Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.07461.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-23T17:10:06.053994Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T17:13:14.029827Z
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 b7b3971b-1360-49f3-8fd6-00c87a768ecf · inbound
Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning cites this paper.
Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
Reference 3
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T17:13:14.033346Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d183cf41-3dc1-42e7-8892-6e3b2dc352a3 · inbound
Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction cites this paper.
Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T15:51:33.780470Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 59428d1a-2e8a-4a17-8538-2a4e173ef752 · inbound
Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization cites this paper.
Frequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning
Reference 2
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T10:39:56.790507Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.