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Paper Citation Record · LEDGER

A Memory Efficient Baseline for Open Domain Question Answering

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2012.15156.

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

pith.paper-citation-record.v1
2012.15156 v1

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 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:24:01.189445Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:16:24.628753Z

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 1863d175-9ab3-4eba-be6e-2487bf289a53 · inbound

Improving language models by retrieving from trillions of tokens cites this paper.

Improving language models by retrieving from trillions of tokens A Memory Efficient Baseline for Open Domain Question Answering

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-17T12:55:41.625899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-17T12:55:41.551478Z digest=sha256:f3377f298484b900d9c40c5513168ebdd4b9895b67cf17caef4e93fc308a8b83

Observation b02ca54e-c350-4a61-8e30-77afa9b82e6a · inbound

Unsupervised Dense Information Retrieval with Contrastive Learning cites this paper.

Unsupervised Dense Information Retrieval with Contrastive Learning A Memory Efficient Baseline for Open Domain Question Answering

Reference 141

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T13:21:17.175998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-12T13:21:16.921001Z digest=sha256:564950e9d9cfe090be2dddcbeb4e75ac599f22ead319ea71f031a2f2a3f88441

Observation e850a3b5-6d5c-4e74-9899-fc67a981bd60 · inbound

Atlas: Few-shot Learning with Retrieval Augmented Language Models cites this paper.

Atlas: Few-shot Learning with Retrieval Augmented Language Models A Memory Efficient Baseline for Open Domain Question Answering

Reference 203

Resolution
metadata mismatch
arxiv_id, observed 2026-05-16T13:48:43.409569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-05-16T13:48:43.024120Z digest=sha256:732b6910c68ddd50bdf055069433f734613ed9a4a95c3635390803ef83399635

Observation 11b3008a-3b96-4922-a969-3e7b8d7986fb · inbound

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

MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering A Memory Efficient Baseline for Open Domain Question Answering

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T11:24:01.189445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:24:01.189445Z digest=sha256:bb4d35133209d2e92adae84584f83444276ca7ace0f728354c9cade8b0befcd2

Observation bca38192-2208-4fdf-9efc-b16060ec8637 · inbound

Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors cites this paper.

Do Neural Retrievers Prefer Certain Documents? Evidence of Learned Relevance Priors A Memory Efficient Baseline for Open Domain Question Answering

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-02T01:16:24.630143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-28T12:20:52.807293Z digest=sha256:a46357140a4cdb61598c32c198a34d52df3f630c74d6ce9c012e4ebfcfd6c692