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Paper Citation Record · LEDGER

ReALLM: A general framework for LLM compression and fine-tuning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2405.13155.

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

pith.paper-citation-record.v1
2405.13155 v1

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:44:02.145908Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T04:44:02.545841Z

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 35f56c8b-0bb0-4858-b3b2-6be087608f11 · inbound

RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy cites this paper.

RILQ: Rank-Insensitive LoRA-based Quantization Error Compensation for Boosting 2-bit Large Language Model Accuracy ReALLM: A general framework for LLM compression and fine-tuning

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-12T04:44:02.552693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T04:44:02.145908Z digest=sha256:7750c54529065d36b324b1abaf23b8bdf0fb0bb244f01c7f5ae88741f524beea

Observation 03583293-1b66-40f7-a070-ab6a01569edd · inbound

Neural Weight Compression for Language Models cites this paper.

Neural Weight Compression for Language Models ReALLM: A general framework for LLM compression and fine-tuning

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-04T10:15:14.735476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T10:15:14.735476Z digest=sha256:aba7518e4322f8d806be793250f089655a1c1f1fe87cc405bd88873f5dd51bbe