Paper Citation Record · LEDGER

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent

As of 22 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2605.28612.

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

pith.paper-citation-record.v1
2605.28612 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-29T14:20:55.968621Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

14 of 14 outbound references displayed

verified exact3
verified fuzzy0
unresolved9
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a7df0e79-480b-4b8c-adbd-4fef0d4fec7f · outbound

This paper cites Learning linear block codes with gradient quantization,.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent Learning linear block codes with gradient quantization,

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:23:30.041171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 793605e0-4935-4ae6-8157-52d7345b9602 · outbound

This paper cites Minor, J.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent Minor, J

Reference 2

Resolution: malformed identifier; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27ef9f13-41b1-4ce7-84a8-efa64024939f · outbound

This paper cites an unresolved cited work.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent Unresolved cited work

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:23:30.043827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2a3db404-eb8e-41ab-91aa-e53b886c4a53 · outbound

This paper cites cc/paper_files/paper/2018/file/ 0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent cc/paper_files/paper/2018/file/ 0e64a7b00c83e3d22ce6b3acf2c582b6-Paper

Reference 4

Resolution: verified exact; doi, observed 2026-06-29T14:23:30.040617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4f18f290-1f7b-492f-83ce-8ea8ab1f8606 · outbound

This paper cites F.1–F.2).

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent F.1–F.2)

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b21470e2-e484-4c74-9851-a8ee096e85ae · outbound

This paper cites F.4–F.6).

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent F.4–F.6)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 509dac47-5791-4297-9868-48bafcdd0526 · outbound

This paper cites F.14–F.16).

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent F.14–F.16)

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60eb4dbe-7bb3-4b32-a5e6-79f0453089ef · outbound

This paper cites F.18–F.23).

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent F.18–F.23)

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30af9d1c-e7ac-410e-ace7-5e2efd8651ba · outbound

This paper cites ym −y true m #2 = 1 M MX m=1.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent ym −y true m #2 = 1 M MX m=1

Reference 9

Resolution: malformed identifier; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a587ec09-776e-460c-8799-a9d004858d7e · outbound

This paper cites jump over.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent jump over

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e0a96dd-f73f-44f2-a7b0-d1ab549f4a72 · outbound

This paper cites an unresolved cited work.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d34d0610-1029-4663-bf72-5cd0e88b4688 · outbound

This paper cites Since bounded updates force trajectories to traverse B before exiting the domain, and dynamics withinBprevent further descent below−1/4, the lower boundary is protected.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent Since bounded updates force trajectories to traverse B before exiting the domain, and dynamics withinBprevent further descent below−1/4, the lower boundary is protected

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32a736e4-1fb9-4ccc-b803-27aff20ece74 · outbound

This paper cites unfavorable.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent unfavorable

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a6ec2f1-93bb-4ab8-9204-c511576be69a · outbound

This paper cites Case 1:x[k]>¯x max (outside envelope, above) Hered(x[k]) =x[k]−¯x max and¯x(x[k])≤¯xmax by definition.

Learning High-Dimensional Parity Functions with Product Networks using Gradient Descent Case 1:x[k]>¯x max (outside envelope, above) Hered(x[k]) =x[k]−¯x max and¯x(x[k])≤¯xmax by definition

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T14:20:55.968621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.