Paper Citation Record · LEDGER

Performance Law of Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.09895.

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

pith.paper-citation-record.v1
2408.09895 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:17:45.149336Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T07:59:50.152883Z

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 b47af84d-f1e1-4b64-8f68-92f228890c4d · inbound

Making Sense of Korean Sentences: A Comprehensive Evaluation of LLMs through KoSEnd Dataset cites this paper.

Making Sense of Korean Sentences: A Comprehensive Evaluation of LLMs through KoSEnd Dataset Performance Law of Large Language Models

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T20:17:45.149336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 260463cc-8b0c-4f67-927b-6258f7fa70f3 · inbound

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs cites this paper.

Sub-Scaling Laws: On the Role of Data Density and Training Strategies in LLMs Performance Law of Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T17:56:44.114496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c128ef9-2554-4df4-902a-eff5f995336d · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Performance Law of Large Language Models

Reference 145

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:36:20.009360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1f8e786c-f0c4-4712-9c65-d79d6c0260b2 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Performance Law of Large Language Models

Reference 145

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:32:30.202714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21ddd539-9718-4bda-b86e-908212b38276 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices Performance Law of Large Language Models

Reference 145

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:59:50.155691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.