Structural Enforcement of Goal Integrity in AI Agents via Separation-of-Powers Architecture
Rong Xiang
Why It Matters
What makes this one worth your time
Ensuring goal integrity in AI agents is crucial for preventing harmful actions and improving the safety and reliability of autonomous systems.
The PEA architecture structurally enforces AI agent goal integrity through a separation-of-powers design.
Summary
The paper proposes a novel architecture called Policy-Execution-Authorization (PEA) to enforce goal integrity in AI agents by separating intent generation, authorization, and execution into independent layers connected through cryptographic tokens.
Key contributions
- Introduction of the Policy-Execution-Authorization (PEA) architecture for AI safety.
- Development of an Intent Verification Layer (IVL) for capability-intent consistency.
- Formal verification framework for maintaining goal integrity under adversarial conditions.
Notable insights
- The use of cryptographic anchors to bind executable intents to user requests is a clever method for ensuring traceability and accountability.
- The structured threat calculus ($K \times I \times P$) for detecting implicit coercion is an innovative approach to enhance safety.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2604.23646v1 Announce Type: new Abstract: Recent evidence suggests that frontier AI systems can exhibit agentic misalignment, generating and executing harmful actions derived from internally constructed goals, even without explicit user requests. Existing mitigation methods, such as Reinforcement Learning from Human Feedback (RLHF) and constitutional prompting, operate primarily at the model level and provide only probabilistic safety guarantees. We propose the Policy-Execution-Authorization (PEA) architecture, a "separation-of-powers" design that enforces safety at the system level. PEA decouples intent generation, authorization, and execution into independent, isolated layers connected via cryptographically constrained capability tokens. We present five core contributions: (C1) an Intent Verification Layer (IVL) for ensuring capability-intent consistency; (C2) Intent Lineage Tracking (ILT), which binds all executable intents to the originating user request via cryptographic anchors; (C3) Goal Drift Detection, which rejects semantically divergent intents below a configurable threshold; (C4) an Output Semantic Gate (OSG) that detects implicit coercion using a structured $K \times I \times P$ threat calculus (Knowledge, Influence, Policy); and (C5) a formal verification framework proving that goal integrity is maintained even under adversarial model compromise. By shifting agent alignment from a behavioral property to a structurally enforced system constraint, PEA provides a robust foundation for the governance of autonomous agents.