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Paper Citation Record · LEDGER

Instruction Mining: Instruction Data Selection for Tuning Large Language Models

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 inbound Pith citation observations for arXiv:2307.06290.

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

pith.paper-citation-record.v1
2307.06290 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:46:10.847203Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:49:17.695166Z

Reference resolution

0 of 0 outbound references displayed

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Outbound references

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Pith citing papers

Observation 42bb65dc-b14d-4987-9e6b-99e210376ade · inbound

MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets cites this paper.

MM-LIMA: Less Is More for Alignment in Multi-Modal Datasets Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 16

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arxiv_id, observed 2026-05-24T08:14:10.175046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-24T08:13:18.191233Z digest=sha256:2d55b111d3dfa130bea968da5821bf24c500e1f8b733572c07de0c2e7b08ace2

Observation d84fe3e4-cad7-4bb8-9fc6-41793cf5e71c · inbound

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models cites this paper.

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 1

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arxiv_id, observed 2026-05-12T14:21:16.542943Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-12T14:21:16.453610Z digest=sha256:4bcb8c858c26e63ac86834b112b59bee50cec27b442d77df1ac6abc0aa93d6fd

Observation c902675e-354f-4eb7-87bd-85b34d25f90c · inbound

Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph cites this paper.

Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 11

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source=pdf_text observed=2026-08-12T10:37:50.825621Z digest=sha256:78bf0cbcfa072c60118499985030f80fff788975543047fa554ec4135f6eefb1

Observation f58c4426-27ed-40aa-8914-61e0532ec9fb · inbound

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models cites this paper.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 26

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source=arxiv_source observed=2026-08-11T22:57:01.559066Z digest=sha256:174c669e10ac5348f6a8143b1f3b65948f2d2077030453710e7eacb99ffbfa2c

Observation 0f183dbe-10f3-474b-bd96-9f14b2929f00 · inbound

Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness cites this paper.

Mastering Collaborative Multi-modal Data Selection: A Focus on Informativeness, Uniqueness, and Representativeness Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 9

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source=pdf_text observed=2026-08-11T19:54:51.909509Z digest=sha256:a7ad0d4445eeb3a9aa71e645fe631df019e741bc81c01621e28865855ec585c2

Observation 4a31ad4a-7136-45e8-8b5c-14b1d658da55 · inbound

Small Language Model as Data Prospector for Large Language Model cites this paper.

Small Language Model as Data Prospector for Large Language Model Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 3

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source=arxiv_source observed=2026-08-11T16:33:10.430133Z digest=sha256:9c2dd4947e5a0ae1696e24309abf19d9efd373289f594b21603ce686870c9d4e

Observation 87d551f8-e1c3-4e3a-95b2-15dce4f78a0e · inbound

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis cites this paper.

ResoFilter: Fine-grained Synthetic Data Filtering for Large Language Models through Data-Parameter Resonance Analysis Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 3

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source=arxiv_source observed=2026-08-11T11:59:28.194116Z digest=sha256:8bb948554f3d8aa2a86dd7aba256aedae9ce1cb6978d3a56a8ee8341eb3926e3

Observation 6c8a38b3-a57a-4f0f-8789-2c22a981b988 · inbound

Boosting LLM via Learning from Data Iteratively and Selectively cites this paper.

Boosting LLM via Learning from Data Iteratively and Selectively Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 2006

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source=pdf_text observed=2026-08-11T05:38:36.697541Z digest=sha256:8a4a7918971a5d47626942a773b1d9a7bdbb5a18b58a559d87756d016b1ac686

Observation b8610aeb-5a97-4dfd-a294-bbd6de6acbf7 · inbound

A Survey on Large Language Models with some Insights on their Capabilities and Limitations cites this paper.

A Survey on Large Language Models with some Insights on their Capabilities and Limitations Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 263

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source=pdf_text observed=2026-08-10T22:17:56.410331Z digest=sha256:c8ea01581f2c6b845a32714efc00a077376d5593e27e377cb45a193bb3796de7

Observation c6714b32-3947-4c83-b032-bb97638279d4 · inbound

Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spaces cites this paper.

Social-LLaVA: Enhancing Robot Navigation through Human-Language Reasoning in Social Spaces Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 33

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source=pdf_text observed=2026-08-10T23:01:08.449490Z digest=sha256:54dfd5c2a0c058692cb05645ddbf7fca1df2240ee28e875fabb1f266a138937b

Observation 670ba245-b910-41ea-83db-6be14fa6775f · inbound

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities cites this paper.

Improving Influence-based Instruction Tuning Data Selection for Balanced Learning of Diverse Capabilities Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 4

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no resolver link, observed 2026-08-10T17:34:52.349982Z

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source=arxiv_source observed=2026-08-10T17:34:52.349982Z digest=sha256:4e602e52361189305aa82f490d779b9c8118c774434086c10727a011e2d88218

Observation 65176a91-5dcc-4722-b4a0-f3a9ef0a97c1 · inbound

R.I.P.: Better Models by Survival of the Fittest Prompts cites this paper.

R.I.P.: Better Models by Survival of the Fittest Prompts Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 3

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no resolver link, observed 2026-08-09T23:02:23.296216Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T23:02:23.296216Z digest=sha256:4dea19ba16db6b1e15253ba410d54cd639290f0037abcc33a44469a4ea3154f4

Observation 9b416675-eaf6-433e-b784-3d726aaf998a · inbound

Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering cites this paper.

Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 2024

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no resolver link, observed 2026-08-08T13:07:08.294764Z

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source=pdf_text observed=2026-08-08T13:07:08.294764Z digest=sha256:d41bb3d7321cf3b644f80d953b41e98921c8eb6b6adb155b1fdbccd6dc5918e7

Observation ee8b96bc-0712-46a4-a494-6b2c784d719b · inbound

Measuring Diversity in Synthetic Datasets cites this paper.

Measuring Diversity in Synthetic Datasets Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 6

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T04:54:50.582249Z digest=sha256:3647751c462eb300591d2980c8da7b53700a8c9f406e83ca73db587d253a045a

Observation 4fe5805a-6445-47aa-b221-09d44d6ab5f2 · inbound

Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples cites this paper.

Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 15

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no resolver link, observed 2026-08-08T11:57:11.728183Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T11:57:11.728183Z digest=sha256:f40639330afba7cbd0072c03f98c132be2d3636e2fe7b27c516d0b85c28bdc8c

Observation 663ff74e-816e-4d2d-b2e6-29d3f9ee6239 · inbound

AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning cites this paper.

AutoSpatial: Visual-Language Reasoning for Social Robot Navigation through Efficient Spatial Reasoning Learning Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 34

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arxiv_id, observed 2026-05-23T00:27:17.816053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-23T00:26:58.273861Z digest=sha256:a0fbd40f4f5d9795d905b959ff485512d82e2fe2c85eab2849563a69d3edd963

Observation 9222b06e-5a74-485f-b531-3b5942f40bb4 · inbound

DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning cites this paper.

DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 7

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:46:10.847203Z digest=sha256:29d23622c08650a4c781d95fafccfb7ab15bb68dd5d0815a7ceb40cf93dc906b

Observation 8da6abe7-b0b7-4fd7-b24f-a2d46321652f · inbound

A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms cites this paper.

A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 271

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source=pdf_text observed=2026-08-16T11:07:59.511218Z digest=sha256:1b96410de84ff768287596a52a7a4ffbdb282d313b47648d75956cd0743ff67b

Observation 155a8d2c-3da2-4604-87a0-b801db279be9 · inbound

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection cites this paper.

RICo: Refined In-Context Contribution for Automatic Instruction-Tuning Data Selection Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 4

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no resolver link, observed 2026-08-15T23:12:38.741716Z

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source=arxiv_source observed=2026-08-15T23:12:38.741716Z digest=sha256:22df328f32c419002cf1cb0beb82669dd732a8555a9094a027d0db718b9a937e

Observation 19e4d153-5cdb-476e-96c9-1d8cd985172b · inbound

Merge to Mix: Mixing Datasets via Model Merging cites this paper.

Merge to Mix: Mixing Datasets via Model Merging Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 8

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no resolver link, observed 2026-08-07T15:09:33.588670Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:09:33.588670Z digest=sha256:b1d11368326680369959c77d89a5966af3dfdc7076999f2532de687309bf0021

Observation 11863cfa-4401-49d1-bde0-355e87d328c5 · inbound

Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks cites this paper.

Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:58:32.633147Z digest=sha256:10e88bf41d5d998a867a2d8ce3af349845f29658170eef34c4cce9fe4313afd0

Observation 16e7db2c-f332-4fa9-bbe5-5e83e1d9133f · inbound

A Survey of LLM $\times$ DATA cites this paper.

A Survey of LLM $\times$ DATA Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 84

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no resolver link, observed 2026-08-07T14:33:12.899936Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:33:12.899936Z digest=sha256:b30754ff976d786e56d8c976cd3747ae5a89182d0793a24a04fab20bce6a2076

Observation 99126653-6042-4339-a9fe-071207acb0c2 · inbound

TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation cites this paper.

TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 2023

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source=pdf_text observed=2026-08-07T14:32:13.530356Z digest=sha256:298708441e3ffb5d9b4e1cb9c08fa54ef8795f9a3bbf69ed333b69281f1d8ff8

Observation 4553c525-29a0-4827-8eaf-0364a95e7b86 · inbound

Efficient Data Selection at Scale via Influence Distillation cites this paper.

Efficient Data Selection at Scale via Influence Distillation Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 41

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source=arxiv_source observed=2026-08-07T14:25:21.464252Z digest=sha256:1bc2567c94769ed87f9c244fbcb9a948572ba784bb336ff9e36938d5287bea99

Observation c2e9f62e-708f-4c21-84bc-ea1c79d19ced · inbound

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation cites this paper.

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 2

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no resolver link, observed 2026-08-07T14:00:29.600015Z

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source=arxiv_source observed=2026-08-07T14:00:29.600015Z digest=sha256:4e6cd5c0eb6f4990a3d50532ff9ab0b8279111af5b49ef8f9f4e0ebb8967bfce

Observation dcf1a6d7-877f-4d33-8226-a98385e3554e · inbound

Towards Efficient and Effective Alignment of Large Language Models cites this paper.

Towards Efficient and Effective Alignment of Large Language Models Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 19

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source=pdf_text observed=2026-08-07T04:55:35.093796Z digest=sha256:5f635d307ac6a7b7ae47801446d300ddb0e007cbc830c2518f2e5257e824ceeb

Observation 08b7ce6f-70e8-4e2e-a172-63c699e58f94 · inbound

ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs cites this paper.

ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 3

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source=arxiv_source observed=2026-08-07T04:37:06.226403Z digest=sha256:f577afe62b45948bb095f552c85127ad9568011f9e91690a929722aad750c098

Observation bc631eb7-ffa2-44e4-ad5e-610667995f35 · inbound

OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models cites this paper.

OVFact: Measuring and Improving Open-Vocabulary Factuality for Long Caption Models Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 2023

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source=pdf_text observed=2026-08-15T18:00:44.495063Z digest=sha256:267c7eff7a0f4da1b00253b8793c9d561eb750d4232bc1acc576c9822fd01ab3

Observation dfcd6ad6-1880-4786-868f-4f7737c631c8 · inbound

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning cites this paper.

ReSURE: Regularizing Supervision Unreliability for Multi-turn Dialogue Fine-tuning Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 4

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source=arxiv_source observed=2026-08-15T16:53:22.770073Z digest=sha256:53f3a3cf3c1740d4f6df1107eafba6ea5e6d8f5d37e7de2680893a2562c08768

Observation 6f4c6a3d-937d-4a27-aa13-7b627447d7d4 · inbound

Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods cites this paper.

Rethinking Data Curation in LLM Training: Online Reweighting Offers Better Generalization than Offline Methods Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 7

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arxiv_id, observed 2026-05-10T07:11:53.337299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-10T07:09:21.652035Z digest=sha256:f148ba018d50862ea6454ea27553117c0c6ffd490141f1e0f83ba182b015a763

Observation c5140b54-8b76-4bc2-b6de-0770784267e3 · inbound

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training cites this paper.

Dr. Post-Training: A Data Regularization Perspective on LLM Post-Training Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 123

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arxiv_id, observed 2026-05-11T04:05:56.776981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-11T01:57:40.347786Z digest=sha256:151c20c0aa09b0f4ef8e9270fd6b57bd7265c061980dd112db6e2bba10003ed8

Observation 8fb8b7cc-7eab-476a-8ced-be9e00067922 · inbound

HARP: Efficient Data Selection for Finetuning Large Language Models cites this paper.

HARP: Efficient Data Selection for Finetuning Large Language Models Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 20

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arxiv_id, observed 2026-07-02T16:17:09.376334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-06-27T22:49:48.330530Z digest=sha256:fae5a7b640a95b3ddbf163dd3f0724d791e87fbc3fd96639620ca657769fbf5e

Observation 23a3e80d-cb90-470b-b2e4-916a38cdbe6e · inbound

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework cites this paper.

OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 83

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arxiv_id, observed 2026-07-02T23:07:27.187260Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-06-27T18:26:32.883834Z digest=sha256:30d679b69284f725d8f2b2300977623cf1b23457acdac92a81681413e47c4e7d

Observation c43ee3e2-8d64-4b29-9e90-8c920ad234a8 · inbound

Predicting Mergeability of Parameter-Efficient Fine-Tuning Updates cites this paper.

Predicting Mergeability of Parameter-Efficient Fine-Tuning Updates Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 47

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arxiv_id, observed 2026-07-04T00:49:17.697551Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-06-26T21:01:04.043286Z digest=sha256:ea791a0529ca91155dc574e96c336ef3931d7a3d82c7503f7fafd10191535486

Observation fbf07f84-e4e4-41be-85f1-9f0fef4c3c2c · inbound

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement cites this paper.

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement Instruction Mining: Instruction Data Selection for Tuning Large Language Models

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T13:54:03.003356Z digest=sha256:34ae6451d937b22932d72fbf64a7ee8782e75880ce8b620535f53bd74cd09578

Observation 9ae12d72-ebda-4805-9f22-fb5d795b8f8d · inbound

Agentic Instruction Data Selection: Let DataMaster Interpret Your Intent cites this paper.

Agentic Instruction Data Selection: Let DataMaster Interpret Your Intent Instruction Mining: Instruction Data Selection for Tuning Large Language Models

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source=arxiv_source observed=2026-08-12T21:35:51.646640Z digest=sha256:9f378100273c0ef274010f0ce822978714ed0c14515b452d3b433c401cf2880d