BMT-11.02 Executive Summary#
BlueMirror.tech | May 2026#
Dorothy Washington trusts her pharmacist’s competence. Nine years of accurate dispensing, proactive interaction warnings, and one direct call to her physician during a prescription conflict have earned that trust. She does not trust the pharmacy’s pricing. Generic medication prices that fluctuate by $15 across three months, brand-name alternatives pushed when generics are in stock, and automated refill reminders that feel like sales pressure. Dorothy’s trust is not a number. It is a structure with high scores in some dimensions and low scores in others. A system that collapses this into a single scalar, a “trust score” of 0.72, has produced a number that is wrong in every specific dimension and accidentally right only in aggregate.
Trust Vector Quantization models the person’s trust in external agents as a vector with sixteen or more dimensions grouped into four families. Competence dimensions measure whether the agent does its job well: dispensing accuracy, interaction flagging, refill timing. Integrity dimensions measure whether the agent behaves consistently with its stated purpose: does the pharmacy charge the price it quoted, does the insurance agent disclose all relevant terms. Benevolence dimensions measure whether the agent acts in the person’s interest when it could act in its own, the hardest dimension to observe because it is most visible in moments the system cannot directly see. Contextual dimensions capture trust variation across interaction types within the same agent relationship, maintaining separate assessments for medication management and pricing behavior rather than averaging across contexts.
TVQ quantizes the continuous vector into discrete tiers rather than operating on continuous scores, for the same reason the integration surface uses discrete tiers: gaming resistance. A continuous trust vector gives an adversarial agent a gradient to optimize against. Quantized tiers replace the gradient with gates. The quantization uses a minimum-across-critical-dimensions rule: the tier is set by the lowest score across dimensions designated as critical for that agent category. A pharmacy that scores high on dispensing accuracy but low on pricing transparency has its tier set by the pricing score. The compensation attack, where excellence in one dimension masks failure in another, is structurally prevented.
Trust is earned slowly and lost quickly, an asymmetry that is architectural. Positive interactions increase vector values incrementally, with diminishing returns. Negative interactions decrease values by a larger amount, and the decrease does not diminish with repetition. A pharmacy that fills 99 prescriptions correctly and prices one deceptively has revealed something about its pricing practices that the 99 correct fills did not reveal about its competence. Inactivity causes decay across all dimensions: an agent that has not interacted in 90 days may have changed ownership, pricing practices, or optimization objectives.
TVQ connects to the equity framework through a specific mechanism. If TVQ scoring systematically produces lower trust tiers for agents serving specific demographic populations, ISHI investigates whether the disparity reflects genuine agent behavior differences or scoring bias. If agents behave equivalently but scores differ, the scoring needs recalibration. If agents genuinely behave worse in underserved markets, the appropriate response is to improve agent quality rather than lower the trust bar, because adjusting scores to produce equitable distributions when agents genuinely behave worse would expose those subscribers to the exploitation TVQ is designed to prevent.
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