Paper Citation Record · LEDGER

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning

As of 17 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2608.04285.

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

pith.paper-citation-record.v1
2608.04285 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:43:46.995409Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

39 of 39 outbound references displayed

verified exact5
verified fuzzy23
unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c7fb15cc-c994-4774-b039-aeb29b558513 · outbound

This paper cites Boxe: a box embedding model for knowledge base completion.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Boxe: a box embedding model for knowledge base completion

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:48.075622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f6100829-bb46-4b65-be45-baf463937a21 · outbound

This paper cites Lie point symmetry and physics-informed networks.Advances in Neural Information Processing Systems, 36:42468–42481, 2023.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Lie point symmetry and physics-informed networks.Advances in Neural Information Processing Systems, 36:42468–42481, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:48.057726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eb20eb6f-0443-463c-b9c2-f70fb8a01602 · outbound

This paper cites Program Synthesis with Large Language Models.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Program Synthesis with Large Language Models

Reference 3

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Source-reported events for the cited work

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Observation 4d4c21be-3b5e-409f-9d16-000e7d7a7c26 · outbound

This paper cites Logic tensor net- works.Artificial Intelligence, 303:103649, 2022.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Logic tensor net- works.Artificial Intelligence, 303:103649, 2022

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:48.042763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4716ce6b-a025-4600-a4e8-283358eb9671 · outbound

This paper cites The price of meaning: Why every semantic memory system forgets, 2026.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning The price of meaning: Why every semantic memory system forgets, 2026

Reference 5

Resolution: verified exact; raw_fallback, observed 2026-08-15T14:43:47.629066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e21409cd-dbc6-43c4-bffc-b77f86b8aaac · outbound

This paper cites E(3)-equivariant graph neural networks for data- efficient and accurate interatomic potentials.Nature Communications, 13(1):1–11, 2022.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning E(3)-equivariant graph neural networks for data- efficient and accurate interatomic potentials.Nature Communications, 13(1):1–11, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:48.027358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c72f064f-1a53-426c-8d42-557cea3b5871 · outbound

This paper cites Lamb, Priscila Vieira Lima, Leo de Penning, Gadi Pinkas, et al.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Lamb, Priscila Vieira Lima, Leo de Penning, Gadi Pinkas, et al

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:48.011169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b317cb61-df37-40c2-9c43-56f0c7a1855f · outbound

This paper cites Automated program refinement: Guide and verify code large language model with refinement calculus.Proc.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Automated program refinement: Guide and verify code large language model with refinement calculus.Proc

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T14:43:46.832245Z

Source-reported events for the cited work

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Observation 04493df0-edde-41b0-86d1-b242ee6260a3 · outbound

This paper cites Knowledge graph completion: A review.IEEE Access, 8:192435–192456, 2020.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Knowledge graph completion: A review.IEEE Access, 8:192435–192456, 2020

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T14:43:46.837876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd88da56-9366-4bb9-be8c-7ea246184d97 · outbound

This paper cites Evolving sci- entific discovery by unifying data and background knowledge with ai hilbert.Nature Communications, 15(1):5922, 2024.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Evolving sci- entific discovery by unifying data and background knowledge with ai hilbert.Nature Communications, 15(1):5922, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.995412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3f53349-1566-4a06-a99c-8ce1da585774 · outbound

This paper cites Inductive logic programming at 30: A new introduction.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Inductive logic programming at 30: A new introduction

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.980552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ec8e18b4-a6f5-4f5a-ba58-863ac3067046 · outbound

This paper cites Scientific machine learning through physics-informed neural networks: Where we are and what’s next.Journal of Scientific Computing, 92(3):1–56, 2022.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Scientific machine learning through physics-informed neural networks: Where we are and what’s next.Journal of Scientific Computing, 92(3):1–56, 2022

Reference 12

Resolution: verified exact; doi, observed 2026-08-15T14:43:47.112915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ff393096-88ee-40c3-a6e3-8365168f4613 · outbound

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The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T14:43:46.857215Z

Source-reported events for the cited work

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Observation 7dd9cdd8-e1f9-460f-b314-27f4a823b0dd · outbound

This paper cites Lamb, and Dov M.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Lamb, and Dov M

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.965584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 10f6f1fc-7130-4c7f-9ee5-4e77d7f956ed · outbound

This paper cites Discovering faster matrix multiplication algorithms with reinforcement learning.Nature, 610 (7930):47–53, 2022.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Discovering faster matrix multiplication algorithms with reinforcement learning.Nature, 610 (7930):47–53, 2022

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.951820Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a2c80bfb-d115-4d7a-b209-47ca77f4e415 · outbound

This paper cites an unresolved cited work.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T14:43:46.871298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbb142c0-12ab-41d6-8a02-a39d8c51cab3 · outbound

This paper cites CCN+: A neuro-symbolic framework for deep learning with requirements.International Journal of Approximate Reasoning, 171:109124, 2024.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning CCN+: A neuro-symbolic framework for deep learning with requirements.International Journal of Approximate Reasoning, 171:109124, 2024

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T14:43:46.876100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0188a278-9188-4cc8-a90e-51c8e57ca90f · outbound

This paper cites Hamiltonian neural networks.Advances in Neural Information Processing Systems, 32, 2019.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Hamiltonian neural networks.Advances in Neural Information Processing Systems, 32, 2019

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.938263Z

Source-reported events for the cited work

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Observation 0f14e4a9-5e15-4939-a33c-79ddd0b161dc · outbound

This paper cites Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, and Antoine Zimmermann

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T14:43:46.885118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d5b43cd-fa9e-4daa-9e8e-c6d6ad722a55 · outbound

This paper cites DeepProbLog: Neural Probabilistic Logic Programming.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning DeepProbLog: Neural Probabilistic Logic Programming

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T14:43:46.890220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 079e99c6-9788-4419-9faa-94eca606dcc5 · outbound

This paper cites An in- troduction to anyburl.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning An in- troduction to anyburl

Reference 21

Resolution: verified exact; doi, observed 2026-08-15T14:43:47.070907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 458e6e5a-a107-401a-9982-d1b7a6558d09 · outbound

This paper cites Pearl and D.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Pearl and D

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.923639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 610a29cf-f375-4009-b65f-ffb5c55d1eb0 · outbound

This paper cites Cambridge University Press, 2000.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Cambridge University Press, 2000

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.907743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0da5529c-752a-4220-bab0-7a5abfb83e5f · outbound

This paper cites Purohit, Y.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Purohit, Y

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.889073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d9c7b72-eafd-4346-8c05-4afc898c2b5d · outbound

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The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:43:47.869746Z

Source-reported events for the cited work

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Observation aad64a98-c4ad-4ca2-80bf-2a4186a7c23e · outbound

This paper cites Toolformer: Language models can teach them- selves to use tools.Advances in Neural Information Processing Systems, 36:68539–68551, 2023.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Toolformer: Language models can teach them- selves to use tools.Advances in Neural Information Processing Systems, 36:68539–68551, 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.853763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7c018599-b75c-432d-aa1f-e00777a928ed · outbound

This paper cites Toward causal representation learning.Proceedings of the IEEE, 109(5): 612–634, 2021.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Toward causal representation learning.Proceedings of the IEEE, 109(5): 612–634, 2021

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.837924Z

Source-reported events for the cited work

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Observation e53a6ac0-98ce-4c61-8e23-bf8b20443e00 · outbound

This paper cites Estimating the causal impact of recommendation systems from observational data.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Estimating the causal impact of recommendation systems from observational data

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.821204Z

Source-reported events for the cited work

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Observation ecdc990b-0f44-48d2-9034-87582605cf64 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.nature, 529(7587):484–489, 2016.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Mastering the game of go with deep neural networks and tree search.nature, 529(7587):484–489, 2016

Reference 29

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Source-reported events for the cited work

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Observation a4429aeb-3ef9-419c-9589-eb9055167b6c · outbound

This paper cites ViperGPT: Visual inference via python execution for reasoning.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning ViperGPT: Visual inference via python execution for reasoning

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.788544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 96aa9b43-4de0-40c0-a9df-5a16e5e676a3 · outbound

This paper cites Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases.Applied Intelligence, 51(9):6528–6546, September 2021.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Modular design patterns for hybrid learning and reasoning systems: a taxonomy, patterns and use cases.Applied Intelligence, 51(9):6528–6546, September 2021

Reference 31

Resolution: verified exact; doi, observed 2026-08-15T14:43:47.054816Z

Source-reported events for the cited work

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Observation a7ad16af-ce1d-4a5e-b519-9fc70916735d · outbound

This paper cites Elsevier, 2008.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Elsevier, 2008

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.771356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 00954cf0-56bf-4064-8d15-474b68ab0ed2 · outbound

This paper cites Analyzing differentiable fuzzy logic opera- tors.Artificial Intelligence, 302:103602, 2022.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Analyzing differentiable fuzzy logic opera- tors.Artificial Intelligence, 302:103602, 2022

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.751864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0a684697-0c97-4231-a45a-b604f988da03 · outbound

This paper cites Informed machine learning – a taxonomy and survey of integrating prior knowledge into learning systems.IEEE Transactions on Knowledge and Data Engineering, 35(1):614–633, 2023.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Informed machine learning – a taxonomy and survey of integrating prior knowledge into learning systems.IEEE Transactions on Knowledge and Data Engineering, 35(1):614–633, 2023

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T14:43:46.970658Z

Source-reported events for the cited work

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Observation 27b699de-850e-4b41-8f0a-1f2b3be17f4c · outbound

This paper cites CodeARC: Benchmarking reasoning capabilities of llm agents for inductive program synthesis.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning CodeARC: Benchmarking reasoning capabilities of llm agents for inductive program synthesis

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.731740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6170e8f3-f190-42d7-bd62-2097ff2b20c7 · outbound

This paper cites Contrastive counterfactual visual explanations with overde- termination.Machine Learning, 112:3497–3525, 2023.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Contrastive counterfactual visual explanations with overde- termination.Machine Learning, 112:3497–3525, 2023

Reference 36

Resolution: verified exact; doi, observed 2026-08-15T14:43:47.038229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 04d59640-baf8-43f5-bc86-fc5ea86f3fc3 · outbound

This paper cites ReAct: Synergizing reasoning and acting in language models.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning ReAct: Synergizing reasoning and acting in language models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.707591Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e226931a-1d1f-45b8-803c-424384cfa6b8 · outbound

This paper cites Adaptable logical control for large language models.Advances in Neural Information Processing Systems, 37: 115563–115587, 2024.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Adaptable logical control for large language models.Advances in Neural Information Processing Systems, 37: 115563–115587, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.688791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fe9b853d-36c4-4bbe-8d1b-75ff696ab853 · outbound

This paper cites Position: Trustworthy AI agents require the integration of large language models and formal methods.

The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning Position: Trustworthy AI agents require the integration of large language models and formal methods

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:43:47.669407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.