RTS: Git-Native Execution Provenance Protocol for AI Decisions

RTS (Reconstructable Transition System) is an experimental research protocol designed to provide structural execution provenance for AI systems. It addresses the challenge of understanding and defending AI decisions by preserving the execution itself, not just the output. RTS captures context, decisions, assumptions, constraints, and outcomes as structural state transitions, enabling reconstructable and auditable AI execution. The protocol generates structural artifacts such as Session Ledgers, Monthly Indexes, and Evidence Snapshots (ESCs), which provide a verifiable chain of evidence for each AI decision. RTS detects structural mutation and flags breakpoints when mutation density crosses a defined boundary, allowing for targeted analysis of critical decision points. Its Git-native design simplifies integration and leverages existing workflows, making it easier for developers to adopt and manage AI execution provenance. However, RTS's narrow focus on structural reconstructability may not address ethical, legal, or policy concerns related to AI decisions. Semantic guarantees require additional systems, potentially increasing complexity and cost. RTS guarantees structural reconstructability of AI execution states. This guarantee is intentionally narrow. RTS does not validate ethics, legality, policy correctness, or output quality. Semantic guarantees require additional systems. RTS provides the structural base layer.

Transparency is essential for building trust in AI systems. RTS's approach to execution provenance enhances accountability and auditability, but it's crucial to understand the limitations of the protocol. Users should be aware that RTS does not provide semantic guarantees and that additional systems may be required to address ethical, legal, and policy concerns. This transparency fosters informed decision-making and promotes responsible AI development.

RTS's design aligns with the principles of responsible AI development by prioritizing accountability and auditability. By enabling users to reconstruct and defend AI decisions, it promotes transparency and reduces the risk of unintended consequences. This approach is essential for building trust in AI systems and ensuring their responsible deployment.