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Paper Citation Record · LEDGER

Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.05078.

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

pith.paper-citation-record.v1
2410.05078 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:31:33.586175Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T11:07:01.955129Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d8b99542-12aa-4b38-8321-14cd242410c2 · inbound

Joint Lossless Compression and Steganography for Medical Images via Large Language Models cites this paper.

Joint Lossless Compression and Steganography for Medical Images via Large Language Models Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T05:31:33.586175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:31:33.586175Z digest=sha256:94b611506ef5dc4847cd242bd363f294b9a53b588218689a99304789324868d2

Observation 9ab6b531-3607-4e2a-aa8f-6f904e613104 · inbound

LUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression cites this paper.

LUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression Compression via Pre-trained Transformers: A Study on Byte-Level Multimodal Data

Reference 21

Resolution
verified exact
local_arxiv, observed 2026-07-10T11:07:01.956758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-10T11:05:36.890801Z digest=sha256:1950ffd5f6a51fb3bc9dc04346168e5926896a4493282ca4f5c55041b96b2127