Adversarial Multi-AI Falsification in Research Workflows: A Bounded Case Study of Scope Error, Verification Failure, and Human Adjudication
Abstract
Version 2.3 of Adversarial Multi-AI Falsification is a bounded case study of AI-assisted scholarly workflow governance. It distinguishes selected public verbatim excerpts and message/line maps from author-reported recovery findings and hashes identifying internal artifacts. The public deposit does not contain the full captured transcripts or executed rubric.
Four process-failure types required exclusion or downgrading: semantic drift with unsupported process certification; assignment-scope expansion; prior-task substitution; and unverified or misdescribed legal citations. E2 remains partial. The principal execution/adjudication sequence remains author-reconstructed, and panel unanimity cannot be independently recomputed from the public deposit. The operational proposition remains untested; this revision does not establish it as true.
The paper proposes a three-field outcome record and six workflow controls, including a two-turn assignment-echo checkpoint. These are unvalidated design propositions, not demonstrated interventions. This is a case report and protocol demonstration, not a benchmark, product comparison, prevalence estimate or complete reproducibility study.
AMAF_v2_3.pdf is the current controlling manuscript, with AMAF_v2_3.docx as its editable counterpart. All seven v2.2 files are retained unchanged as historical records, including the old preparation-candidate manuscript, README, manifest and checksum list. Read 00_README_AMAF_v2_3_RELEASE.txt for the current controlling-version guide; AMAF_v2_3_SHA256SUMS.txt lists this release's file identities. The historical v1.8 paper is retained only for its Section C dual-track summary. No additional private records, new experiment or new empirical validation are released.
Generative AI systems were used for structured literature discovery, comparative issue mapping, adversarial review, source-check planning, language editing and revision support. They are not authors. The human author retains responsibility for the sources, interpretations, arguments and conclusions.
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BibTeX
@misc{yu2026adversarial,
title = {{Adversarial Multi-AI Falsification in Research Workflows: A Bounded Case Study of Scope Error, Verification Failure, and Human Adjudication}},
author = {QianJun Yu},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23489600},
url = {https://zenodo.org/records/23489600},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Adversarial Multi-AI Falsification in Research Workflows: A Bounded Case Study of Scope Error, Verification Failure, and Human Adjudication
AU - QianJun Yu
PY - 2026
DA - 2026/10/19
PB - Zenodo
DO - 10.5281/zenodo.23489600
UR - https://zenodo.org/records/23489600
AB - Version 2.3 of Adversarial Multi-AI Falsification is a bounded case study of AI-assisted scholarly workflow governance. It distinguishes selected public verbatim excerpts and message/line maps from author-reported recovery findings and hashes identifying internal artifacts. The public deposit does not contain the full captured transcripts or executed rubric. Four process-failure types required exclusion or downgrading: semantic drift with unsupported process certification; assignment-scope expansion; prior-task substitution; and unverified or misdescribed legal citations. E2 remains partial. The principal execution/adjudication sequence remains author-reconstructed, and panel unanimity cannot be independently recomputed from the public deposit. The operational proposition remains untested; this revision does not establish it as true. The paper proposes a three-field outcome record and six workflow controls, including a two-turn assignment-echo checkpoint. These are unvalidated design propositions, not demonstrated interventions. This is a case report and protocol demonstration, not a benchmark, product comparison, prevalence estimate or complete reproducibility study. AMAF_v2_3.pdf is the current controlling manuscript, with AMAF_v2_3.docx as its editable counterpart. All seven v2.2 files are retained unchanged as historical records, including the old preparation-candidate manuscript, README, manifest and checksum list. Read 00_README_AMAF_v2_3_RELEASE.txt for the current controlling-version guide; AMAF_v2_3_SHA256SUMS.txt lists this release's file identities. The historical v1.8 paper is retained only for its Section C dual-track summary. No additional private records, new experiment or new empirical validation are released. Generative AI systems were used for structured literature discovery, comparative issue mapping, adversarial review, source-check planning, language editing and revision support. They are not authors. The human author retains responsibility for the sources, interpretations, arguments and conclusions.
ER -