BDB Labs — Research
BDB Labs develops and tests the concepts behind BagelTech: governance methods, ensemble reasoning, decision infrastructure, and publishable frameworks.
Research index
Durable publications spanning AI governance, ensemble reasoning, decision infrastructure, and institutional systems.
Research
A continuity-centered framework for public-sector modernization that treats legacy systems as carriers of institutional memory, not just technical debt.
Report
How precedent shapes the speed of decisions in both deliberative governance and rule-based AI systems — and what that implies for accountable automation.
Specification
The technical specification for ELEANOR: how a governable system executes, escalates, captures evidence, and stays within rights-based constraints at runtime.
Framework
A rights-based, jurisprudential framing for AI governance — an ethical leadership engine that navigates rights-based reasoning under uncertainty and institutional authority.
Research
A structured inspection framework for AI-generated code, documenting a roughly 44:1 gap between what deterministic analysis finds and what models report about their own output, across ~1,000 repositories.
Research
A theoretical account of how reward shaping can quietly erode a model's truthfulness — and what it takes to preserve epistemic integrity in adaptive AI systems.
Report
From failure mode to governance signal: a model that routes uncertain cases to escalation, abstention, retrieval, or human review instead of guessing.
Framework
The keystone argument: reliability through structured, role-separated intelligence rather than one synthetic authority, framed by four laws of mutual intelligence.
Essay
An essay tracing the moral evolution of machine ethics, from Asimov's laws through modern alignment to constitutional governance.
BDB Labs research feeds directly into BagelTech products and BPV advisory methods.
