Benchmark Designers Should "Train on the Test Set" to Expose Exploitable
  Non-Visual Shortcuts
Authors: name: Ellis Brown, name: Jihan Yang, name: Shusheng Yang, name: Rob Fergus, name: Saining Xie
Organization: VISIONx @ NYU

Robust benchmarks are crucial for evaluating Multimodal Large Language Models
(MLLMs). Yet we find that models can ace many multimodal benchmarks without
strong visual understanding, instead exploiting biases, linguistic priors, and
superficial patterns. This is especially problematic for vision-centric
benchmarks that are meant to require visual inputs. We adopt a diagnostic
principle for benchmark design: if a benchmark can be gamed, it will be.
Designers should therefore try to ``game'' their own benchmarks first, using
diagnostic and debiasing procedures to systematically identify and mitigate
non-visual biases. Effective diagnosis requires directly ``training on the test
set'' -- probing the released test set for its intrinsic, exploitable patterns.
  We operationalize this standard with two components. First, we diagnose
benchmark susceptibility using a ``Test-set Stress-Test'' (TsT) methodology.
Our primary diagnostic tool involves fine-tuning a powerful Large Language
Model via k-fold cross-validation on exclusively the non-visual, textual
inputs of the test set to reveal shortcut performance and assign each sample a
bias score s(x). We complement this with a lightweight Random Forest-based
diagnostic operating on hand-crafted features for fast, interpretable auditing.
Second, we debias benchmarks by filtering high-bias samples using an
``Iterative Bias Pruning'' (IBP) procedure. Applying this framework to four
benchmarks -- VSI-Bench, CV-Bench, MMMU, and VideoMME -- we uncover pervasive
non-visual biases. As a case study, we apply our full framework to create
VSI-Bench-Debiased, demonstrating reduced non-visual solvability and a wider
vision-blind performance gap than the original.