Paper Citation Record · LEDGER

How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2302.07452.

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

pith.paper-citation-record.v1
2302.07452 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:57:29.297001Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T11:52:16.286484Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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malformed identifier0
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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 5a671a00-ac93-47b3-843f-962649d89b94 · inbound

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems cites this paper.

From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 94

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:52:16.288109Z

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.

Observation eaa8141f-3f16-4a6d-b282-7af9b9387353 · inbound

FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems cites this paper.

FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:57:29.297001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b118bd9c-cdca-487e-a8c9-94afbb991aca · inbound

FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation cites this paper.

FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T00:51:21.149980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94aea020-4ed2-4a53-9dfa-c5c2a9b8e749 · inbound

Graph World Model cites this paper.

Graph World Model How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T17:37:11.706811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac66a5de-4540-4f3e-9623-0eb1ce8d231a · inbound

A Multi-Task Evaluation of LLMs' Processing of Academic Text Input cites this paper.

A Multi-Task Evaluation of LLMs' Processing of Academic Text Input How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T19:49:50.581437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cc04607-c720-468a-8526-4a91c48ff187 · inbound

Beyond Hard Negatives: The Importance of Score Distribution in Knowledge Distillation for Dense Retrieval cites this paper.

Beyond Hard Negatives: The Importance of Score Distribution in Knowledge Distillation for Dense Retrieval How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:48.200050Z

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.

Observation 9ace5bf2-664e-4c73-9fd5-fe0a5421f137 · inbound

Mutual Linearity in and out of Stationarity for Markov Jump Processes: A Trajectory-Based Approach cites this paper.

Mutual Linearity in and out of Stationarity for Markov Jump Processes: A Trajectory-Based Approach How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T09:07:18.453118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb3920d4-89af-4f6c-b231-2a8ace615775 · inbound

Data, Not Model: Explaining Bias toward LLM Texts in Neural Retrievers cites this paper.

Data, Not Model: Explaining Bias toward LLM Texts in Neural Retrievers How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:41:04.787086Z

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.

Observation b2375ec0-0c39-488f-8b6a-2e965a4ce773 · inbound

AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views cites this paper.

AV-SQL: Decomposing Complex Text-to-SQL Queries with Agentic Views How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:30:58.027505Z

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.

Observation 6d8bd637-d9c5-4be0-a564-6b0bab995239 · inbound

KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrieval cites this paper.

KAMR: Grounding Generation via Knowledge-Aligned Multi-hop Retrieval How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-30T11:11:55.422143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.