Pith. sign in

Paper Citation Record · LEDGER

SeDR: Segment Representation Learning for Long Documents Dense Retrieval

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2211.10841.

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

pith.paper-citation-record.v1
2211.10841 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T21:20:41.726774Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:19:13.194842Z

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 f5e50d7f-639b-49e5-99a6-6a233d64276a · inbound

Retrieval-Augmented Generation for Natural Language Processing: A Survey cites this paper.

Retrieval-Augmented Generation for Natural Language Processing: A Survey SeDR: Segment Representation Learning for Long Documents Dense Retrieval

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-23T23:08:35.556587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-23T23:06:41.081461Z digest=sha256:282e23d7a58104a377aac6f759427216910b23902c0aaf14afa0eaf6ef249854

Observation 47f4fdb3-9d82-4bd1-853e-ce0a2befa994 · inbound

Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation cites this paper.

Lost in a Single Vector: Improving Long-Document Retrieval with Chunk Evidence Aggregation SeDR: Segment Representation Learning for Long Documents Dense Retrieval

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:19:13.196205Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-26T21:20:41.726774Z digest=sha256:2b7992ec17fb020f8a9f3b1a05a45609a79a37e34722040d0aa75eb0658d45d2