Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:44:32.136821Z
Paper Citation Record · LEDGER
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models
As of 18 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2505.12216.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
13 of 13 outbound references displayed
- verified exact0
- verified fuzzy3
- unresolved10
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 38e2a364-1366-44e6-9eb5-38f3223ad163 · outbound
This paper cites Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:31.996022Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9ce29c8e-504f-47db-af1b-a2b46962e63b · outbound
This paper cites ShortGPT: Layers in Large Language Models are More Redundant Than You Expect.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models ShortGPT: Layers in Large Language Models are More Redundant Than You Expect
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:32.000215Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7a2efdac-6b4f-4fd4-a41a-6e89c241eca1 · outbound
This paper cites The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:32.106556Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4b9f4fd5-1703-47c2-8ff1-acd17001c7a7 · outbound
This paper cites EvoPress: Accurate Dynamic Model Compression via Evolutionary Search.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:32.115409Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d6712259-3d05-4354-8759-50afda2488a3 · outbound
This paper cites QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:32.123193Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ac7c410-f328-4c0c-b2d5-37322410a269 · outbound
This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:32.127470Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5556c198-136c-4e34-ac83-1475d5b3fda7 · outbound
This paper cites Transactions of the Associa- tion for Computational Linguistics, 12:1556–1577.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models Transactions of the Associa- tion for Computational Linguistics, 12:1556–1577
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:44:32.280711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b9fb8bea-e17b-4f0e-a736-526f62c90390 · outbound
This paper cites SparseGPT generates spar- sified blocks with varying sparsity levels across layers.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models SparseGPT generates spar- sified blocks with varying sparsity levels across layers
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:44:32.268129Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e7ef7af7-77ce-4ae9-8a5c-52ce8f7785a0 · outbound
This paper cites In 15th In- ternational Conference on Scientific and Statistical Database Management, 2003., pages 141–150.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models In 15th In- ternational Conference on Scientific and Statistical Database Management, 2003., pages 141–150
Reference 2003
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:44:32.292280Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3a0d2829-fc77-4fce-a0fd-2f7d1ddc7fc8 · outbound
This paper cites Pointer Sentinel Mixture Models.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models Pointer Sentinel Mixture Models
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:32.101934Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4bdc28a0-2ff6-4cb9-b7f0-fcd5f2a05e0c · outbound
This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:31.986746Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 33b7391d-9ae5-4c39-82ad-7f837963e7c7 · outbound
This paper cites Weight subcloning: direct initialization of transformers using larger pretrained ones.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models Weight subcloning: direct initialization of transformers using larger pretrained ones
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:32.111122Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 45bf08ae-5f02-4f3c-a600-d612d4339a6c · outbound
This paper cites The Unreasonable Ineffectiveness of the Deeper Layers.
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models The Unreasonable Ineffectiveness of the Deeper Layers
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:44:31.991534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.