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Paper Citation Record · LEDGER

Continual Neural Topic Model

As of 10 August 2026, this Paper Citation Record lists 7 of 7 outbound references and 0 inbound Pith citation observations for arXiv:2508.15612.

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

pith.paper-citation-record.v1
2508.15612 v1

Coverage vector

measured 7 of 7 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:51:45.676999Z

measured 7 of 7 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

7 of 7 outbound references displayed

  • verified exact0
  • verified fuzzy2
  • unresolved3
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e2e2eef2-a56e-411d-ad40-a59cfbb4714a · outbound

This paper cites In International conference on machine learn- ing, pages 3987–3995.

Continual Neural Topic Model In International conference on machine learn- ing, pages 3987–3995

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T17:51:46.263230Z

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=2026-08-05T17:51:45.523623Z digest=sha256:80eca5d93f14fdfe0401ec6dfa9601319e8326af2afacf148ae298fae3a3fc22

Observation e037f424-d20f-4a35-9754-26a483245775 · outbound

This paper cites republican.

Continual Neural Topic Model republican

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T17:51:46.112524Z

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=2026-08-05T17:51:45.598790Z digest=sha256:ff956a189168430fc5997bf4bdcf676df6b796acbc47334ebbdf5c8edecfc26d

Observation 38675730-279d-4cd7-8de8-d6014a3f6ac0 · outbound

This paper cites politics,.

Continual Neural Topic Model politics,

Reference 16

Resolution
malformed identifier
raw_fallback, observed 2026-08-05T17:51:45.969940Z

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=2026-08-05T17:51:45.676999Z digest=sha256:55e8a79128d8abfb03fa2fc358efaf3fb64e0ac137f5081f3de38ada35dafe96

Observation e186ea0e-ec67-4ed8-b224-0db77fee0850 · outbound

This paper cites BERTrend: Neural Topic Modeling for Emerging Trends Detection.

Continual Neural Topic Model BERTrend: Neural Topic Modeling for Emerging Trends Detection

Reference 2003

Resolution
unresolved
no resolver link, observed 2026-08-05T17:51:45.272864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:51:45.272864Z digest=sha256:ebb5103338a3fcd2732a478918056992310a9cc0933c9618aa867fb9bbfd88b6

Observation d175ae3c-7e53-4305-8972-5979ce180880 · outbound

This paper cites Enriching and Controlling Global Semantics for Text Summarization.

Continual Neural Topic Model Enriching and Controlling Global Semantics for Text Summarization

Reference 2014

Resolution
unresolved
no resolver link, observed 2026-08-05T17:51:45.352156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:51:45.352156Z digest=sha256:b35c1c94ab9f21153bc649fc8b96b16a1e5733f27bb934fbb1c9dcfbc570f905

Observation c8fd08f7-8b55-4a49-b179-62e2c4f8c7a7 · outbound

This paper cites Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence.

Continual Neural Topic Model Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence

Reference 2017

Resolution
metadata mismatch
local_arxiv, observed 2026-08-05T17:51:45.826214Z

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=2026-08-05T17:51:45.229914Z digest=sha256:883cab9fa5cb7679aa9f36b6b2848e1c27f647568eb4884f64878ad34230f035

Observation b18155c0-9de4-4cf0-b7e8-d3ca1628ac6e · outbound

This paper cites Continuous Time Dynamic Topic Models.

Continual Neural Topic Model Continuous Time Dynamic Topic Models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-05T17:51:45.450631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T17:51:45.450631Z digest=sha256:4cdefca1eb29eb675d05b76c6a0b8ade13db243d6af9f34a641242ab8667d830

Pith citing papers

No inbound Pith citation observations are available.