Paper Citation Record · LEDGER

Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2102.10073.

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

pith.paper-citation-record.v1
2102.10073 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:55:54.793253Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T20:30:07.120934Z

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 5697319a-823d-4f26-94b9-646ee79a43aa · inbound

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level cites this paper.

Investigating the Robustness of Retrieval-Augmented Generation at the Query Level Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:55:54.793253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6bfbda41-1f6c-4a1d-967b-8013df31af79 · inbound

Rethinking On-policy Optimization for Query Augmentation cites this paper.

Rethinking On-policy Optimization for Query Augmentation Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T09:08:02.581992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc601db4-3bf8-4af8-8c6c-69ef589ecc32 · inbound

BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination cites this paper.

BracketRank: Large Language Model Document Ranking via Reasoning-based Competitive Elimination Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:55:57.779981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 82f54ed9-b9a2-49f7-a37c-958195afa183 · inbound

BiCon-Gate: Consistency-Gated De-colloquialisation for Dialogue Fact-Checking cites this paper.

BiCon-Gate: Consistency-Gated De-colloquialisation for Dialogue Fact-Checking Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:20:25.416237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72e5217a-9950-44bb-8a83-61789a5e32db · inbound

Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction cites this paper.

Mask-to-Correct$^+$: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-21T01:03:52.821218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e2747c22-9db0-40ce-bce6-a5f6c54f6cfb · inbound

SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics cites this paper.

SPECTRA: Synthetic IR Test Collections with Relevance Oracles and Controlled Distractor Diagnostics Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:42:37.213651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 08e51002-92cd-4d61-ab34-b34192e224d9 · inbound

Evaluating LLMs on Real-World Software Performance Optimization cites this paper.

Evaluating LLMs on Real-World Software Performance Optimization Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:30:07.123394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3f901359-d5ab-4663-8054-79b7926c9c10 · inbound

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms cites this paper.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-01T14:35:28.950304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e3c249d-a813-4bdd-b487-570d15b7e6af · inbound

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms cites this paper.

BM25 Wins at Scale: A Scaling Study of Retrieval-Augmented Generation Paradigms Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T01:45:02.618087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.