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Paper Citation Record · LEDGER

DataS^3: Dataset Subset Selection for Specialization

As of 23 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 3 inbound Pith citation observations for arXiv:2504.16277.

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

pith.paper-citation-record.v1
2504.16277 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:12:59.725083Z

measured 23 of 23 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:11:01.510667Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T09:03:15.951664Z

Reference resolution

20 of 20 outbound references displayed

  • verified exact0
  • verified fuzzy13
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 722e5930-3e23-4e01-88dd-f26176daf3e3 · outbound

This paper cites low data quality.

DataS^3: Dataset Subset Selection for Specialization low data quality

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:12:59.930440Z

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=2026-08-16T11:12:59.710067Z digest=sha256:742ea593a2965dbd6f4a2231ee519817c97e3f3a0ea82e5c1f03048d90d526e1

Observation 0d6b5fde-27a5-4c95-a940-3f7c27e914f5 · outbound

This paper cites When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations.

DataS^3: Dataset Subset Selection for Specialization When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-16T11:12:59.643113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:12:59.643113Z digest=sha256:9b14b398fa29a491fed6ca53c692df3c919b63b57134d2d03d345ddbfd8acf6d

Observation f7e29eb6-2b99-4458-9bde-502048b26a33 · outbound

This paper cites Active Learning for BERT: An Empirical Study.

DataS^3: Dataset Subset Selection for Specialization Active Learning for BERT: An Empirical Study

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:13:00.044737Z

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=2026-08-16T11:12:59.661595Z digest=sha256:f7253ffc66f5fe640ec78d3206aacb126419db668060e3607fc0026402a53f8b

Observation 72d02356-3171-449f-9067-50d5aa710763 · outbound

This paper cites SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification.

DataS^3: Dataset Subset Selection for Specialization SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification

Reference 10

Resolution
metadata mismatch
local_arxiv, observed 2026-08-16T11:12:59.813971Z

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=2026-08-16T11:12:59.665698Z digest=sha256:73fe4a4398ddaae8873d1b68b52ffa6b8c88b93551880db99ff9dd7e8cb14fb3

Observation 8bfad4d4-d481-4dd7-85f9-692d9344176d · outbound

This paper cites Measuring Robustness to Natural Distribution Shifts in Image Classification.

DataS^3: Dataset Subset Selection for Specialization Measuring Robustness to Natural Distribution Shifts in Image Classification

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-16T11:12:59.683981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:12:59.683981Z digest=sha256:d6ae17a67446d0188a3f9ce25dcb43838933adaad610fb77c8305130d803761e

Observation 222fb1f7-73b0-456b-b242-29cee7ed5682 · outbound

This paper cites Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models.

DataS^3: Dataset Subset Selection for Specialization Drive anywhere: Generalizable end-to-end autonomous driving with multi-modal foundation models

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:12:59.976072Z

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=2026-08-16T11:12:59.689209Z digest=sha256:e4c20988f1e458c129953674440733eecb489929f435a1b9ee51293424de9326

Observation 6ec94220-d1db-466f-a918-0400823cac6a · outbound

This paper cites Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber.

DataS^3: Dataset Subset Selection for Specialization Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:12:59.959109Z

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=2026-08-16T11:12:59.693849Z digest=sha256:60ecde583355d3f7f9c29352de7a26c3a704bb65d2a14c91d6a629f614b6c1d8

Observation 8aa7a5c7-61aa-48a0-8346-5f5337425c4f · outbound

This paper cites efficiency-style.

DataS^3: Dataset Subset Selection for Specialization efficiency-style

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:12:59.944862Z

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=2026-08-16T11:12:59.700982Z digest=sha256:c334177b82798412c5f7ac8aca4da5c1a33dd8db780786689746720538f65d39

Observation f001f79e-eb31-4708-b397-c08bb7d2826a · outbound

This paper cites in-distribution.

DataS^3: Dataset Subset Selection for Specialization in-distribution

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:12:59.915520Z

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=2026-08-16T11:12:59.717714Z digest=sha256:113c31bd380f44155b47bb24e20a12cb6200c28349c9866828775b7c5569126f

Observation 2f2b843f-3d20-4bb2-b130-ec9e7cb3f670 · outbound

This paper cites extraneous.

DataS^3: Dataset Subset Selection for Specialization extraneous

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:12:59.901982Z

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=2026-08-16T11:12:59.725083Z digest=sha256:9e56ddb6aabe0b104619473fd8c572dc834a4fa5b9e0d6cf9a78334b8e040816

Observation 139aac4e-0964-4438-afdb-c64e2c426b6b · outbound

This paper cites Zhang, Shaoqing Ren, and Jian Sun.

DataS^3: Dataset Subset Selection for Specialization Zhang, Shaoqing Ren, and Jian Sun

Reference 2004

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:13:00.028538Z

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=2026-08-16T11:12:59.670533Z digest=sha256:d7ec809ec2a31e1eba95ac05c599867d46a92ee4d25ec21f432f0a18b862a917

Observation 6b9a3b2c-d562-4f52-8cc8-9241965e964e · outbound

This paper cites An open-source platform for underwater image and video analytics.

DataS^3: Dataset Subset Selection for Specialization An open-source platform for underwater image and video analytics

Reference 2008

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:13:00.076458Z

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=2026-08-16T11:12:59.648014Z digest=sha256:8ce8d0bce6d3d6f397126ac15b08e4dd84c65d8603ac0ad6550a126637270197

Observation f361b2ef-f449-4b3b-a78a-21849a2e026d · outbound

This paper cites an unresolved cited work.

DataS^3: Dataset Subset Selection for Specialization Unresolved cited work

Reference 2009

Resolution
unresolved
raw_fallback, observed 2026-08-16T11:13:00.009818Z

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=2026-08-16T11:12:59.675397Z digest=sha256:1675e3f0ecaf70762492baa964c21e5bd0717aa28c772fb94238450b4bb74f45

Observation ffc13944-4bb3-49d7-a45f-02974818bce5 · outbound

This paper cites New Frameworks for Offline and Streaming Coreset Constructions.

DataS^3: Dataset Subset Selection for Specialization New Frameworks for Offline and Streaming Coreset Constructions

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-16T11:12:59.631115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:12:59.631115Z digest=sha256:c153c027d8514b7f5c25344de8e8780573c9f9cbf98b37893fcd7ac494fe486b

Observation e0b6f37f-8d59-4371-9000-efc2eaf734ea · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

DataS^3: Dataset Subset Selection for Specialization Imagenet: A large-scale hierarchical image database

Reference 2017

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:13:00.063345Z

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=2026-08-16T11:12:59.653229Z digest=sha256:032fb825e7ed61796f641f51c7ed6834d373452e4684f031a0da7cf8e618333d

Observation 1f05a677-7bf7-431d-8e8b-b1b9a7caa592 · outbound

This paper cites The iwildcam 2021 competition dataset,.

DataS^3: Dataset Subset Selection for Specialization The iwildcam 2021 competition dataset,

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:13:00.104759Z

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=2026-08-16T11:12:59.622039Z digest=sha256:0b45606d043ae1d7e2f94c31807756a414d1bbe9b99ae9a1a14a716bffbb7153

Observation ac244540-d017-4716-b9c9-31daece11d9d · outbound

This paper cites Language Models are Few-Shot Learners.

DataS^3: Dataset Subset Selection for Specialization Language Models are Few-Shot Learners

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-16T11:12:59.636255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:12:59.636255Z digest=sha256:62c833f0fd6c7020ae4a1ef30d6ec5e434ba420b7ce7c42d5faf5a14a72633fb

Observation 0dbd61fa-096f-4bf5-9699-0f18b9b2831f · outbound

This paper cites Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, and Jonathan Huang.

DataS^3: Dataset Subset Selection for Specialization Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, and Jonathan Huang

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:13:00.089991Z

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=2026-08-16T11:12:59.626888Z digest=sha256:3f8bac3c05b640da7959b5a2f6724ea69eb0eb062d3ac26e02a80d44b4b684fd

Observation 9fa48ff3-55cb-4f05-8ccc-78b2fd7af2ec · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

DataS^3: Dataset Subset Selection for Specialization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-16T11:12:59.657095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:12:59.657095Z digest=sha256:df74f111876df2909a850587c7bdfcd5626e431dcf4b2b5bd757acc866deaba8

Observation 0af5b4a2-d719-4dde-aad0-2cc0d7801501 · outbound

This paper cites Living planet report 2020: Bending the curve of biodiversity loss,.

DataS^3: Dataset Subset Selection for Specialization Living planet report 2020: Bending the curve of biodiversity loss,

Reference 2023

Resolution
verified fuzzy
raw_fallback, observed 2026-08-16T11:12:59.992830Z

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=2026-08-16T11:12:59.679885Z digest=sha256:0d2a16289ef373e3549350e67b438f04298731afb74d8337a2b14c7b0ac13570

Pith citing papers

Observation 1ec67ea5-43a0-4183-9063-6db0fc85e775 · inbound

A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't) cites this paper.

A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't) DataS^3: Dataset Subset Selection for Specialization

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-02T23:11:01.510667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T23:11:01.510667Z digest=sha256:328870b4675c4d95063aff0775d963d83200dcae559b37896a7bceade60d4f3a

Observation 273cbdb5-5794-41e2-90b1-10073ee69bc6 · inbound

Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching cites this paper.

Efficient Test-Time Finetuning of LLMs via Convex Reconstruction and Gradient Caching DataS^3: Dataset Subset Selection for Specialization

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-06-29T09:03:15.953204Z

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=2026-06-29T08:58:52.511363Z digest=sha256:6357ea7859c85b9e410899f279d5cf3c3f0c6bce32f2d43677073d4d6eedfe06

Observation c1f1a7be-71d1-4a3b-94c2-16ed7e5cdff1 · inbound

Provable Pruning for Efficient 3D Gaussian Splatting via Coresets cites this paper.

Provable Pruning for Efficient 3D Gaussian Splatting via Coresets DataS^3: Dataset Subset Selection for Specialization

Reference 49

Resolution
unresolved
no resolver link, observed 2026-07-12T07:31:01.780245Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T07:31:01.780245Z digest=sha256:329502ab4e87b18eba68af1e27e937364c2a2fcebb619e19c30f1e50050e38a5