Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2411.06463.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:46:07.675172Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T19:46:11.080379Z
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 1b89d182-d351-4226-ad24-0f1d8f5b27fc · inbound
SepPrune: Structured Pruning for Efficient Deep Speech Separation cites this paper.
SepPrune: Structured Pruning for Efficient Deep Speech Separation RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:46:07.675172Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f9412ea4-dfad-4026-94e1-3c5b6d4b6dbb · inbound
SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning cites this paper.
SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learning RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:59:45.367940Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6c017dfb-3f69-4f08-90ca-fae28ecfb380 · inbound
Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing cites this paper.
Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:49:36.776845Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5b453c19-8848-4468-a355-832a513701bf · inbound
Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation cites this paper.
Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T10:34:07.240185Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation e75a7533-c5b0-4c19-bfc1-464064ce8080 · inbound
Before Parc Ferm\'e: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving cites this paper.
Before Parc Ferm\'e: RL-Time Pruning for Efficient Embodied LLMs in Autonomous Driving RL-Pruner: Structured Pruning Using Reinforcement Learning for CNN Compression and Acceleration
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T19:46:11.081688Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.