Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2501.15296.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:42:22.520527Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 052e0a74-8ea6-4abe-9850-bfc6f7250a8c · inbound
EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models cites this paper.
EfficientVLA: Training-Free Acceleration and Compression for Vision-Language-Action Models You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:42:22.520527Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e2fe82bc-5295-4c01-9e41-ac0d4e3c25be · inbound
SecRL-Prune: Structured Reinforcement Learning-Based Pruning of CodeLLMs for Preserving Adversarial Code Mutation cites this paper.
SecRL-Prune: Structured Reinforcement Learning-Based Pruning of CodeLLMs for Preserving Adversarial Code Mutation You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T01:31:29.791122Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 05067279-0961-484b-b232-cfb3d156823d · inbound
SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models cites this paper.
SlimVLM: Sensitivity-aware Dynamic Structured Pruning with Adaptive Visual Token Selection for Efficient Vision-Language Models You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:41:28.839158Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.