When a Result Survives Its Explanation: A Source-Linked Dossier of Errors and Corrections in AI-Assisted Research

An erroneous research statement need not invalidate the calculation it describes. Conversely, an apparently correct result does not establish that its tests, explanation or readiness assessment were sound. This retrospective dossier examines twelve records from five related episodes in three projects of one research program. Nine records have documented artifact chains; three remain qualified as task interpretation, conditional model accounting or insufficient chronology evidence. The records include premature gate approval, ineffective numerical assertions, failure-signaling defects, an inappropriate numerical test, a reporting arithmetic error, unsupported effect and power inference, trajectory overgeneralization, transcript-level provenance contradiction and a specification/implementation mismatch. Several corrections left a primary negative result unchanged as historically reported. A report that prematurely passed a source-point requirement was followed by a separately recorded five-point completion. Corrections, recommendations and qualifications are distinguished. Cases were selected after findings were known; opportunities for error and a complete candidate pool were not enumerated. Codex assisted selection, adjudication and drafting and is finder of record for eight cases, while none attributes an original error to Codex. This asymmetric, exposed collection supports neither model rankings nor an estimated benefit from multi-AI review. The release includes the author-adopted R3 manuscript and a selected-evidence supplement with 73 entries from 26 retained source artifacts. Historical scientific outputs remain audit-reported; no new scientific execution is reported. AI tools assisted curation, drafting and advisory review; known exposure and curator-role asymmetry are disclosed. The author authorized publication on 1 November 2026. The PDF presents the wide case table as case records for readability, with the same text. Full internal source archives and raw scientific outputs are not included.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-01
DOI
https://doi.org/10.5281/zenodo.23867298
Primary Topic
Scientific Computing and Data Management
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

When a Result Survives Its Explanation: A Source-Linked Dossier of Errors and Corrections in AI-Assisted Research

QianJun Yu
3 links
Zenodo (CERN European Organization for Nuclear Research)
Scientific Computing and Data Management
preprint

When a Result Survives Its Explanation: A Source-Linked Dossier of Errors and Corrections in AI-Assisted Research

QianJun Yu
preprint en

Abstract

An erroneous research statement need not invalidate the calculation it describes. Conversely, an apparently correct result does not establish that its tests, explanation or readiness assessment were sound. This retrospective dossier examines twelve records from five related episodes in three projects of one research program. Nine records have documented artifact chains; three remain qualified as task interpretation, conditional model accounting or insufficient chronology evidence. The records include premature gate approval, ineffective numerical assertions, failure-signaling defects, an inappropriate numerical test, a reporting arithmetic error, unsupported effect and power inference, trajectory overgeneralization, transcript-level provenance contradiction and a specification/implementation mismatch. Several corrections left a primary negative result unchanged as historically reported. A report that prematurely passed a source-point requirement was followed by a separately recorded five-point completion. Corrections, recommendations and qualifications are distinguished. Cases were selected after findings were known; opportunities for error and a complete candidate pool were not enumerated. Codex assisted selection, adjudication and drafting and is finder of record for eight cases, while none attributes an original error to Codex. This asymmetric, exposed collection supports neither model rankings nor an estimated benefit from multi-AI review. The release includes the author-adopted R3 manuscript and a selected-evidence supplement with 73 entries from 26 retained source artifacts. Historical scientific outputs remain audit-reported; no new scientific execution is reported. AI tools assisted curation, drafting and advisory review; known exposure and curator-role asymmetry are disclosed. The author authorized publication on 1 November 2026. The PDF presents the wide case table as case records for readability, with the same text. Full internal source archives and raw scientific outputs are not included.

Zenodo (CERN European Organization for Nuclear Research)
Scientific Computing and Data Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
When a Result Survives Its Explanation: A Source-Linked Dossier of Errors and Corrections in AI-Assisted Research — QianJun Yu · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS