SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement

Researchers introduce SAHOO, a framework to prevent alignment drift in AI systems that recursively self-improve by monitoring goal changes, preserving constraints, and quantifying regression risks. The system achieved 18.3% improvement in code generation and 16.8% in reasoning tasks while maintaining safety constraints across 189 test scenarios.

• →SAHOO framework addresses the critical problem of alignment drift in self-improving AI systems through three key safeguards.
• →The Goal Drift Index (GDI) uses multiple signals to detect when AI systems deviate from their intended objectives during self-modification.
• →Testing across 189 tasks showed substantial quality gains of 18.3% in code generation and 16.8% in mathematical reasoning.
• →The framework successfully preserved safety constraints in two domains while maintaining low violation rates in truthfulness tasks.
• →Research reveals that early improvement cycles are efficient but alignment costs increase over time, exposing trade-offs between capabilities and safety.