---
title: "When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions — Stuff That Spins"
description: "arXiv:2607.03386v1 Announce Type: new Abstract: Agentic AI systems are increasingly used to edit, refine, and repair decision policies, but evaluating these ed…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:03:22.238823+00:00"
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# When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.03386  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.03386v1 Announce Type: new Abstract: Agentic AI systems are increasingly used to edit, refine, and repair decision policies, but evaluating these edits is difficult when per-state expert action labels are unavailable. We study this problem in a hotel-pricing simulator where an agentic policy editor receives only region-level diagnostic feedback: summaries of how its price distribution differs from a benchmark policy across time, inventory, and market regions. The editor cannot observe

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