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Paper Citation Record · LEDGER

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments

As of 11 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2501.00169.

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

pith.paper-citation-record.v1
2501.00169 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:01:04.371165Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

20 of 20 outbound references displayed

  • verified exact0
  • verified fuzzy12
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c5bdcdfa-61a4-493f-b84d-30383096990f · outbound

This paper cites O’Reilly Media, Inc.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments O’Reilly Media, Inc

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.720909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.268299Z digest=sha256:76f3da2cff2b573f9ab8cc1b54cf7971be7f1ec39d335bff8e735226fda70ede

Observation fdf9d4e0-8eec-45e1-838e-8e7def0d0ef7 · outbound

This paper cites an unresolved cited work.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:01:04.704945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.273830Z digest=sha256:4c92f2d6438d128eac361e374a42cdb627b4070d4ecf85671da50cadc944a8fb

Observation d7afe2b2-3f7e-4002-b64d-29737013b471 · outbound

This paper cites Brown and D.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Brown and D

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.688085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.279094Z digest=sha256:dc63e34e3c62f5da8e1e26f56e4b723d6b448d8a35928d7dbf29183b71fb67d7

Observation 7489d3db-1506-4a1a-9c70-48298bccde2f · outbound

This paper cites Chollet and F.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Chollet and F

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.672046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.284965Z digest=sha256:62bd6d1fd6ea2d16f0c9d9f6f361592f4185a237e5b29076bc9718b1f4dee895

Observation 811994d5-2c80-4c25-a5fc-9e999d3559b5 · outbound

This paper cites Di Cosmo and D.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Di Cosmo and D

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.654099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.290968Z digest=sha256:c8ed06f0c4bd8537fc7980d3d52723584274d323ef3b20a3bb1624433d5705c6

Observation ddc9e9b7-7d39-4cc4-acac-9eb5b7fed1a3 · outbound

This paper cites an unresolved cited work.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:01:04.638179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.296441Z digest=sha256:342867c1041384d9205b933c8397899c9220562cffd57d89044e433d9c46a683

Observation 01dc5ec9-b321-44b4-a302-db316a620818 · outbound

This paper cites an unresolved cited work.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Unresolved cited work

Reference 7

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:01:04.622415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.303015Z digest=sha256:2b300600b99408c7efc1eebbf04ea937fb94f42c8b8fb32f78bffe01d6e719ba

Observation 08d8402d-70cb-4916-bddf-e2fb3939d0d8 · outbound

This paper cites Goodfellow, Y.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Goodfellow, Y

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.604892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.308161Z digest=sha256:7ace6e37a8207d5419f5184e24f632505edba59b8e3b17c4bf76b4880a647793

Observation 815a445f-f65a-4490-bb2b-ba725a3e563c · outbound

This paper cites Howard and S.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Howard and S

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.588877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.313238Z digest=sha256:b1659a52ac752cc6dd21d372d02d3d29e9160413fc0fcb86b6949af827db8274

Observation 3c06cb25-b4f1-40b6-b6d8-7331bd328b9e · outbound

This paper cites LeCun, Y.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments LeCun, Y

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-10T23:01:04.318253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:01:04.318253Z digest=sha256:a07cf38f7bf128e28ed699e8ba2ba021383bddb0e5696fe94b938510c8d01949

Observation 5ef0d6e0-70b2-46da-bc73-851ce8abc17a · outbound

This paper cites Martí-Oliet and J.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Martí-Oliet and J

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.561317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.323585Z digest=sha256:456a5abdbc79abe9000713b0e3f1ef9c2819e0b71c3f5741eebc045f79fafaa5

Observation a1363c1b-b421-4f96-8dbe-33f3cca3c2b5 · outbound

This paper cites an unresolved cited work.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:01:04.544867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.328565Z digest=sha256:02b6375be923fc35c747bd847830866dac0636e40a8b736c21f5ee00db246733

Observation 4fd456ce-9139-400a-8f1d-2518c68da385 · outbound

This paper cites an unresolved cited work.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Unresolved cited work

Reference 13

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:01:04.529038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.334216Z digest=sha256:e53341fb0b8f8d1450072683538eeb626d6e4f9c5e9c8c59c402ef0eb0ddbc1d

Observation 1f57efd9-1396-4b86-ac76-013ad1e4ae73 · outbound

This paper cites Salvagno, F.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Salvagno, F

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.512265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.339547Z digest=sha256:77c71fe9dfa660d304bce42fa0cda0ef0933ef836698c6768139d7211aa67722

Observation 762a5baa-9e2b-4420-ae2d-bf17151e494f · outbound

This paper cites O’Reilly Media, Inc.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments O’Reilly Media, Inc

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.494411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.344763Z digest=sha256:3c57d55b403e0a7239be54e322c1d013dacf1fb2a8caa7768117d1a73b7500cf

Observation 3bf1484c-1162-4329-a964-73da68e13f28 · outbound

This paper cites an unresolved cited work.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Unresolved cited work

Reference 16

Resolution
unresolved
raw_fallback, observed 2026-08-10T23:01:04.476665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.349689Z digest=sha256:4d26c480493345c54eae4298a14d488e9e4a4737526f7460e5a874335504634b

Observation bdf84160-1135-4b15-9cea-ad5126d61e1c · outbound

This paper cites Schack-Nielsen and C.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Schack-Nielsen and C

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.459061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.355175Z digest=sha256:8f3421b9898caa2526cb7e689082cabd3a7bf0875d826d2e26363d07bfdb3e31

Observation b6c66241-5491-41cb-a5ff-5c1659816123 · outbound

This paper cites Stevens, L.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Stevens, L

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.443631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.360128Z digest=sha256:98ba7a40f1f2b2b7b3d193871a8a7951c98b6e881ee49e73431fff44daf14379

Observation 06f7c554-ae9d-429d-8c38-06ed84e54a38 · outbound

This paper cites Benchmarking TPU, GPU, and CPU Platforms for Deep Learning.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Benchmarking TPU, GPU, and CPU Platforms for Deep Learning

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T23:01:04.365156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:01:04.365156Z digest=sha256:da9bfbc3bdd031afeec228fdda7875205fcf8a3c1880e39fdfe32d5425f7dd00

Observation f19ca2ac-e061-4653-b052-033b0c51e896 · outbound

This paper cites Watkins, I.

DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments Watkins, I

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T23:01:04.427348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T23:01:04.371165Z digest=sha256:426c518fdb37732300213d6b48e86043940e929824aadb0b306e496db377290f

Pith citing papers

No inbound Pith citation observations are available.