Paper Citation Record · LEDGER

Reassessing Layer Pruning in LLMs: New Insights and Methods

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2411.15558.

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

pith.paper-citation-record.v1
2411.15558 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 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 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:42:06.926464Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T08:40:46.192725Z

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 7d230d89-0053-4eaa-bb79-f3a7f865cef3 · inbound

Latent Flow Transformer cites this paper.

Latent Flow Transformer Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:06.926464Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a77fbc9f-3a5c-47b5-a901-9a8369f35e12 · inbound

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition cites this paper.

FCOS: A Two-Stage Recoverable Model Pruning Framework for Automatic Modulation Recognition Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:50:54.535902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e82cb4d2-288f-4c0f-a32e-967c90a39f45 · inbound

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices cites this paper.

ReStNet: A Reusable & Stitchable Network for Dynamic Adaptation on IoT Devices Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:44:05.696335Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08ace226-6f47-448c-a11b-882ec5e8d735 · inbound

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers cites this paper.

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T12:53:03.806074Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1086b16e-21de-4696-b24a-5c2cd94bf857 · inbound

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture cites this paper.

A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T12:53:14.848355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0dfaeab-3c9a-43e5-82a7-4e052e5f32a5 · inbound

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs cites this paper.

Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:25.164991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 212c36bb-87b6-4678-85a3-46acc148d99e · 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 Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T08:11:51.762463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d3f73d1-a062-4db0-9a51-301124256fa1 · inbound

On the Limits of Layer Pruning for Generative Reasoning in Large Language Models cites this paper.

On the Limits of Layer Pruning for Generative Reasoning in Large Language Models Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:40:46.195212Z

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.

Observation 37559619-48d5-4893-ae66-ead2d411f619 · inbound

Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity cites this paper.

Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity Reassessing Layer Pruning in LLMs: New Insights and Methods

Reference 45

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T05:09:46.277104Z

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.