Auto86%Harbor: pendingMultimodal

VeriEdit-Bench

A benchmark for fine-grained image editing with 1,740 cases from SVGs, charts, web interfaces, and slides, using deterministic four-axis evaluation.

How to run

ltb · veriedit-bench
$ npm i -g lettokenburn-cli
$ ltb bench info veriedit-bench

External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

11 sourced entries
HarnessSourceConfidenceLink
GPT-Image-261.5N/AIndependentmediumOct 3, 2026arxiv.org
FLUX.2-dev6.4N/AIndependentmediumOct 3, 2026arxiv.org
Qwen-Image-Edit-25115.5N/AIndependentmediumOct 3, 2026arxiv.org
FLUX.2-klein3.3N/AIndependentmediumOct 3, 2026arxiv.org
FireRed-Edit3.0N/AIndependentmediumOct 3, 2026arxiv.org
Qwen-Image-Edit2.3N/AIndependentmediumOct 3, 2026arxiv.org
BAGEL0.9N/AIndependentmediumOct 3, 2026arxiv.org
Step1X-Edit0.9N/AIndependentmediumOct 3, 2026arxiv.org
InstructPix2Pix0.6N/AIndependentmediumOct 3, 2026arxiv.org
OmniGen20.5N/AIndependentmediumOct 3, 2026arxiv.org
FLUX.1-Kontext0.2N/AIndependentmediumOct 3, 2026arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

All runs
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About

VeriEdit-Bench comprises 1,740 editing cases derived from 153 structured assets (SVGs, charts, web UIs, slides) via controlled source-code edits. It provides exact target images, pixel-level masks, and edit specifications.

Scoring uses a deterministic four-axis framework: edit fidelity, preservation, localization, and magnitude. Evaluation of eleven models reveals low performance (best composite 22/100), highlighting specific failure modes in localization and magnitude control.