# QuoteBench: Matched Scores Can Hide Command-Path Failures

> Source: <https://arxiv.org/abs/2608.13547>
> Published: 2026-08-15 13:51:29+00:00

# Computer Science > Artificial Intelligence

[Submitted on 13 Aug 2026]

# Title:QuoteBench: How Matched Scores Can Hide Command-Path Failures

[View PDF](/pdf/2608.13547)

[HTML (experimental)](https://arxiv.org/html/2608.13547v1)

Abstract:LLM coding agents issue Bash commands through interfaces that may serialize, wrap, and reparse model output. Matched execution scores alone cannot distinguish command-generation errors from failures introduced after generation. QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families, crossing the generation contract with the execution transport around one deliberately unescaped added parser. Escaping at the interpolation point reproduces each replayed reply's raw-path outcome, so any recovery under a disclosed boundary must come from the model changing its generation. Across eight same-window configurations, replaying the same reply through the added parser lowers success by 55.4 to 73.2 percentage points; disclosure recovers 30.4 to 60.7 points for six configurations, and zero or slightly negative for the other two. Raw generation is nearly saturated at the frontier; boundary adaptation is what still separates models. GPT-5.6-sol's matched gap of -3.6 points hides -64.3 points of damage and +60.7 points of compensation. The deployment configuration reorders models: one reversal among 26 comparable pairs is unambiguous and four more sit on single-task margins. Evaluations of command-issuing agents should report the model configuration, generation contract, execution path, operating point, and final-state validator rather than treat a matched score as an intrinsic model property.

### References & Citations

Loading...

# Bibliographic and Citation Tools

Bibliographic Explorer

*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))
Connected Papers

*(*[What is Connected Papers?](https://www.connectedpapers.com/about))
Litmaps

*(*[What is Litmaps?](https://www.litmaps.co/))
scite Smart Citations

*(*[What are Smart Citations?](https://www.scite.ai/))# Code, Data and Media Associated with this Article

alphaXiv

*(*[What is alphaXiv?](https://alphaxiv.org/))
CatalyzeX Code Finder for Papers

*(*[What is CatalyzeX?](https://www.catalyzex.com))
DagsHub

*(*[What is DagsHub?](https://dagshub.com/))
Gotit.pub

*(*[What is GotitPub?](http://gotit.pub/faq))
Hugging Face

*(*[What is Huggingface?](https://huggingface.co/huggingface))
ScienceCast

*(*[What is ScienceCast?](https://sciencecast.org/welcome))# Demos

# Recommenders and Search Tools

Influence Flower

*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))
CORE Recommender

*(*[What is CORE?](https://core.ac.uk/services/recommender))# arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? [ Learn more about arXivLabs](https://info.arxiv.org/labs/index.html).