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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:a04a4b2e2a8b5cacdb3520d324f3bfb0b5a3d0702dc4c81ebf75bea475a9b7fe

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:c27ad98f5c63f2bc7beaabf037cd9baa0425161b2f437eb0beffbd801921b46e

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=sha256:15f099ba8a9f0c35bcdd37ce91c0633833e7ac2577f8fb7e9eccf3342cc9d143

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=sha256:3ae8b7321a6b150978f3a5921583f6b8550d4d6b179caa4a45238867d5f69a61

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:cf10733d13983c22bdedbb651965371f9a9baec31b4d462abefebc92e436dee5

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=sha256:04cb3d282a046cc663a18f7106b8475f021be7a6ca3d12b0bb87d2f315c97b18

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=sha256:7041996bc7c9196672f782990dde7adb1a38440b7c2eeaef0255eff15533367c

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=sha256:9cf24e6c3bab997f63960ff3db4636aab6ac0607c362493b0c249951c970269e

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=sha256:1b733e06b3f166d0e6aca0ac279cd1fd0d1199f39c81b02ef3c8593349ab4575

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=sha256:9d0e0ef851ebecf4e82a36fd94195fa934f85add866c86eba4dca325c35eade0

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=sha256:7fc906e15a764366b4b68d652178e95339cb0863dcc1a425386c00b255f42171

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=sha256:96687e0e516f40b888a610ff653a094c6eacc219cd219d80bdfaafb5a071a1fd

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=sha256:8448158fc9a9403eb230e24f8a8e7e1ca3b774c694fc108851cfe9ea5075b394

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:ac315544e2b7066b8947e0703c05761ff4fb86a487115223507ec3f34a4f07a7

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:c5409a39db449a985b234f31609fa181b7e5465f91a36327acc9f34c54153feb

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=sha256:37c7a90074b01ca42bae45326bbf2f56c6b90aeadc9809f6f6a1fddc7edbcc09

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:a457281fdc9fb578d0f21500c8e559e90889ceb1b0c340a184c22ad36a1340c7

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:c33d952099bb61da363470421b2d09b6d5a5ebd7c054335b02cf493bc516187a

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:c62d5b4b6c447dd5f6ffe436b8502ea8054c82528523be2290578a65f6a0dd9a

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:eb6382463a614d728425e8b2b9e1d8d2e595a94ef32c4b6ec5e9831115adaa7c

Pith citing papers

No inbound Pith citation observations are available.