Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2410.09615.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T17:22:08.957747Z
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 dcee6e71-dd51-4cc2-bfbd-e1c7fc08b8a8 · inbound
PHLoRA: data-free Post-hoc Low-Rank Adapter extraction from full-rank checkpoint cites this paper.
PHLoRA: data-free Post-hoc Low-Rank Adapter extraction from full-rank checkpoint SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T17:22:08.957747Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b99dbbf8-fcf6-4637-a5c2-bc813505de04 · inbound
Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression cites this paper.
Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T08:41:08.099099Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 3425db89-3c3e-4de9-a759-b2d70be90f30 · inbound
Scaling Video Understanding via Compact Latent Multi-Agent Collaboration cites this paper.
Scaling Video Understanding via Compact Latent Multi-Agent Collaboration SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T15:21:09.422077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation a8ea7d0e-14f6-4f51-ad70-807ae529d5cf · inbound
RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models cites this paper.
RT-Lynx: Putting the GEMM Sparsity In a Right Way for Diffusion Models SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T19:43:54.691777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0bf62d11-22e9-4b53-9004-2f239fe0d07d · inbound
Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks cites this paper.
Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight Compression
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T06:57:39.854675Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.