PAC-2026
A machine-readable AI publication accountability protocol that defines a Publication Authority mechanism with precise state-binding, non-transferable, and single-use properties. It constrains the publication of AI-assisted conclusions through six mutually non-compensable obligations, ensuring that conclusions remain independently challengeable and auditable.
Timeline (last 90 days)
PAC-2026 (Publication-Accountability Calculus) is a machine-readable AIJIM protocol candidate.
The paper focuses on evaluating the fourth Bounded Semantic Freeze (SF-4) of PAC-2026, which is a fixed configuration specification designed for substitutable bindings.
Across the ten models in the study, a total of 110,764 safely reachable states were explored.
One historical predecessor path reproduced 17 frozen authorization-successor outcomes, instance blind tests matched all 183 scoring expectations, and software packages reproduced 240 archived observation results.
The research results support four conclusions: internal consistency, bounded safety, failure sensitivity, and limited constructability.
The paper explicitly states that its results do not support factual truth, generalized refinement, blind interoperability, field effectiveness, or standards status.
PAC-2026 addresses whether a publication action can be independently and falsifiably challenged, rather than the correctness of AI conclusion content itself.
All Facts (7)
PAC-2026 (Publication-Accountability Calculus) is a machine-readable AIJIM protocol candidate.
50%UnverifiedThe paper focuses on evaluating the fourth Bounded Semantic Freeze (SF-4) of PAC-2026, which is a fixed configuration specification designed for substitutable bindings.
50%UnverifiedAcross the ten models in the study, a total of 110,764 safely reachable states were explored.
50%UnverifiedOne historical predecessor path reproduced 17 frozen authorization-successor outcomes, instance blind tests matched all 183 scoring expectations, and software packages reproduced 240 archived observation results.
50%UnverifiedThe research results support four conclusions: internal consistency, bounded safety, failure sensitivity, and limited constructability.
50%UnverifiedThe paper explicitly states that its results do not support factual truth, generalized refinement, blind interoperability, field effectiveness, or standards status.
50%UnverifiedPAC-2026 addresses whether a publication action can be independently and falsifiably challenged, rather than the correctness of AI conclusion content itself.
50%