Paper Citation Record · LEDGER

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation

As of 18 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2603.17205.

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

pith.paper-citation-record.v1
2603.17205 v3

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T02:34:22.725072Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy0
unresolved40
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6a076bfc-d7e4-4297-9cae-ce3c43c2bed7 · outbound

This paper cites SemDeDup: Data-efficient learning at web-scale through semantic deduplication.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation SemDeDup: Data-efficient learning at web-scale through semantic deduplication

Reference 1

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Observation ac99a121-77f9-4c14-aa72-b2e2383b5500 · outbound

This paper cites Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models

Reference 9

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Observation 04e17b24-ccdc-4fd3-9791-0c55bc33723f · outbound

This paper cites Handschuh, A.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Handschuh, A

Reference 10

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Observation 02f8e2ee-dc95-42cb-a6ab-97b1549804ad · outbound

This paper cites When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale

Reference 11

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Observation 2bcb5d45-2286-4eef-86d5-149ef3ea0949 · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation MTEB: Massive Text Embedding Benchmark

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:19.819441Z

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Observation adc519f0-9d47-48c7-853e-6cc0c0d91ebf · outbound

This paper cites Competence-based Curriculum Learning for Neural Machine Translation.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Competence-based Curriculum Learning for Neural Machine Translation

Reference 14

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Observation a63d6035-3a35-4cad-bbf6-96344981b9e0 · outbound

This paper cites BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:20.512620Z

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Observation 5475180c-f206-48e5-b92e-d07f1223681d · outbound

This paper cites FEVER: a large-scale dataset for fact extraction and VERification.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation FEVER: a large-scale dataset for fact extraction and VERification

Reference 20

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Observation a3e67083-d0d0-4e26-8bd9-4d9219b56c92 · outbound

This paper cites doi: 10.18653/v1/N18-1074.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation doi: 10.18653/v1/N18-1074

Reference 21

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Observation 00e2ab29-a0f6-4636-8fe5-307b1fbe0e52 · outbound

This paper cites An Empirical Study of Example Forgetting during Deep Neural Network Learning.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation An Empirical Study of Example Forgetting during Deep Neural Network Learning

Reference 22

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Observation c70d8bca-78cf-4d06-9986-f893252e7fc6 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 23

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Observation 0ab545f9-84c0-4b4a-bc5f-87c2cd5236b2 · outbound

This paper cites Text Embeddings by Weakly-Supervised Contrastive Pre-training.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Text Embeddings by Weakly-Supervised Contrastive Pre-training

Reference 24

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Observation fb7c36ff-1ef1-441c-bf0e-cf793e736ab6 · outbound

This paper cites Dynamic Data Pruning for Automatic Speech Recognition.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Dynamic Data Pruning for Automatic Speech Recognition

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:21.235727Z

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Observation d859c6a1-2d00-492d-8768-0045a8ede3f6 · outbound

This paper cites Dataset Pruning: Reducing Training Data by Examining Generalization Influence.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:21.345501Z

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Observation 2d6bdf1e-b466-4123-a780-929ca627ce88 · outbound

This paper cites an unresolved cited work.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Unresolved cited work

Reference 27

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Observation 94036946-2da2-47a2-8cc9-033b9801ab89 · outbound

This paper cites doi: 10.18653/v1/D18-1259.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation doi: 10.18653/v1/D18-1259

Reference 28

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Observation 28d1f29e-4c95-4a9c-8d0d-8e073664479b · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 29

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Observation 8357659c-82a6-4480-9293-e3176eb8af40 · outbound

This paper cites While models like NV-Embed- v1 (Lee et al.,.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation While models like NV-Embed- v1 (Lee et al.,

Reference 31

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Observation b289423e-0e40-4d3c-b50c-b527861129fc · outbound

This paper cites an unresolved cited work.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Unresolved cited work

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:21.933113Z

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Observation 4a67eb4b-4506-40b7-a3d7-703e6f40331e · outbound

This paper cites an unresolved cited work.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:22.008506Z

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Observation 82eae0b4-73a4-4fc9-ba09-52d29d91e4d0 · outbound

This paper cites an unresolved cited work.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:22.067937Z

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Observation c789ca9a-2e8d-40f4-9930-5a0a617f2596 · outbound

This paper cites Our work extends these ideas to the domain adaptation setting, where different considerations apply due to the distinct nature of the training data.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Our work extends these ideas to the domain adaptation setting, where different considerations apply due to the distinct nature of the training data

Reference 35

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Observation 0e4b7d2b-170a-44f4-bbce-5b9d8c966f86 · outbound

This paper cites A.3 Curriculum Learning and Adaptive Training Curriculum learning has emerged as a promising approach to train neural networks by presenting training examples in a meaningful order.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation A.3 Curriculum Learning and Adaptive Training Curriculum learning has emerged as a promising approach to train neural networks by presenting training examples in a meaningful order

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:22.237137Z

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Observation 4dd6f081-0d31-46bc-8eb4-ca0a6d0859e0 · outbound

This paper cites • Training set:Documents with relevance levels of 2, 3, and 4 were treated as positive samples.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation • Training set:Documents with relevance levels of 2, 3, and 4 were treated as positive samples

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:22.509911Z

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Observation b53c9357-82a3-4b3e-9c78-9577476103ee · outbound

This paper cites an unresolved cited work.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Unresolved cited work

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:22.664748Z

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Observation e7108f77-525c-49b6-88c7-0bcb5cf711f3 · outbound

This paper cites The results, presented in Figure 4, demonstrate that SP consistently surpasses FT in terms of NDCG@10 across all retention rates.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation The results, presented in Figure 4, demonstrate that SP consistently surpasses FT in terms of NDCG@10 across all retention rates

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:22.725072Z

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Observation 194985a2-0dbd-423e-b2be-877f175dbce9 · outbound

This paper cites The temperature is set to 0.02, and the system leverages cross-device negatives during training.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation The temperature is set to 0.02, and the system leverages cross-device negatives during training

Reference 64

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Observation 26e17d7e-00ae-44e8-ad84-d38bbadccf03 · outbound

This paper cites Mistral 7B.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Mistral 7B

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:19.239397Z

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Observation 0569c59f-1251-41b0-b54c-4d1abc1b0bad · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 2003

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:20.309080Z

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Observation d7681e02-8582-41e1-866e-59495178c3c1 · outbound

This paper cites Other works have explored the use of teacher-student frameworks (Matiisen et al., 2019), where a teacher model determines the curriculum for a student model.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Other works have explored the use of teacher-student frameworks (Matiisen et al., 2019), where a teacher model determines the curriculum for a student model

Reference 2010

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Observation f6e60640-fb9b-4efa-8ea0-000cb72bff21 · outbound

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

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:18.518206Z

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Observation b62934c8-f276-48f9-bbf4-49d641e9f2b1 · outbound

This paper cites Dense Passage Retrieval for Open-Domain Question Answering.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Dense Passage Retrieval for Open-Domain Question Answering

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:19.377747Z

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Observation 6cea7470-e9d7-484a-b8f4-8affa5e61608 · outbound

This paper cites How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:18.740900Z

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Observation 785ed716-352c-40bd-9124-9c65858132a0 · outbound

This paper cites InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation InfoBatch: Lossless Training Speed Up by Unbiased Dynamic Data Pruning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:20.139275Z

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Observation 5ff49fe2-80b8-4097-9259-41c965a017e3 · outbound

This paper cites Universal Language Model Fine-tuning for Text Classification.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Universal Language Model Fine-tuning for Text Classification

Reference 2020

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Observation f5d75f63-dbad-44a4-a796-719ec79f6d38 · outbound

This paper cites Unsupervised Dense Information Retrieval with Contrastive Learning.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Unsupervised Dense Information Retrieval with Contrastive Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:19.052789Z

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Observation 2a9d85cf-f43b-40d5-9b31-af4a42ce8166 · outbound

This paper cites Generative Representational Instruction Tuning.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Generative Representational Instruction Tuning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:19.873982Z

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Observation 88e2f1ee-ad78-4f86-99bb-8583cdb2831b · outbound

This paper cites Accelerating Deep Learning with Dynamic Data Pruning.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Accelerating Deep Learning with Dynamic Data Pruning

Reference 2023

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Observation 4e24626d-11e1-400f-879a-023973c2779b · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2024

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Observation 2f08ae21-6b82-4d08-a992-dffcd7a9b5fb · outbound

This paper cites Under review.

OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation Under review

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T02:34:21.724741Z

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Pith citing papers

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