Measuring What Persists: Conditioning Mechanisms and a Geometric Framework for AI Agent Identity

Researchers present a geometric framework using magnitude homology to measure and detect AI agent identity drift in long-context applications. The study identifies two conditioning mechanisms explaining how identity specifications influence agent behavior, validates the framework empirically, and reveals that observed drift patterns reflect padding artifacts rather than genuine context-length degradation.

This research addresses a fundamental challenge in deploying persistent AI agents: maintaining behavioral consistency with specified identity parameters over extended interactions. Traditional approaches only detect identity drift after qualitative degradation becomes apparent, creating a gap in proactive monitoring. The geometric framework using square-root Jensen-Shannon divergence and magnitude homology from enriched category theory represents a novel approach to quantifying identity structure as non-geodesic patterns in behavioral space.

The empirical validation on persistent agents yields sophisticated mechanistic insights. The discovery of two distinct conditioning clusters—an identity-vacuum region where specifications fill behavioral gaps and a safety-basin region where they displace post-training attractors—suggests identity operates through fundamentally different mechanisms depending on baseline model characteristics. The equilateral probe baseline demonstrating 55 unique response patterns versus 1 for the base model provides quantifiable evidence that identity specifications measurably enrich behavioral complexity.

The perturbation theory framework offers predictive power, showing that magnitude changes follow from perimeter changes alone while symmetry cancels shape perturbations at first order. However, the drift experiment revealed a critical methodological finding: apparent magnitude decreases resulted from repetitive padding artifacts rather than genuine context degradation, with diverse padding producing no measurable deformation across 150K tokens.

This work matters for AI reliability and safety engineering. Successfully detecting identity drift before qualitative failures occur enables proactive intervention in production systems. The framework's architectural grounding suggests potential for detecting structural collapse through homological simplification, though this diagnostic capability remains empirically unconfirmed. Future work should focus on validating anisotropic contraction detection and extending analysis across diverse model architectures.

• →A magnitude homology framework quantifies AI agent identity drift through geometric structure in behavioral spaces
• →Two distinct conditioning mechanisms explain how identity specifications influence agent behavior in complementary ways
• →Identity creates measurable behavioral richness with 55 unique response patterns versus baseline's single pattern
• →Observed drift patterns in long-context settings reflect padding artifacts rather than genuine context-length degradation
• →Framework remains promising for detecting structural collapse but requires empirical confirmation of homological simplification predictions