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Paper Citation Record · LEDGER

ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.04866.

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

pith.paper-citation-record.v1
2406.04866 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:16:44.254431Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:13:53.139323Z

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 3442d303-f673-4f49-b712-ddc66ec53061 · inbound

DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification cites this paper.

DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-12T05:16:44.254431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:16:44.254431Z digest=sha256:5a3e7915a53f3e3cd921d2a6bc6e9138d61361477041938f712b795f65c5186a

Observation f5bcba87-90f8-4b02-be31-29f406ccd984 · inbound

MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering cites this paper.

MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T11:24:01.176948Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:01.176948Z digest=sha256:a3e4ccf76695a629bd50668f6d70fd7b21ffe591a512f3284739c2b5b83ca3da

Observation b50263db-db88-4bec-8f86-497746b41cc6 · inbound

ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval cites this paper.

ASRank: Zero-Shot Re-Ranking with Answer Scent for Document Retrieval ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-10T14:32:37.893078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T14:32:37.893078Z digest=sha256:d60a6d2da7caccd3725fae6b364c711d8ac20b6ebf5beeabfff84bde385ee85e

Observation 62cde85a-f37c-4ac6-b554-5a848a051b02 · inbound

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models cites this paper.

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T17:15:15.275605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T17:15:15.275605Z digest=sha256:0eecf0032e1f254a2af5f7acb08c28fb95fa5aaf2531e138bd93907ec3358775

Observation ae754ac9-d66f-4605-a598-b26ef22075eb · inbound

A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts cites this paper.

A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Texts ComplexTempQA:A 100m Dataset for Complex Temporal Question Answering

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-06-29T19:13:53.141422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-06-29T04:52:30.770882Z digest=sha256:eaa48b27c46e6e6368b23eaab37692f9b5bb924846472ec0e9c217dc93d5a3fd