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Paper Citation Record · LEDGER

Dynamics of Instruction Fine-Tuning for Chinese Large Language Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.19651.

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

pith.paper-citation-record.v1
2310.19651 v3

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 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:13:48.362274Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T21:04:56.819846Z

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 c64e7757-b5ca-49ec-bc5a-46533b783150 · inbound

How Many Instructions Can LLMs Follow at Once? cites this paper.

How Many Instructions Can LLMs Follow at Once? Dynamics of Instruction Fine-Tuning for Chinese Large Language Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T17:13:48.362274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:13:48.362274Z digest=sha256:4a146e447c0736a787aa85bd7a85feb54c1fedf5597187a7e4ac4d5bea93c697

Observation a5f9c952-fb7e-47c4-887a-931c559cc5e9 · inbound

Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach cites this paper.

Enhancing Memory Recall in LLMs with Gauss-Tin: A Hybrid Instructional and Gaussian Replay Approach Dynamics of Instruction Fine-Tuning for Chinese Large Language Models

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-05T21:04:56.863336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-05T21:04:56.138246Z digest=sha256:c950edf303e55a622dd4c2f2315184179748d3d6ce1d046983fceefba533aaa7