Paper Citation Record · LEDGER

A deeper look at depth pruning of LLMs

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2407.16286.

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

pith.paper-citation-record.v1
2407.16286 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:51:51.607178Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T14:50:15.307051Z

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 81592bf1-f8b0-46af-b943-035313db2e30 · inbound

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling cites this paper.

SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling A deeper look at depth pruning of LLMs

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.607178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06e44b8d-493f-4b07-9890-2f66f876fd32 · inbound

OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference cites this paper.

OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference A deeper look at depth pruning of LLMs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T20:08:08.244465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15086fa9-9b1c-4595-9885-d6598ada96d6 · inbound

When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs cites this paper.

When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs A deeper look at depth pruning of LLMs

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T08:11:52.296913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f57a7ca0-7c11-4e34-b4eb-608d48aa0a9a · inbound

Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models cites this paper.

Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models A deeper look at depth pruning of LLMs

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:50:15.310007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.