Paper Citation Record · LEDGER

Streamlining Redundant Layers to Compress Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2403.19135.

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

pith.paper-citation-record.v1
2403.19135 v5

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 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:19.050049Z

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

3
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 c6e43979-3966-4e2b-a5da-b8f2a4b90edd · inbound

SlimLLM: Accurate Structured Pruning for Large Language Models cites this paper.

SlimLLM: Accurate Structured Pruning for Large Language Models Streamlining Redundant Layers to Compress Large Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:19.050049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 567103ee-da14-48b1-994e-2e33bbada56b · 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 Streamlining Redundant Layers to Compress Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T10:51:51.433698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cc4256c-a7b8-4d46-9afe-4135f99a4ca6 · inbound

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones cites this paper.

Projectable Models: One-Shot Generation of Small Specialized Transformers from Large Ones Streamlining Redundant Layers to Compress Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:23.334815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49a42d07-a58b-4e49-9cf0-388dc536869d · inbound

Prune&Comp: Free Lunch for Layer-Pruned LLMs via Iterative Pruning with Magnitude Compensation cites this paper.

Prune&Comp: Free Lunch for Layer-Pruned LLMs via Iterative Pruning with Magnitude Compensation Streamlining Redundant Layers to Compress Large Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T14:48:28.208165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c9511e9-cd8f-4a98-a2a2-b3d273406492 · inbound

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers cites this paper.

Short-LVLM: Compressing and Accelerating Large Vision-Language Models by Pruning Redundant Layers Streamlining Redundant Layers to Compress Large Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T10:55:18.094704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59f40061-b6f2-4e58-aaf8-8fc44800932a · 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 Streamlining Redundant Layers to Compress Large Language Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:40:46.188779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1e7528d4-126e-4adc-8b72-d00acb658c02 · inbound

A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Models cites this paper.

A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Models Streamlining Redundant Layers to Compress Large Language Models

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-29T23:04:01.071339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 57064e4b-035f-4ee1-a172-47512001f9c4 · inbound

A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Models cites this paper.

A Hamiltonian-Inspired Local-Operator Ansatz for Slimming Large Language Models Streamlining Redundant Layers to Compress Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T05:01:02.972092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54a4ea46-4664-436b-a2a3-ae80b4d5b0cb · inbound

Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling cites this paper.

Dense2MoE: Pushing the Pareto Frontier of On-Device LLMs via Unified Pruning and Upcycling Streamlining Redundant Layers to Compress Large Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:55.050399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9cbff2c7-a761-444e-9274-8e25d7ebb107 · inbound

Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think cites this paper.

Finetuning Vision-Language-Action Models Requires Fewer Layers Than You Think Streamlining Redundant Layers to Compress Large Language Models

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:29:34.941369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 47d70c76-ea28-44b2-8c97-4039edb599ae · inbound

CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry cites this paper.

CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry Streamlining Redundant Layers to Compress Large Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-26T05:29:00.039148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.