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Paper Citation Record · LEDGER

GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2112.07577.

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

pith.paper-citation-record.v1
2112.07577 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:34:54.223392Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:29:51.687574Z

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 7bb1e88f-0f45-4896-869b-cedb8b393e95 · inbound

SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval cites this paper.

SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-11T05:44:00.115811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:44:00.115811Z digest=sha256:f75a3e1cd55f863e19dabf4f1fbe576dbd02916ef8638055254a4355f068cb1a

Observation 013b96bf-85d5-494d-85d1-7f316087263d · inbound

Reading with Intent -- Neutralizing Intent cites this paper.

Reading with Intent -- Neutralizing Intent GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-10T21:55:28.493847Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:55:28.493847Z digest=sha256:436ea2635d35a323ed0e1fa5a1e8d18318ed89fe4c78a721f6b1b0d37d4c6f90

Observation 2895f64c-8281-4521-b1fb-cad455c25334 · inbound

Interpretability Analysis of Domain Adapted Dense Retrievers cites this paper.

Interpretability Analysis of Domain Adapted Dense Retrievers GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-10T15:10:28.128645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T15:10:28.128645Z digest=sha256:b49a7675c5d0aec2bd2e07f4e4da97677ba791f43389d34e1b2e2c47b955feee

Observation b5bfeb76-4744-4c5c-9efa-5e1d29cb329c · inbound

Don't Retrieve, Generate: Prompting LLMs for Synthetic Training Data in Dense Retrieval cites this paper.

Don't Retrieve, Generate: Prompting LLMs for Synthetic Training Data in Dense Retrieval GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-22T19:35:03.933032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-22T19:35:02.029358Z digest=sha256:d28cd22f8e508e9b01ca5e3fbfea10c732aa46c87e5f87cb291c5319ccff997c

Observation 873342c7-56e6-4472-a317-54678523a41c · inbound

Towards A Generalist Code Embedding Model Based On Massive Data Synthesis cites this paper.

Towards A Generalist Code Embedding Model Based On Massive Data Synthesis GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T20:34:54.223392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:34:54.223392Z digest=sha256:45be4741ab5792c73aaa80c04bef0d420c4d9464faf70bed5329e4ea801473a7

Observation 9f429c72-e27b-42a8-b0a2-940ba24924bf · inbound

Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains cites this paper.

Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domains GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T13:47:13.039866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:47:13.039866Z digest=sha256:bb8590cdd601e67b332a0192f758b474b811bd60065e251b0e5f14710992b43d

Observation 2df3b569-de6c-4746-9b88-90c0c388d0fa · inbound

From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval cites this paper.

From Token to Action: State Machine Reasoning to Mitigate Overthinking in Information Retrieval GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-07T12:57:25.443501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:57:25.443501Z digest=sha256:ee2a426ea914bf4c0b3cd374431f774ce660db4c9d2d5cce71f67370719c53a4

Observation 80ada7e7-e338-4e80-8931-a54464ce4362 · inbound

When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR cites this paper.

When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T11:39:46.532542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:39:46.532542Z digest=sha256:800ca28ab9dfd97afc57f4bc03438365411c28defb49a049e775df665e276a1d

Observation 29f1badf-a46c-47f4-bb57-0d51efa64424 · inbound

UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval cites this paper.

UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 18

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T00:06:15.923052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-07T15:51:41.243646Z digest=sha256:7e21d10a443ca520f68134039c80ae69ee678dcb4f140991afa721253ee0d82e

Observation 3f9d5571-ad4e-4d05-94d2-f0d4aedbdffa · inbound

Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search cites this paper.

Designing Reward Signals for Portable Query Generation: A Case Study in Industrial Semantic Job Search GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-04T13:29:51.689191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-26T05:10:59.825246Z digest=sha256:8892b7422b76beddbc3ab06bc5a6dd7f52b17f067f851a77e6cc17a50ef4c858

Observation 1132ebc8-669b-438b-9602-395a41e38998 · inbound

RAGAL: A Frugal, Fully Local Retrieval-Augmented Assistant for Technical Support at a Government Agency cites this paper.

RAGAL: A Frugal, Fully Local Retrieval-Augmented Assistant for Technical Support at a Government Agency GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T14:30:35.141673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:30:35.141673Z digest=sha256:4d29c0f0e7aa976cae4e98bb7c44efa661bd355f7f54457464fe415487d61a49