Paper Citation Record · LEDGER

RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2010.08191.

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

pith.paper-citation-record.v1
2010.08191 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:46:10.638784Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:22:08.867858Z

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 27160ac0-cd2d-454c-972f-4762a34b0be5 · inbound

C-Pack: Packed Resources For General Chinese Embeddings cites this paper.

C-Pack: Packed Resources For General Chinese Embeddings RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-13T13:24:32.133594Z

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.

Observation 4ec0796c-faef-48e7-974d-1c9c4d3e7919 · inbound

M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation cites this paper.

M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:39:03.404121Z

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.

Observation ddf616ff-3751-42c1-a362-104d6a18da3a · inbound

A Survey of Scaling in Large Language Model Reasoning cites this paper.

A Survey of Scaling in Large Language Model Reasoning RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 160

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:22:08.871095Z

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.

Observation 21cd1b9a-aeff-4632-9096-c0b0afcdeeaa · inbound

Locality-Sensitive Hashing for Efficient Hard Negative Sampling in Contrastive Learning cites this paper.

Locality-Sensitive Hashing for Efficient Hard Negative Sampling in Contrastive Learning RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T14:46:10.638784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7199e30f-c493-47bb-8f40-7166d9e6451a · inbound

Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data cites this paper.

Conventional Contrastive Learning Often Falls Short: Improving Dense Retrieval with Cross-Encoder Listwise Distillation and Synthetic Data RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:23:41.204459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8a59e9d-dd27-40da-a024-43613a4a8063 · inbound

Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion cites this paper.

Exp4Fuse: A Rank Fusion Framework for Enhanced Sparse Retrieval using Large Language Model-based Query Expansion RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T10:38:18.573116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 817a2b6f-e2a4-4cc1-98e8-e96aac953449 · inbound

OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion cites this paper.

OneSug: The Unified End-to-End Generative Framework for E-commerce Query Suggestion RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:52:06.441848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff23b9f7-be72-4646-8054-f3142a55b78d · inbound

A Comparative Study of Specialized LLMs as Dense Retrievers cites this paper.

A Comparative Study of Specialized LLMs as Dense Retrievers RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T20:02:17.536736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce21189e-6112-447e-bf62-4b9a8eab0128 · inbound

Boosting Data Utilization for Multilingual Dense Retrieval cites this paper.

Boosting Data Utilization for Multilingual Dense Retrieval RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T19:07:21.215396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36f56d3d-6bfc-4581-9f6b-68320d095779 · inbound

The Role of Vocabularies in Learning Sparse Representations for Ranking cites this paper.

The Role of Vocabularies in Learning Sparse Representations for Ranking RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-18T15:56:34.267601Z

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.

Observation dcd6d093-966b-4272-82f3-c8707ab83f17 · inbound

Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization cites this paper.

Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-18T09:42:30.767085Z

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.

Observation 9e8592c8-3ae9-441d-b97b-254619a84299 · inbound

Next-Gen Sponsored Search: Crafting the Perfect Query with Inventory-Aware RAG (InvAwr-RAG) Based GenAI cites this paper.

Next-Gen Sponsored Search: Crafting the Perfect Query with Inventory-Aware RAG (InvAwr-RAG) Based GenAI RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-11T23:17:23.141427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24a855ad-ccea-4f80-8b18-b5aa263a8d2a · inbound

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models cites this paper.

GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-02T12:15:44.927062Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.