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Paper Citation Record · LEDGER

Optimizing Dense Retrieval Model Training with Hard Negatives

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2104.08051.

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

pith.paper-citation-record.v1
2104.08051 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:36:25.531779Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:31:24.393169Z

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 aa6ac72d-3e49-4866-8d2d-4992a44a47f3 · inbound

WARP: An Efficient Engine for Multi-Vector Retrieval cites this paper.

WARP: An Efficient Engine for Multi-Vector Retrieval Optimizing Dense Retrieval Model Training with Hard Negatives

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-10T04:36:25.531779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:36:25.531779Z digest=sha256:519c98365e5a421fc72e75ad4211870d4c7b013cf38401fb6004f83554e41f4c

Observation 87f2c896-6a0d-4be9-abf6-124ec09767bc · inbound

MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization cites this paper.

MASS-DPO: Multi-negative Active Sample Selection for Direct Policy Optimization Optimizing Dense Retrieval Model Training with Hard Negatives

Reference 70

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:31:24.404290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T04:14:37.374346Z digest=sha256:a00c88bb3ae8450dc4e791a8ad8d050a850387d1df4c43b93048a849763fd00d