Research on judgment, AI governance, reasoning and interdisciplinary discovery. Every paper is linked to its permanent DOI record.
Selected collection
Six papers with direct relevance to judgment and executive responsibility.
The selection foregrounds practical frameworks for adoption, governance, reasoning, multi-agent assurance and research exploration.
01Governance · 2026
Judgment Net Gain Framework: A Pre-Adoption Framework for Deciding Whether an AI System Should Be Incorporated into an Organisation
A practical, AI-positive and anti-abdication framework for deciding whether an AI system should be adopted before it becomes embedded in workflows, policies, procurement decisions or institutional habits. Its core test is simple: will this AI system leave human judgment stronger than it found it?
Governance as Ceiling and Accelerator: The TenthDan AI Framework for Institutional AI Discipline
The TenthDan framework treats governance not as a brake on AI adoption but as the discipline that simultaneously bounds risk and unlocks institutional speed.
Judgment.dev: A Judicially Inspired Governance Framework for Autonomous AI Systems
A governance layer that treats every consequential AI-agent decision as a reviewable case — with facts, applicable rules, comparable prior cases, reasons, accountable human authority, an appeal route and post-case learning. Auditability without judgement is not yet accountability.
InnerSapience: Restoring the Reasoning Journey in the Age of Automated Answers
An AI-positive, proportionate post-answer scaffold for calibrated epistemic agency. InnerSapience helps users inspect, contextualise and take responsibility for AI-supported conclusions without turning every use of AI into a long exercise — so AI removes effort without silently removing judgement.
Agentic Choir: A Protocol and Proof-of-Concept for Coordination-Level Assurance in Multi-Agent AI Systems
A protocol for making declared multi-agent coordination behaviour inspectable, rehearsable and receiptable. It names the gap False Harmony — a final output that looks coherent while hiding missed escalation, role failure, authority breach or suppressed dissent — and introduces Choir Receipts as tamper-evident records of what was declared, tested and submitted.
The Large Research Collider: A Distance-First Application to Scholarly Knowledge
Extends the Vlogcano framework to scholarly discovery. Instead of searching for what is closest and most similar, the Collider searches outward for Maximum Viable Distance — the furthest intellectual distance at which a research collision retains an explicit bridge, sufficient evidence, and enough stability to form a coherent research opening. Deterministic by design, with inspectable collision receipts and a voluntary Collision Commons.