified robustness to label-flipping attacks via randomized smoothing.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Certified robustness to label-flipping attacks via randomized smoothing

Reference 31

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This paper cites Neuro-symbolic artificial intelligence: Current trends.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Neuro-symbolic artificial intelligence: Current trends

Reference 32

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On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Part-based models improve adversarial robustness

Reference 33

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On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Data-Efficient Learning with Neural Programs

Reference 34

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This paper cites Measuring robustness to natural distribution shifts in image classification.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Measuring robustness to natural distribution shifts in image classification

Reference 35

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This paper cites Long range arena: A benchmark for efficient transformers.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Long range arena: A benchmark for efficient transformers

Reference 36

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Observation d55e0b31-3d7e-4d8a-8339-e22bc9e32392 · outbound

This paper cites How fair can we go in machine learning? assessing the boundaries of accuracy and fairness.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms How fair can we go in machine learning? assessing the boundaries of accuracy and fairness

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:42.434802Z

Source-reported events for the cited work

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Observation bd016674-4e20-4391-bfbd-2213d0e6ca90 · outbound

This paper cites Wagner and Artur d’Avila Garcez.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Wagner and Artur d’Avila Garcez

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:42.346534Z

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This paper cites Simple statistical gradient-following algorithms for connectionist reinforce- ment learning.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Simple statistical gradient-following algorithms for connectionist reinforce- ment learning

Reference 39

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This paper cites Safe neurosymbolic learning with differentiable symbolic execution.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Safe neurosymbolic learning with differentiable symbolic execution

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:42.321780Z

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This paper cites Fairness beyond disparate treatment & disparate impact: Learning classification without dis- parate mistreatment.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Fairness beyond disparate treatment & disparate impact: Learning classification without dis- parate mistreatment

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:42.306745Z

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This paper cites Detect adversarial attacks against deep neural networks with gpu monitoring.

On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms Detect adversarial attacks against deep neural networks with gpu monitoring

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T23:14:42.252061Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 163f24a2-0972-455b-898a-988f53c4293b · inbound

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities cites this paper.

Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunities On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms

Reference 104

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.