Confusing Large Models by Confusing Small Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite a steady growth in average accuracy, computer vision models continue to fail on many robustness benchmarks. In this paper, we take a step back from standard benchmarks and focus on how models perceive data, and which aspects of the data they find confusing. Using an ensemble-based confusion score we examine how the training and test samples appear simple or confusing to a given model. Based on these heuristics, we demonstrate an application of the confusion score in identifying images that appear confusing to the trained model, and show that these images are highly likely to be misclassified by the model. We further demonstrate how confusion carries over to models of various sizes and architectures, which gives rise to the possibility of identifying challenging images via ensembles of small networks to produce a custom benchmark of challenging data, that remains appropriate for large models where ensembling is costly to implement. Finally, we demonstrate how training via upsampling on confusing images can improve accuracy on the hard subset.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it