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Paper Citation Record · LEDGER

ZipNN: Lossless Compression for AI Models

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2411.05239.

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

pith.paper-citation-record.v1
2411.05239 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T17:18:51.455245Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 13f2b916-e420-45f9-86c6-38e9454a6290 · inbound

Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference cites this paper.

Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference ZipNN: Lossless Compression for AI Models

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-09T17:18:51.455245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T17:18:51.455245Z digest=sha256:8197a569177e084c6067a33572a88f5d004f834fcaba72b7a7cb1f65e7bb4203

Observation 53e329a7-3820-418c-a5a1-f5ad01a6dc62 · inbound

TStore: Rethinking AI Model Hub with Tensor-Centric Compression cites this paper.

TStore: Rethinking AI Model Hub with Tensor-Centric Compression ZipNN: Lossless Compression for AI Models

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-10T06:21:27.062277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T06:16:58.389670Z digest=sha256:b9d5fd0b12630c1629177b9efa0ea97847803c7db3a5b8cacf18a19f0b33c465

Observation 5281f895-f1ea-496b-a411-3f946bf96b66 · inbound

TStore: Rethinking AI Model Hub with Tensor-Centric Compression cites this paper.

TStore: Rethinking AI Model Hub with Tensor-Centric Compression ZipNN: Lossless Compression for AI Models

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-14T22:08:04.719972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-14T22:04:16.100009Z digest=sha256:fb57e7012d5cb136800e00fcd30dfdfaf663e1dc429a9d5396c4fad21d9cf3d1

Observation 9eb9c7d3-e90c-4a9f-83fa-e643b7be7891 · inbound

Distributed Generative Inference of LLM at Internet Scales with Multi-Dimensional Communication Optimization cites this paper.

Distributed Generative Inference of LLM at Internet Scales with Multi-Dimensional Communication Optimization ZipNN: Lossless Compression for AI Models

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-09T23:04:17.331133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-09T23:01:56.712670Z digest=sha256:22e5f06ae35d6e7efd06c21ffa7e394d10fb4ca505065a4fe22d704b0c01a359

Observation 76bc2c02-ea83-46ac-84f5-173aaa22f5d7 · inbound

ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training cites this paper.

ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training ZipNN: Lossless Compression for AI Models

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-12T10:36:30.646593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-07T05:22:35.487173Z digest=sha256:b01cb238ff38abcc64becf69cc51048a9bfcf3472c7c766c898be0550c22e3e3

Observation e2372ba1-6bd2-4ed6-88f5-b575af9c856a · inbound

SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving cites this paper.

SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving ZipNN: Lossless Compression for AI Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:26:08.847026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-09T16:59:58.809897Z digest=sha256:03efd2dc7c971e3f80fe3a69e8c21358d29ebb3fbfacabc4ae74f1ffc08520c3

Observation 5dbe353d-e02f-4547-8041-ddd5799f94a1 · inbound

SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving cites this paper.

SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving ZipNN: Lossless Compression for AI Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-12T08:21:23.701491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-12T01:15:06.859289Z digest=sha256:fead9c6fb6b769fd14d11679fe6d0d77e3d22adc2ef5edbf9cef4c80bfd55ee4

Observation 56493c13-98b7-43f2-b44e-09ea8451816e · inbound

SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving cites this paper.

SplitZip: Ultra Fast Lossless KV Compression for Disaggregated LLM Serving ZipNN: Lossless Compression for AI Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-01T13:15:45.317927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-01T00:58:28.798369Z digest=sha256:78de922795466555024d140cac826a9d1ffe2f52501b4dd7e83319c732fe8601