Verified Synthesis: From Agent Fan-Out to One Accountable Result

Organizations that dispatch one bounded request to several AI capacity providers receive back a set of outputs that partially agree, materially conflict, omit different evidence, and sometimes fail outright. This paper examines why possession of that fan-out set is not the same thing as possession of an accountable result, and it proposes — as unproven, author-owned design — a minimum verified-synthesis contract intended to close that gap. The proposed contract requires correlation-complete inputs; triage of generated content into load-bearing and supporting claims carrying provenance, evidence class, and locator; contradiction-first, bounded-depth verification against a coverage rule with a frozen denominator; explicit treatment of material contradiction, disagreement, uncertainty, missingness, and minority evidence as five distinct states; forced abstention under named conditions; bounded escalation that decays to abstention; consumption of a pre-committed protected reserve before any separately authorized release; and exactly two terminal outputs — an accountable result or a principled abstention — each preserving lineage, unresolved states, and limitations. The contract inherits authority from ARBITER, budget from PRETIUM, and execution returns from NIMBUS, and it hands its terminal state to the suite capstone without defining clearing. A fair counter-thesis is stated and preserved: published aggregation, judging, calibration, and logging practice may already be adequate at lower cost. The thesis is presented with explicit falsification and narrowing conditions, and nothing in this paper asserts implementation, adoption, superiority, or measured performance.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-08-02
DOI
https://doi.org/10.5281/zenodo.21709667
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
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article

Verified Synthesis: From Agent Fan-Out to One Accountable Result

Justin H. Kuiper
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
article

Verified Synthesis: From Agent Fan-Out to One Accountable Result

Justin H. Kuiper
article en

Abstract

Organizations that dispatch one bounded request to several AI capacity providers receive back a set of outputs that partially agree, materially conflict, omit different evidence, and sometimes fail outright. This paper examines why possession of that fan-out set is not the same thing as possession of an accountable result, and it proposes — as unproven, author-owned design — a minimum verified-synthesis contract intended to close that gap. The proposed contract requires correlation-complete inputs; triage of generated content into load-bearing and supporting claims carrying provenance, evidence class, and locator; contradiction-first, bounded-depth verification against a coverage rule with a frozen denominator; explicit treatment of material contradiction, disagreement, uncertainty, missingness, and minority evidence as five distinct states; forced abstention under named conditions; bounded escalation that decays to abstention; consumption of a pre-committed protected reserve before any separately authorized release; and exactly two terminal outputs — an accountable result or a principled abstention — each preserving lineage, unresolved states, and limitations. The contract inherits authority from ARBITER, budget from PRETIUM, and execution returns from NIMBUS, and it hands its terminal state to the suite capstone without defining clearing. A fair counter-thesis is stated and preserved: published aggregation, judging, calibration, and logging practice may already be adequate at lower cost. The thesis is presented with explicit falsification and narrowing conditions, and nothing in this paper asserts implementation, adoption, superiority, or measured performance.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 7%
Ethics and Social Impacts of AI
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