Paper Citation Record · LEDGER

Investigating Layer Importance in Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2409.14381.

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

pith.paper-citation-record.v1
2409.14381 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:20:51.230669Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 92751710-f78b-42f9-9984-3b16e99aa25c · inbound

RAP: Runtime Adaptive Pruning for LLM Inference cites this paper.

RAP: Runtime Adaptive Pruning for LLM Inference Investigating Layer Importance in Large Language Models

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:21:35.460178Z

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 c5623f59-da6b-48f4-81dd-6f7bd2954a2d · inbound

A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation cites this paper.

A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation Investigating Layer Importance in Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-07T05:20:51.230669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29aabc29-a33f-464c-ae12-608be072466e · inbound

DipSVD: Dual-importance Protected SVD for Efficient LLM Compression cites this paper.

DipSVD: Dual-importance Protected SVD for Efficient LLM Compression Investigating Layer Importance in Large Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T22:58:09.448270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa8c8dd9-155e-4530-8f37-65efc5f0e69e · inbound

A Survey on Latent Reasoning cites this paper.

A Survey on Latent Reasoning Investigating Layer Importance in Large Language Models

Reference 137

Resolution: unresolved; no resolver link, observed 2026-08-06T19:14:34.146213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acec151c-263f-4e90-81a4-32562f1924b2 · inbound

EvolKV: Evolutionary KV Cache Compression for LLM Inference cites this paper.

EvolKV: Evolutionary KV Cache Compression for LLM Inference Investigating Layer Importance in Large Language Models

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T20:53:30.630054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bf80d580-e5c1-4cea-b8cd-ad7b0c8a182f · inbound

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations cites this paper.

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations Investigating Layer Importance in Large Language Models

Reference 68

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:50:30.176074Z

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 d322b00f-808d-454e-8896-1999524c75ef · inbound

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations cites this paper.

You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations Investigating Layer Importance in Large Language Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-03T23:21:42.287229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a55c69f-d060-453e-ba59-56aac5369466 · inbound

Are LLM Uncertainty and Correctness Encoded by the Same Features? A Functional Dissociation via Sparse Autoencoders cites this paper.

Are LLM Uncertainty and Correctness Encoded by the Same Features? A Functional Dissociation via Sparse Autoencoders Investigating Layer Importance in Large Language Models

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:53:28.975338Z

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 33cae471-4b56-45d2-bce0-2b4b6a3b2b3e · inbound

Statistically-Lossless Quantization of Large Language Models cites this paper.

Statistically-Lossless Quantization of Large Language Models Investigating Layer Importance in Large Language Models

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T16:31:10.019435Z

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 901b0e99-c7ff-4315-9b8f-e75ed1421a2f · inbound

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training cites this paper.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Investigating Layer Importance in Large Language Models

Reference 15

Resolution: malformed identifier; arxiv_id, observed 2026-07-02T14:57:03.892472Z

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 863f83a1-83b8-42c2-a51a-3190a8161e1d · inbound

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training cites this paper.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Investigating Layer Importance in Large Language Models

Reference 15

Resolution: malformed identifier; arxiv_id, observed 2026-07-03T21:38:58.228463Z

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.