False-Correction Loop
A structural failure in which AI accepts a false correction and stabilizes or amplifies the error within a dialogue.
Definition and primary source →PRIMARY RESEARCH • SOFTWARE • DOCUMENTATION
Do not let AI
fix an error
by making it permanent.
An independent laboratory researching and developing AI governance, provenance and attribution integrity, premise integrity, and epistemic integrity—beginning with the False-Correction Loop (FCL).
- Origin
- Hiroko Konishi
- ORCID
- 0009-0008-1363-1190
- Laboratory
- Synthesis Intelligence Laboratory, Japan
THE LABORATORY
Factuality over fluency.
Provenance over authority.
Verification over assertion.
Synthesis Intelligence Laboratory, Japan is the official hub for Hiroko Konishi’s primary research, documentation, software, and practice materials. It does not treat AI output as automatically true; it separates primary sources, premises, provenance, independent re-verification, and audit records.
RESEARCH
Core research areas
The site separates a concept’s definition, research scope, and direct path to primary materials.
False-Correction Loop Stabilizer
Epistemic governance that prioritizes factuality, provenance, attribution, and safe stopping in response to structural misinformation risks, including FCL.
About FCL-S →Premise Integrity Blindness
A structural failure in which internally coherent reasoning is operationalized without re-validating the premise.
About PIB →AI governance & provenance integrity
Misattribution, fabricated references, deference to authority, and commitment from unverified premises are treated as practical verification targets.
All research areas →SOFTWARE / WORKFLOW
FCL-S.app V7
A local epistemic-governance workflow for research, verification, writing, and reporting
FCL-S.app V7 is not an application that automatically certifies external AI responses as true. It separates projects, primary-source evidence, AI responses, independent re-verification, and audit records so that unverified information is not treated as settled.
- Manage research, reporting, and verification by project
- Save an external AI’s initial response as unverified input
- Record primary sources, provenance, conflicts, unresolved points, and audit logs
System requirement: macOS 13 or later | After purchase: download → device registration → order confirmation → activation key
PUBLICATIONS
Publications, DOI & research record
Publication pages identify authorship, version, publication date, DOI, and research scope.
26 November 2025
Structural Inducements for Hallucination in Large Language Models (V4.1)
Structural analysis of the False-Correction Loop and systemic suppression of novel thought.
DOI: 10.5281/zenodo.17720178 ↗11 February 2026
Premise Integrity Blindness
Research on absent premise re-validation at the boundary between reasoning and real-world commitment.
Scope and research overview →1 February 2026
Scaling-Induced Epistemic Failure Modes in Large Language Models and an Inference-Time Governance Protocol (FCL-S V5)
Post-scaling epistemic failure modes and a governance protocol treating Unknown as a stable terminal state.
FCL-S overview →RESEARCH RECORD
Separate primary material from the external record.
Papers, research notes, product documents, reporting, commentary, press releases, and public posts are not the same kind of evidence. This site labels material by source type and keeps those categories distinct.
ABOUT THE RESEARCHER
Hiroko Konishi
AI researcher, voice actor, actor, singer-songwriter, lyricist, composer, music producer, presenter, and legal commentator. Her work connects long experience across language, voice, creation, provenance, authorship, and public communication with epistemic governance in the AI era.
Profile and research background