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Paper Citation Record · LEDGER

Deep Model Reassembly

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.17409.

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

pith.paper-citation-record.v1
2210.17409 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:07:11.998231Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T13:37:21.576745Z

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 5dbf071c-3e88-4dcc-9fa6-e6ef100916d9 · inbound

Linear Representation Transferability Hypothesis: Leveraging Small Models to Steer Large Models cites this paper.

Linear Representation Transferability Hypothesis: Leveraging Small Models to Steer Large Models Deep Model Reassembly

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T12:07:11.998231Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:07:11.998231Z digest=sha256:a1358f9aefc661b2580a806284a55664df7d7e12a6693226e53e136361e8fb4a

Observation 0534b599-4abe-4c6b-9166-588ec1eb3d16 · inbound

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement cites this paper.

Learning Yourself: Class-Incremental Semantic Segmentation with Language-Inspired Bootstrapped Disentanglement Deep Model Reassembly

Reference 50

Resolution
verified exact
local_arxiv, observed 2026-08-05T13:37:21.652739Z

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.

source=pdf_text observed=2026-08-05T13:37:18.876187Z digest=sha256:b977581a12b785d7671f93ca0eae92c83610654ced29b79ad8f92ad807151e52