Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
EfficientLLM: Efficiency in Large Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2505.13840.
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 2026-08-08T12:00:53.896063Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T23:40:51.690711Z
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 de19ad69-08bb-40da-a065-06d671a7492a · inbound
SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation cites this paper.
SkipOPU: An FPGA-based Overlay Processor for Large Language Models with Dynamically Allocated Computation EfficientLLM: Efficiency in Large Language Models
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T18:16:48.384737Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cdd5c10d-32a5-4e3a-8d9f-14c7eb85c04d · inbound
MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU cites this paper.
MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU EfficientLLM: Efficiency in Large Language Models
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T23:40:51.693768Z
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 99a9a95c-ad7b-4a1b-ae3d-a7f8c2c974eb · inbound
APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning cites this paper.
APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning EfficientLLM: Efficiency in Large Language Models
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T12:00:53.896063Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.