--- license: cc-by-4.0 pretty_name: Droplet-impact videos for "One Perturbation Is Not Enough" tags: - video - physics - vision-language-models - evaluation --- # Droplet-impact videos High-speed recordings of liquid droplets falling onto a superhydrophobic surface, used in **"One Perturbation Is Not Enough: Identifiability and Blind Baselines for Behavioral AI Evaluation"** (AAAI-27 Alignment Track) to test whether vision-language models ground a velocity estimate in an in-frame calibration reference. Code, per-item model outputs and the reconciliation script: **https://github.com/rasulkhanbayov/5-iclr** ## Layout ``` raw_videos/ 02182026/ evaluation session (all model experiments) *.mp4 one droplet per recording, 1280x512, ~2996 fps scale v.mp4 static scale-reference (ruler) recording samples.txt sample key (surfactant / concentration codes) 13.5cm_speed/05122026/ (optional) higher-drop session; oracle only derived/ clips rendered from raw_videos/ by the code repo fall_clips/ fall+impact window of each of the 27 evaluated clips trimmed_scale_clips/ 3 s scale-reference clips (base, relabeled, resampled) perturbed_clips/ relabeled scale clips (R_alpha, alpha in {0.5, 2}) resampled_clips/ resampled scale and fall clips (S_s, s in {0.5, 2}) ``` ## Key facts - Ruler in session 02182026 is graduated in **1/16 inch (1.5875 mm) per tick**, not 1 mm; see the paper's Appendix B.2. - Ground-truth pre-impact velocities come from a classical CV oracle (background subtraction + free-fall-plateau fit), not a learned model. Per-clip values are in the code repo's `results/e2_selected_clips.json`. - The 27 evaluated clips are every high-confidence oracle track in 02182026. ## Download ```bash git clone https://github.com/rasulkhanbayov/5-iclr.git && cd 5-iclr python scripts/download_data.py # derived clips (~150 MB) python scripts/download_data.py --raw # + raw 02182026 session (~4.4 GB) ``` ## Citation ```bibtex @inproceedings{khanbayov2027perturbation, title = {One Perturbation Is Not Enough: Identifiability and Blind Baselines for Behavioral AI Evaluation}, author = {Khanbayov, Rasul and Sohail, Mariam and Abdala, Ahmed and Kurban, Hasan}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (Alignment Track)}, year = {2027}, } ```