Paper Citation Record · LEDGER

P$^2$ Law: Scaling Law for Post-Training After Model Pruning

As of 14 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2411.10272.

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

pith.paper-citation-record.v1
2411.10272 v3

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:55:21.801020Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:42:45.647996Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T20:42:45.938173Z

Reference resolution

29 of 29 outbound references displayed

verified exact1
verified fuzzy0
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe6e4158-7220-4b95-bec0-d8ca6d3396d7 · outbound

This paper cites SliceGPT: Compress Large Language Models by Deleting Rows and Columns.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.704588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d7dfb4a-4788-4a95-bde8-f261c229c302 · outbound

This paper cites Streamlining Redundant Layers to Compress Large Language Models.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Streamlining Redundant Layers to Compress Large Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.709147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 288d7679-5aca-4452-9c7d-2adc9000eb33 · outbound

This paper cites The Llama 3 Herd of Models.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning The Llama 3 Herd of Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.712891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c35b220e-4f5a-4968-81eb-76e6ed2765c9 · outbound

This paper cites an unresolved cited work.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:55:22.056895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1cfc7ab7-37eb-414c-8197-1717ad60b6c1 · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.721063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21d8c847-e2f3-468e-b04d-66bac45efef3 · outbound

This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning The Unreasonable Ineffectiveness of the Deeper Layers

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.724905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 265132ad-fa2d-4040-bcdf-0734e1963a28 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.728891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 669e7edb-2385-4e0a-9f2a-360009bc108d · outbound

This paper cites Training Compute-Optimal Large Language Models.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Training Compute-Optimal Large Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.732381Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd6d65bd-6adc-481a-9883-618564783632 · outbound

This paper cites LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning LoRS: Efficient Low-Rank Adaptation for Sparse Large Language Model

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.735878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d301e5fd-9f22-4ed5-af99-faa6a5dfd6f8 · outbound

This paper cites $\rm SP^3$: Enhancing Structured Pruning via PCA Projection.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning $\rm SP^3$: Enhancing Structured Pruning via PCA Projection

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-12T19:55:21.929350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f782f795-fee3-4ab9-adf5-f7c693fce51e · outbound

This paper cites an unresolved cited work.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:55:22.048716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 76405a8d-35af-4888-a62b-b724ff58f68e · outbound

This paper cites Scaling Laws for Neural Language Models.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Scaling Laws for Neural Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.746161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98af25a4-2a6b-4c35-a9c3-7db8424e1a6d · outbound

This paper cites Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.749325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d92075b0-8bd3-4e45-8716-c4ad4a30e16d · outbound

This paper cites PAT: Pruning-Aware Tuning for Large Language Models.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning PAT: Pruning-Aware Tuning for Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.752474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e7135d4-89d0-4337-971e-9e353cb2d545 · outbound

This paper cites an unresolved cited work.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.755921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16ae742d-9b32-4049-b51d-69faf31642b8 · outbound

This paper cites An Empirical Model of Large-Batch Training.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning An Empirical Model of Large-Batch Training

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.759218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64545ba0-b086-4a44-9214-532ff068e3cf · outbound

This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning ShortGPT: Layers in Large Language Models are More Redundant Than You Expect

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.762572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc7a8b43-d414-4764-b1c2-07b32af279f8 · outbound

This paper cites Accelerating Sparse Deep Neural Networks.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Accelerating Sparse Deep Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.765373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2428dfc1-e666-4568-8591-09912ee44044 · outbound

This paper cites an unresolved cited work.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:55:22.033300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 18b3be9d-8281-4356-be93-cea0fceaad63 · outbound

This paper cites Compact Language Models via Pruning and Knowledge Distillation.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Compact Language Models via Pruning and Knowledge Distillation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.771414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bcfea201-7347-4bd4-b569-cfb123b0e990 · outbound

This paper cites D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.774415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a5f0e05-884a-44ae-bb32-edc33bd8389d · outbound

This paper cites SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.777538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b374811-a87c-45f2-a7d1-bc37a3db206e · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning A Simple and Effective Pruning Approach for Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.780724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea47f9fa-74db-4215-bc81-01be7cf64c8f · outbound

This paper cites an unresolved cited work.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.784138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2ff0dbd-a92c-40a1-bf33-6510357fb0fe · outbound

This paper cites an unresolved cited work.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-12T19:55:22.018233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c4d03ac0-82eb-4159-967e-67d035271932 · outbound

This paper cites LaCo: Large Language Model Pruning via Layer Collapse.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning LaCo: Large Language Model Pruning via Layer Collapse

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.790374Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0755663f-9b52-4ff0-ae7f-d5a19c0ae61a · outbound

This paper cites an unresolved cited work.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.794149Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9a0e4f6-3e55-4f1f-bf5c-b835de005dd7 · outbound

This paper cites online" 'onlinestring :=.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning online" 'onlinestring :=

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.797396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adaf57f9-962c-4e83-85d5-235e61ff845c · outbound

This paper cites write newline.

P$^2$ Law: Scaling Law for Post-Training After Model Pruning write newline

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T19:55:21.801020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation d3accc11-56c5-4627-87a9-fea314d029bd · inbound

Ubiquitous Intelligence Via Wireless Network-Driven LLMs Evolution cites this paper.

Ubiquitous Intelligence Via Wireless Network-Driven LLMs Evolution P$^2$ Law: Scaling Law for Post-Training After Model Pruning

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-04T20:42:45.941885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.