Governed Reasoning for Institutional AI
Summary: arXiv:2604.10658v1 Announce Type: new
Abstract: Institutional decisions — regulatory compliance, clinical triage, prior authorization appeal — require a different AI architecture than general-purpose agents provide. Agent frameworks infer authority conversationally, reconstruct accountability from logs, and produce silent errors: incorrect determinations that execute without any human review signal. We propose Cognitive Core: a governed decision substrate built from nine typed cognitive primitives (retrieve, classify, investigate, verify, challenge, reflect, deliberate, govern, generate), a four-tier governance model where human review is a condition of execution rather than a post-hoc check, a tamper-evident SHA-256 hash-chain audit ledger endogenous to computation, and a demand-driven delegation architecture supporting both declared and autonomously reasoned epistemic sequences.
Introduction
As artificial intelligence continues to evolve, the need for specialized systems capable of handling institutional decision-making processes has become increasingly clear. Traditional AI frameworks often fall short when it comes to regulatory compliance and clinical decision-making due to their reliance on general-purpose architectures. This article introduces a new paradigm for institutional AI through the Cognitive Core framework.
The Cognitive Core Framework
The Cognitive Core represents a significant shift in how AI can be applied to institutional decision-making. It consists of:
- Nine Cognitive Primitives: These include retrieve, classify, investigate, verify, challenge, reflect, deliberate, govern, and generate. Each primitive serves a distinct function in the decision-making process.
- Four-Tier Governance Model: This model ensures that human review is integrated into the execution process, rather than merely serving as a post-hoc check.
- Tamper-Evident Audit Ledger: Utilizing a SHA-256 hash-chain, this ledger provides transparency and accountability, making it impossible to alter records without detection.
- Demand-Driven Delegation Architecture: This architecture supports both predefined and autonomously reasoned decisions, allowing for flexibility in various institutional contexts.
Benchmarking Performance
To evaluate the effectiveness of the Cognitive Core, a benchmarking exercise was conducted using an 11-case balanced prior authorization appeal evaluation set. The results were striking:
- Accuracy: Cognitive Core achieved an impressive 91% accuracy, significantly outperforming its competitors, ReAct (55%) and Plan-and-Solve (45%).
- Governance and Error Rates: Notably, Cognitive Core produced zero silent errors, while both baseline systems generated between 5 to 6 silent errors. This underscores the importance of governability in institutional AI systems.
Introducing Governability
Governability is introduced as a central evaluation metric for institutional AI. It refers to how reliably a system can identify when it should refrain from taking autonomous actions. This concept is vital alongside traditional measures of accuracy, as it addresses the critical issue of silent errors that can lead to significant consequences in institutional settings.
Conclusion
The Cognitive Core framework represents a groundbreaking advancement in the field of institutional AI. By integrating a structured governance model with advanced cognitive primitives, it offers a robust solution for complex decision-making environments. As organizations seek to implement AI systems for regulatory compliance and clinical decision-making, frameworks like Cognitive Core provide a pathway to ensure accountability and accuracy.