Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Prune Once for All: Sparse Pre-Trained Language Models
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2111.05754.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T23:34:05.422247Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T06:34:01.085306Z
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 ab782af0-5a3f-4b23-85c7-4fa18ff0f453 · inbound
Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs cites this paper.
Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs Prune Once for All: Sparse Pre-Trained Language Models
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T06:34:01.088473Z
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 6780cff8-c7b8-4e90-9fa0-c0bf9dbcaf9b · inbound
Pruning Large Language Models by Identifying and Preserving Functional Networks cites this paper.
Pruning Large Language Models by Identifying and Preserving Functional Networks Prune Once for All: Sparse Pre-Trained Language Models
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T23:34:05.422247Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 30829f63-1c4d-4676-b8d4-9bb57c7ed39b · inbound
EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models cites this paper.
EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Prune Once for All: Sparse Pre-Trained Language Models
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:08:18.724499Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 221e9d06-6313-4778-8efe-403e7fe44d6a · inbound
Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity cites this paper.
Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity Prune Once for All: Sparse Pre-Trained Language Models
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T22:21:19.024131Z
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 ecff095c-910a-4899-bf92-cdef98be5975 · inbound
Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity cites this paper.
Resting Neurons, Active Insights: Robustifying Activation Sparsity in LLMs via Spontaneity Prune Once for All: Sparse Pre-Trained Language Models
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T11:54:51.100662Z
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 a13485af-abc4-4ae8-bbc4-0665e8db7976 · inbound
ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook cites this paper.
ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook Prune Once for All: Sparse Pre-Trained Language Models
Reference 109
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T20:28:14.181179Z
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 dd2e4c22-7a51-4002-8c12-0e8f61ebefb2 · inbound
The Sparsity Tax: Weight Sparsity Trade-offs in Event-Driven SIMD and SIMT Neuromorphic Cores cites this paper.
The Sparsity Tax: Weight Sparsity Trade-offs in Event-Driven SIMD and SIMT Neuromorphic Cores Prune Once for All: Sparse Pre-Trained Language Models
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T05:19:34.039471Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.