---
title: "One Verdict Is Not Enough: Regression Testing Learned World Models and Their Test Harnesses"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.41130v1"
permalink: "https://feed.astralor.com/items/itm_cb63894186c147afc15c9489"
published: "2026-10-30T01:36:37.000Z"
score: 55
channel: "ai"
tags: ["多模态","One Verdict","Not Enough","Regression Testing Learned","World Models","Their Test Harnesses","Learned","Work"]
---

# One Verdict Is Not Enough: Regression Testing Learned World Models and Their Test Harnesses

## 速览

Learned world models ship as software components whose model versions, preprocessing, and test harnesses change between releases. Work on model updates already counts per-sample regressions, or negative flips, that an a…

## 关键点

- Learned world models ship as software components whose model versions, preprocessing, and test harnesses change between releases.
- Work on model updates already counts per-sample regressions, or negative flips, that an accuracy check hides.
- Two problems remain: a test can pass under one configuration and fail under another, and a harness fault can produce erroneous scores and wrong verdicts without a crash.

## 全文

Learned world models ship as software components whose model versions, preprocessing, and test harnesses change between releases. Work on model updates already counts per-sample regressions, or negative flips, that an accuracy check hides. Two problems remain: a test can pass under one configuration and fail under another, and a harness fault can produce erroneous scores and wrong verdicts without a crash. We present TetraState, a regression-testing protocol for paired possible/impossible video tests. Each test runs under four fixed model-native configurations; its number of correct verdicts assigns one of four outcome classes. Inconclusive tests and failed executions are reported separately alongside the verdict rate. Revisions are compared by per-test outcome transitions, and the harness is versioned as part of the system under test. On all 506 IntPhys 2 Main tests and two heterogeneous systems, Cosmos 3 Nano and V-JEPA 2, two V-JEPA 2 harness revisions move modal accuracy by only 0.2 and 2.6 points yet change 11 and 214 test labels; in the second, accuracy rises although fewer tests are judged correctly, and 43 regressions keep the modal verdict, so a modal-verdict flip check misses them. These results outline a research agenda on configuration-level regression oracles for learned components.

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