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Paper Citation Record · LEDGER

Computational Bottlenecks of Training Small-scale Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2410.19456.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2410.19456 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:30:25.510864Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T05:36:22.595820Z

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 0581aa2a-35ce-42b6-9c97-38346cf502e4 · inbound

Towards a Small Language Model Lifecycle Framework cites this paper.

Towards a Small Language Model Lifecycle Framework Computational Bottlenecks of Training Small-scale Large Language Models

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T05:30:25.510864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:30:25.510864Z digest=sha256:571dec9a1174e3cd3a194a352e1c4733d38eb8976b689fb2e89a9e6debf65c43

Observation d220843a-790f-4b94-93b2-9ab424255fec · inbound

MagicVL-2B: Empowering Vision-Language Models on Mobile Devices with Lightweight Visual Encoders via Curriculum Learning cites this paper.

MagicVL-2B: Empowering Vision-Language Models on Mobile Devices with Lightweight Visual Encoders via Curriculum Learning Computational Bottlenecks of Training Small-scale Large Language Models

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T05:36:22.608519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-08-06T05:36:15.746964Z digest=sha256:96f84d4c3d13dfc8f742d5b7999c7fb5663e71afcdfe890b18e634d4f76b8a71