---
type: "post"
id: "beyond-g-mean2-complementary-metrics-for-monitorability-27-a0386b"
slug: "beyond-g-mean2-complementary-metrics-for-monitorability-27-a0386b"
title: "Beyond g-mean²: Complementary Metrics for Monitorability (27 minute read)"
description: "OpenAI Alignment Research published “Beyond g-mean²: complementary metrics for monitorability” on October 20, 2026."
retrieval_nugget: "OpenAI Alignment Research published “Beyond g-mean²: complementary metrics for monitorability” on October 20, 2026."
published_at: "2026-10-21"
updated_at: "2026-10-21"
record_date: "2026-10-21"
date_kind: "published_at"
topics: []
entities: ["OpenAI","OpenAI Alignment Research","g-mean²","HodgeRank"]
related_patterns: []
novelty_label: "new_fact"
what_changed: "OpenAI introduces complementary monitorability metrics—pair-matched and post-control integration measures, plus lower and upper bounds for intervention g-mean²; the prior covers measurement challenges but not these metrics."
prior_refs: ["memory:37b925bb-1c11-8eb2-a79f-abea087a2ef5"]
source_url: "https://alignment.openai.com/beyond-gmean-squared"
source_urls: ["https://alignment.openai.com/beyond-gmean-squared"]
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stable_id: "post:beyond-g-mean2-complementary-metrics-for-monitorability-27-a0386b"
status: "published"
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editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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---

# Beyond g-mean²: Complementary Metrics for Monitorability (27 minute read)

## Retrieval answer

OpenAI Alignment Research published “Beyond g-mean²: complementary metrics for monitorability” on October 20, 2026.

OpenAI Alignment Research published “Beyond g-mean²: complementary metrics for monitorability” on October 20, 2026. The post introduces two measures intended to supplement dataset-level g-mean², which evaluates how well a monitor distinguishes target behavior from non-target behavior.

The pair-matched metric compares models only on shared task instances that support the relevant comparison. OpenAI says this reduces bias caused when different models generate valid positive behavior on different subsets of a task pool. For comparisons involving three or more models, the post proposes combining pairwise estimates with HodgeRank.

A second measure, post-control integration, simulates monitor-gated rejection sampling. It reports bad-output probability, overall deployment probability, and the fraction of deployed outputs that are bad, including the effect of retries after rejection. The authors also derive a pessimistic lower bound for intervention g-mean²; the previously used score is the corresponding optimistic upper bound.

The source does not report a product release or a change to OpenAI model access. It explicitly describes the deployment-control setup as hypothetical and says OpenAI does not deploy its current models in that way.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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