Paper Citation Record · LEDGER

Revisiting the Calibration of Modern Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2106.07998.

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

pith.paper-citation-record.v1
2106.07998 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:06:46.651932Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

69
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 65c861cc-6e06-49dc-963f-d45db9eeec59 · inbound

Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making cites this paper.

Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making Revisiting the Calibration of Modern Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T01:06:46.651932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db4bb66e-0dc2-4ed1-a05c-b544317ec638 · inbound

Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation cites this paper.

Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation Revisiting the Calibration of Modern Neural Networks

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:14:07.203326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 69f74b07-8f63-436e-86f4-d278deae2686 · inbound

Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation cites this paper.

Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generation Revisiting the Calibration of Modern Neural Networks

Reference 181

Resolution: verified exact; arxiv_id, observed 2026-05-10T03:14:07.765935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 853c10f7-8615-445e-9f30-26f0545f30dd · inbound

Prior-Aligned Data Cleaning for Tabular Foundation Models cites this paper.

Prior-Aligned Data Cleaning for Tabular Foundation Models Revisiting the Calibration of Modern Neural Networks

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:31:17.011293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6aae96e9-6bee-4d5d-9b24-3b01fb710a8c · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Revisiting the Calibration of Modern Neural Networks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-09T20:37:32.192506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37bcb226-a4f4-4b01-8dda-91ce472043f2 · inbound

Diversity in Large Language Models under Supervised Fine-Tuning cites this paper.

Diversity in Large Language Models under Supervised Fine-Tuning Revisiting the Calibration of Modern Neural Networks

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:11:18.065386Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c1c77c0-c135-4404-98d6-a7f7abc9c85c · inbound

Toward Calibrated, Fair, and accurate Deepfake Detection cites this paper.

Toward Calibrated, Fair, and accurate Deepfake Detection Revisiting the Calibration of Modern Neural Networks

Reference 287

Resolution: verified exact; arxiv_id, observed 2026-06-28T07:11:45.286601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 294040a7-91f3-45d1-b2db-50a28674cf24 · inbound

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language cites this paper.

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language Revisiting the Calibration of Modern Neural Networks

Reference 210

Resolution: unresolved; no resolver link, observed 2026-07-11T23:16:58.545731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6777762c-ba2d-4bb7-8324-cb58b7f265f6 · inbound

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language cites this paper.

Consistent but Miscalibrated: Evaluating LLM Limitations for Risk Communication in Natural Language Revisiting the Calibration of Modern Neural Networks

Reference 210

Resolution: unresolved; no resolver link, observed 2026-07-13T07:02:13.140334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ee5db17-0091-4c77-ab77-80de8c6d3103 · inbound

Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers cites this paper.

Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers Revisiting the Calibration of Modern Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-14T04:52:20.985671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.