Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2305.17328.
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 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-13T02:43:47.779443Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T07:40:43.953117Z
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 af0d5530-787c-4b52-8c13-c5976192d664 · inbound
CORP: Closed-Form One-shot Representation-Preserving Structured Pruning for Transformers cites this paper.
CORP: Closed-Form One-shot Representation-Preserving Structured Pruning for Transformers Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T07:40:43.954788Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 898e07d2-6449-49b1-89e5-5e0e6162a163 · inbound
Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers cites this paper.
Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T02:43:47.779443Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.