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Paper Citation Record · LEDGER

Continual Neural Topic Model

As of 20 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 2026-08-20T06:33:59.587034+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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T17:51:45.523623Z digest=sha256:1ce4bed8afd86a56809313d6f183905ac4a5c90e5fdee621211f5f29097e5397

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T17:51:45.598790Z digest=sha256:a13c968b167564b71462c12e864507410dee24d981ad32364a6adf06c3c4eef8

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T17:51:45.676999Z digest=sha256:c5e88d0afeb71986a849a23162cd50c80dc9082687f1a1f68def2fa5c8ede6ce

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=sha256:7e0518c83577db9dbfa822b8ce42bf89bfec82f0014d3fe64fe495ea1684cdbc

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:aed8aca80b1c7bdb7fbbe3e3bfb7b95ee8b8f727c03bd457c1609b1d6005883a

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T17:51:45.229914Z digest=sha256:b40d5f2485d3c2557c862c736cef96d2d1cb8552c7739968b83380f6e5c2fa05

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=sha256:8f6c54e9094f5d0512dd0d9e5c38add3a67ff33aa054ebbe18ccd87ceccc53fc

Pith citing papers

No inbound Pith citation observations are available.