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Paper Citation Record · LEDGER

Continual Learning with Fully Probabilistic Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2104.09240.

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

pith.paper-citation-record.v1
2104.09240 v1

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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:50:19.305795Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T21:04:56.939377Z

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 3e136ba7-155d-4b4c-b15d-de35bf96f5da · inbound

CCD: Continual Consistency Diffusion for Lifelong Generative Modeling cites this paper.

CCD: Continual Consistency Diffusion for Lifelong Generative Modeling Continual Learning with Fully Probabilistic Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-15T20:50:19.305795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:50:19.305795Z digest=sha256:c14cbdeaa9874601dc0a6e26b1a05292956c98eb6608d4831a55273aca0b8ca2

Observation 80ee8a7e-5e47-48d9-b4e7-f02e04d7e691 · 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 Continual Learning with Fully Probabilistic Models

Reference 2021

Resolution
verified exact
local_arxiv, observed 2026-08-05T21:04:57.005922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-05T21:04:55.882412Z digest=sha256:9bf47b88cc1bb750e797005a0557cb0799adabb4713a6f4454e4ae4d09758b21