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Paper Citation Record · LEDGER

Submodularity In Machine Learning and Artificial Intelligence

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2202.00132.

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

pith.paper-citation-record.v1
2202.00132 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 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 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:47:40.941197Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T03:37:35.232087Z

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 6c73a10c-b19d-4f42-89b6-bbb1b2d90002 · inbound

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models cites this paper.

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models Submodularity In Machine Learning and Artificial Intelligence

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-13T10:07:53.794000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-13T10:07:53.748795Z digest=sha256:b3c135ffe8b6e3d02532fd3431e911eac64131546dccabd5d5199071911228c5

Observation 6c273336-5036-4ebe-85da-754aa2c01fd4 · inbound

COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation cites this paper.

COBRA: COmBinatorial Retrieval Augmentation for Few-Shot Adaptation Submodularity In Machine Learning and Artificial Intelligence

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-11T05:22:11.570886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:22:11.570886Z digest=sha256:e8c7f4d37f40236cc45be840d3bfeeed84f8f0e65f709535ba94bf83f644ff58

Observation e79792ec-0ff8-4fe3-8f42-f2f94b2dfe8a · inbound

SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation cites this paper.

SELECT: A Submodular Approach for Active LiDAR Semantic Segmentation Submodularity In Machine Learning and Artificial Intelligence

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-15T23:47:40.941197Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:47:40.941197Z digest=sha256:c05c5267d3ac53e38fef604d44cbf78f6993ac246bc034c5a33aa07f931a078f

Observation 2e4e283d-aab2-442a-9bb0-16ab785df192 · inbound

Partitioning and Observability in Linear Systems via Submodular Optimization cites this paper.

Partitioning and Observability in Linear Systems via Submodular Optimization Submodularity In Machine Learning and Artificial Intelligence

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-07T15:11:36.334568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:11:36.334568Z digest=sha256:5f56497fc54a2bcb6b30edd295684139b00ef5fcb731393828dde7a17fee4ba3

Observation 28346ea3-1fb3-4d53-9bcb-da400756e2dc · inbound

Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs cites this paper.

Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs Submodularity In Machine Learning and Artificial Intelligence

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T12:47:43.011261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T12:47:43.011261Z digest=sha256:d3e6b425fce4c44216cbd37de1cfbc574864e6ff27359a842d9cd704437c50e8

Observation ada8709e-93ba-4c3b-9024-b3c542f44dc4 · inbound

Robust Least Squares Problems with Binary Uncertain Data cites this paper.

Robust Least Squares Problems with Binary Uncertain Data Submodularity In Machine Learning and Artificial Intelligence

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-04T10:13:58.842695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T10:13:58.842695Z digest=sha256:47d88c193b3a3617f6ebf3acc8a8da31a41a1e5461930e259464e0457a3c172d

Observation 2f38a1b3-e5de-4eeb-b647-e0abf2c7d355 · inbound

Sum of Squares Submodularity cites this paper.

Sum of Squares Submodularity Submodularity In Machine Learning and Artificial Intelligence

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-15T15:50:36.741798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T15:50:36.741798Z digest=sha256:0a1ef678d53d4521d286769f9370426f7825e4699d501dd1a51e732e06eaac2c

Observation d9836d13-7cf4-4e8c-b331-384ccbd0245f · inbound

Accelerated Relax-and-Round for Concave Coverage Problems cites this paper.

Accelerated Relax-and-Round for Concave Coverage Problems Submodularity In Machine Learning and Artificial Intelligence

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:05:57.075458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-11T00:52:41.972226Z digest=sha256:4c0de6ef57aea5bc139d86063081496c96e98045f62e676556a216b97d35d6f6

Observation 41e25a10-01f4-49de-abec-f81ba440bba9 · inbound

SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning cites this paper.

SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning Submodularity In Machine Learning and Artificial Intelligence

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-14T20:32:56.596654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-14T20:32:00.361991Z digest=sha256:f9c891840e4047f8c13a0a0a537fc94f9ac76374c527eb4867e61601012c1305

Observation ba973b6c-f49c-4208-bd03-2ae66975c2b6 · inbound

Complement Submodular Information Measures for Balanced and Robust Data Selection cites this paper.

Complement Submodular Information Measures for Balanced and Robust Data Selection Submodularity In Machine Learning and Artificial Intelligence

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-06-30T13:24:40.230230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-30T13:18:02.382334Z digest=sha256:34b1361a49118f20d3b4edb3704d5279529b8975feab26722d1d1c7bdf478b93

Observation 94563602-b027-4df5-9cde-02316fde0025 · inbound

How Much Is a Dataset Worth? Scaling Laws, the Vendi Score, and Matrix Spectral Functions cites this paper.

How Much Is a Dataset Worth? Scaling Laws, the Vendi Score, and Matrix Spectral Functions Submodularity In Machine Learning and Artificial Intelligence

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:33:14.772666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-29T08:33:05.952601Z digest=sha256:ac79c70125577f877ebcf3d19352525a7a2bb3d5640fa896a12350f68258690c

Observation 21ea8ec0-5cb7-4200-8069-1fd20dfde975 · inbound

How Much Is a Dataset Worth? Scaling Laws, the Vendi Score, and Matrix Spectral Functions cites this paper.

How Much Is a Dataset Worth? Scaling Laws, the Vendi Score, and Matrix Spectral Functions Submodularity In Machine Learning and Artificial Intelligence

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-02T12:58:41.632832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T12:58:41.632832Z digest=sha256:be56183295f94a5558b48eb6c3e28aded9918a444dced9cd5d7beb036ce0ec77

Observation 9e9a1fbb-1fe6-4a2d-86ac-43484eb71e5a · inbound

Submodular Optimization with Applications to Decision and Control cites this paper.

Submodular Optimization with Applications to Decision and Control Submodularity In Machine Learning and Artificial Intelligence

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-07-03T03:37:35.233423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-27T15:14:03.455914Z digest=sha256:5d32a1f3d54d2a9265c922b70c1e054776029764b51c58b3368555b1a322f988

Observation 322e00e5-5ad4-47e9-a1c4-66b2d950b7ae · inbound

Recall Is Not Enough: A Reader-Context Diagnostic for Budget-Constrained Retrieval-Augmented Generation cites this paper.

Recall Is Not Enough: A Reader-Context Diagnostic for Budget-Constrained Retrieval-Augmented Generation Submodularity In Machine Learning and Artificial Intelligence

Reference 41

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T13:36:58.511304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-07-02T13:32:55.274187Z digest=sha256:e59ee7916d7f085759d9acbf4390757cc9ac780e102bb308117429b547410620

Observation ce978379-3edd-40a9-83e3-d6677f4944d2 · inbound

Submodular Maximization over Many Matroids via Ordered Local Search cites this paper.

Submodular Maximization over Many Matroids via Ordered Local Search Submodularity In Machine Learning and Artificial Intelligence

Reference 167

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T04:36:35.996952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-07-02T04:31:33.375845Z digest=sha256:0d4c73491916a6d73aac33b06e459e01cac04cc9e6c95d1ec4529f0f72c106ac