Scarab Systems: AI Bug Fix With Diagnostic Layer Used 96% Fewer Decision Tokens

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Scarab Systems tested whether its Scarab Diagnostic Suite (SDS) could make AI-assisted software repair more context-efficient on a real public repository issue. The experiment targeted a known bug in xdg-desktop-portal-wlr where omitting an optional D-Bus field caused target selection to fail, fixed by a single-line code change. A cold OpenAI Codex baseline solved the problem correctly but consumed 72,194 tokens by reasoning across a large repository snapshot. The SDS-guided workflow reached the identical patch using a final decision context of just 2,651 tokens — roughly 96% fewer than the baseline input token count. The finding does not suggest Codex cannot solve such problems unaided, but highlights that a diagnostic governance layer can significantly reduce the contextual cost of reaching the same repair decision.

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