Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.12930.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:15:17.220735Z
A source-named dated measurement, never combined with another source.
Source: pith, 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
pith, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 956306c0-2d7a-408c-bfcb-0b98e6374352 · inbound
Reassessing Layer Pruning in LLMs: New Insights and Methods cites this paper.
Reassessing Layer Pruning in LLMs: New Insights and Methods Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:13:28.064720Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d4581649-9da4-4127-b0ed-a2068e2f1d0d · inbound
Transitive Array: An Efficient GEMM Accelerator with Result Reuse cites this paper.
Transitive Array: An Efficient GEMM Accelerator with Result Reuse Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:15:17.220735Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9f2d7edb-3921-4959-865f-af6472d1851e · inbound
FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs cites this paper.
FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:52:21.195688Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dced4112-1fb6-4d92-9316-a9ba97f0d80e · inbound
LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization cites this paper.
LightNobel: Improving Sequence Length Limitation in Protein Structure Prediction Model via Adaptive Activation Quantization Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T22:59:42.400100Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3cb3c291-17d4-43ba-978e-724c2a685a19 · inbound
SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision cites this paper.
SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-08-03T16:38:30.906423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.