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Paper Citation Record · LEDGER

Accounting for Variance in Machine Learning Benchmarks

As of 27 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2103.03098.

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

pith.paper-citation-record.v1
2103.03098 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-07-27T06:30:09.085275+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T08:46:29.099447Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T16:39:58.508622Z

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 fc3391f6-c76f-4669-ac5b-9f77a4a832b5 · inbound

QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks cites this paper.

QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks Accounting for Variance in Machine Learning Benchmarks

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-11T11:56:29.644687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

source=arxiv_source observed=2026-05-10T04:27:11.735657Z digest=sha256:4642111d65f36d4ede0549ae47504e0b03df0ab3bb7002f5b8d24154bcd4153f

Observation 125fbc8a-b8eb-47b1-a456-c1f0ed959364 · inbound

SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarks cites this paper.

SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarks Accounting for Variance in Machine Learning Benchmarks

Reference 7

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T22:54:01.011913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

source=arxiv_source observed=2026-06-29T22:50:48.263600Z digest=sha256:4bab66e5aeb812d59a29f486e0f418317b2577962197f50d561e71c05275f02d

Observation 62093066-090e-4af8-ae41-0f36bc1f6d6d · inbound

A Pre-Registered Causal Partition of Self-Consistency Elicitation and Reward Design in RLVR cites this paper.

A Pre-Registered Causal Partition of Self-Consistency Elicitation and Reward Design in RLVR Accounting for Variance in Machine Learning Benchmarks

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-07-02T13:06:59.087174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

source=pdf_text observed=2026-06-28T01:35:26.306648Z digest=sha256:5d2c125533ae774effdf30aa30c4609d32dc6b1dc1727024397912b3336d5612

Observation 182831b8-0846-464d-b9d8-3d90703284e6 · inbound

From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol cites this paper.

From Forecasting Leaderboards to Deployment Decisions: A Fail-Closed Certification Protocol Accounting for Variance in Machine Learning Benchmarks

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-04T16:39:58.510193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-27T06:30:09.085275+00:00.

source=pdf_text observed=2026-06-26T00:18:20.146234Z digest=sha256:a097bd5bec05581b4a60d10558b59d083cb2dd9338385cea3b40b37e473e934d

Observation 4642e8ac-6ba4-4372-8407-365155d4dbfc · inbound

Grokking Is Conditional and Fragile: A Fully-Tractable, Multi-Seed Study at 12K Parameters cites this paper.

Grokking Is Conditional and Fragile: A Fully-Tractable, Multi-Seed Study at 12K Parameters Accounting for Variance in Machine Learning Benchmarks

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-11T08:46:29.099447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T08:46:29.099447Z digest=sha256:49fcdbff381f83f5b46cb0b634ebb424a17e21ce50f5a5bd2bd546b492cb1a06