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Paper Citation Record · LEDGER

Federated Continual Instruction Tuning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.12897.

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

pith.paper-citation-record.v1
2503.12897 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:40:21.128497Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 318df3d9-ca66-4677-ac09-7cb29b1a6368 · inbound

Continual Learning for Generative AI: From LLMs to MLLMs and Beyond cites this paper.

Continual Learning for Generative AI: From LLMs to MLLMs and Beyond Federated Continual Instruction Tuning

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-07T00:40:21.128497Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:40:21.128497Z digest=sha256:4e1b49f50de6d7bb5ab6f8ad0500d10a94ff4e6492c849d10063250285b0919d

Observation 08841ea8-3390-4d00-a35c-9da01e9a6f84 · inbound

Model-Dowser: Data-Free Importance Probing to Mitigate Catastrophic Forgetting in Multimodal Large Language Models cites this paper.

Model-Dowser: Data-Free Importance Probing to Mitigate Catastrophic Forgetting in Multimodal Large Language Models Federated Continual Instruction Tuning

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-03T04:44:50.312881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:44:50.312881Z digest=sha256:c97442d4fe9cfd395f36219f110b0ef16d6cdf25c3eb882ea11194978aacd7be