Paper Citation Record · LEDGER

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach

As of 15 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2411.08463.

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

pith.paper-citation-record.v1
2411.08463 v2

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:35:25.689041Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

18 of 18 outbound references displayed

verified exact2
verified fuzzy1
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3dc16c35-7191-43f0-94b2-18b556f5231a · outbound

This paper cites Bridging Logic and Learning: A Neural-Symbolic Approach for Enhanced Reasoning in Neural Models (ASPER).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Bridging Logic and Learning: A Neural-Symbolic Approach for Enhanced Reasoning in Neural Models (ASPER)

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.595982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a9f597b-1f96-460d-b62e-8f477f411db2 · outbound

This paper cites Information Fusion58, 82–115 (2020).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Information Fusion58, 82–115 (2020)

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.602297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c677b362-89aa-481a-bbb3-4a6657c86ae7 · outbound

This paper cites Cambridge University Press, ISBN 978-0-521-87361-1 (2017).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Cambridge University Press, ISBN 978-0-521-87361-1 (2017)

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.607313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f015e8a6-7877-445e-bad0-540b8d0dc32d · outbound

This paper cites Neural Computing and Applications36(21), 12809– 12844 (2024).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Neural Computing and Applications36(21), 12809– 12844 (2024)

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.612280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb17eb9f-ed86-421b-91d4-4bf387d294b1 · outbound

This paper cites Acta Mechanica Sinica37(12), 1727–1738 (2021).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Acta Mechanica Sinica37(12), 1727–1738 (2021)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.617611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 670e11b7-f233-498c-9815-c0c5c0dac1fd · outbound

This paper cites IEEE Access 10, 88117–88126 (2022).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach IEEE Access 10, 88117–88126 (2022)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.622605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98f0cbfc-8f74-4ec1-8f49-4e5c533a9836 · outbound

This paper cites an unresolved cited work.

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Unresolved cited work

Reference 7

Resolution: verified exact; doi, observed 2026-08-12T21:35:25.824259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fd5ed735-7add-45c6-9f6d-997b4f1c7851 · outbound

This paper cites SymbolicAI: A framework for logic-based approaches combining generative models and solvers.

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach SymbolicAI: A framework for logic-based approaches combining generative models and solvers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.633284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16e33e73-c071-44a7-94c5-5ff5bcfa2b46 · outbound

This paper cites Minds and Machines 28(4), 645–666 (2018).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Minds and Machines 28(4), 645–666 (2018)

Reference 9

Resolution: verified exact; doi, observed 2026-08-12T21:35:25.792533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 63d08b27-8e06-44bf-94d5-a4ed04846398 · outbound

This paper cites Journal of Medical Ethics 47(5), 329–335 (2021).https://doi.org/10.1136/medethics-2020-106820.

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Journal of Medical Ethics 47(5), 329–335 (2021).https://doi.org/10.1136/medethics-2020-106820

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.644053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 927e8f7c-8e6d-4493-bae7-af1990935c3c · outbound

This paper cites Theory and Practice of Logic Programming19(1), 27–82 (2019).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Theory and Practice of Logic Programming19(1), 27–82 (2019)

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.649109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51098478-d053-491d-9155-50f4d580ed30 · outbound

This paper cites In: Proceedings of ICLP/SLP 1988.

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach In: Proceedings of ICLP/SLP 1988

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.654118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11e2f18f-fd74-45c1-9a8b-36c378239557 · outbound

This paper cites Knowl- edge acquisition 5(2), 199–220 (1993).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Knowl- edge acquisition 5(2), 199–220 (1993)

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.660221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3068094-83c9-41a6-bdbe-39ccc7ffb986 · outbound

This paper cites In: IFIP congress.

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach In: IFIP congress

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:35:26.204487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c0486175-52bd-412a-ace0-a4105c8bc552 · outbound

This paper cites Nature521, 436–444 (2015).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Nature521, 436–444 (2015)

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.670496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c992fbae-cdb2-4f51-abcf-a5a714afb5e1 · outbound

This paper cites Artificial Intelligence298, 103504 (2021).

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach Artificial Intelligence298, 103504 (2021)

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.676252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2077baa-0407-4ae5-9f7c-634c40a311eb · outbound

This paper cites The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision.

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T21:35:25.681387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1daf84ac-c4f5-4ed0-a4f8-83cea593939c · outbound

This paper cites In: Proceedings of IJCAI’20.

Symbolic-AI-Fusion Deep Learning (SAIF-DL): Encoding Knowledge into Training with Answer Set Programming Loss Penalties by a Novel Loss Function Approach In: Proceedings of IJCAI’20

Reference 18

Resolution: metadata mismatch; raw_fallback, observed 2026-08-12T21:35:26.054158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.