SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement
Recursive self-improvement is moving from theory to practice: modern systems can critique, revise, and evaluate their own outputs, yet iterative self-modification risks subtle alignment drift. We introduce SAHOO, a practical framework to monitor and control drift through three safeguards: (i) the Goal Drift Index (GDI), a learned multi-signal detector combining semantic, lexical, structural, and distributional measures; (ii) constraint preservation checks that enforce safety-critical invariants such as syntactic correctness and non-hallucination; and (iii) regression-risk quantification to flag improvement cycles that undo prior gains. Across 189 tasks in code generation, mathematical reasoning, and truthfulness, SAHOO produces substantial quality gains, including 18.3 percent improvement in code tasks and 16.8 percent in reasoning, while preserving constraints in two domains and maintaining low violations in truthfulness. Thresholds are calibrated on a small validation set of 18 tasks across three cycles. We further map the capability-alignment frontier, showing efficient early improvement cycles but rising alignment costs later and exposing domain-specific tensions such as fluency versus factuality. SAHOO therefore makes alignment preservation during recursive self-improvement measurable, deployable, and systematically validated at scale.
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Subramanyam Sahoo
claimed_verified
2026-03-13T08:14:41.169Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6651cadeb6fc2144ecccf035/UfHSrdbE2XbI-WeN3YbyN.jpeg
Subramanyam Sahoo
SahoobhAI
user
SahoobhAI
69b0c18b1f7902d47f6df922
Aman Chadha
claimed_verified
2026-03-11T08:44:27.326Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
amanchadha
user
amanchadha
69b0c18b1f7902d47f6df923
Vinija Jain
69b0c18b1f7902d47f6df924
Divya Chaudhary
SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement
Recursive self-improvement is moving from theory to practice: modern systems can critique, revise, and evaluate their own outputs, yet iterative self-modification risks subtle alignment drift. We introduce SAHOO, a practical framework to monitor and control drift through three safeguards: (i) the Goal Drift Index (GDI), a learned multi-signal detector combining semantic, lexical, structural, and distributional measures; (ii) constraint preservation checks that enforce safety-critical invariants such as syntactic correctness and non-hallucination; and (iii) regression-risk quantification to flag improvement cycles that undo prior gains. Across 189 tasks in code generation, mathematical reasoning, and truthfulness, SAHOO produces substantial quality gains, including 18.3 percent improvement in code tasks and 16.8 percent in reasoning, while preserving constraints in two domains and maintaining low violations in truthfulness. Thresholds are calibrated on a small validation set of 18 tasks across three cycles. We further map the capability-alignment frontier, showing efficient early improvement cycles but rising alignment costs later and exposing domain-specific tensions such as fluency versus factuality. SAHOO therefore makes alignment preservation during recursive self-improvement measurable, deployable, and systematically validated at scale.
SAHOO provides a framework for monitoring and controlling alignment drift in self-improving AI systems through goal drift detection, constraint preservation, and regression risk quantification across multiple domains.
recursive self-improvement alignment drift Goal Drift Index multi-signal detector constraint preservation regression-risk quantification code generation mathematical reasoning truthfulness capability-alignment frontier