[2608.13547] QuoteBench: How Matched Scores Can Hide Command-Path Failures

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arXiv:2608.13547 (cs)

[Submitted on 13 Aug 2026]

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

Authors:Shangao Li, Yao Zhang, Volker Tresp, Yuanyuan Yang<br>View a PDF of the paper titled QuoteBench: How Matched Scores Can Hide Command-Path Failures, by Shangao Li and 3 other authors

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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.

Comments:<br>29 pages, 5 figures. Project page: this https URL

Subjects:

Artificial Intelligence (cs.AI); Software Engineering (cs.SE)

Cite as:<br>arXiv:2608.13547 [cs.AI]

(or<br>arXiv:2608.13547v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.13547

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Shangao Li [view email]<br>[v1]<br>Thu, 13 Aug 2026 17:57:20 UTC (283 KB)

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