Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2410.11772.
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 2026-08-11T19:55:12.226365Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T13:21:35.500109Z
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 5aeead13-0190-46bb-a891-3d6579cea622 · inbound
S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity cites this paper.
S$^{2}$FT: Efficient, Scalable and Generalizable LLM Fine-tuning by Structured Sparsity Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models
Reference 85
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T19:55:12.226365Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ab9d2d25-bc84-4b60-bb51-90247a46ef82 · inbound
Train More Parameters But Mind Their Placement: Insights into Language Adaptation with PEFT cites this paper.
Train More Parameters But Mind Their Placement: Insights into Language Adaptation with PEFT Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T13:55:47.334531Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 85b50e00-c05b-44c1-8d84-f06e8da4d08d · inbound
RAP: Runtime Adaptive Pruning for LLM Inference cites this paper.
RAP: Runtime Adaptive Pruning for LLM Inference Layer-wise Importance Matters: Less Memory for Better Performance in Parameter-efficient Fine-tuning of Large Language Models
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T13:21:35.503548Z
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.