{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/disk","entry":"disk","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":8,"n_papers_ran":7,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":2,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":1},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2606.16996","paper":"/paper/arxiv-2606-16996","title":"ActiveSAM: Image-Conditional Class Pruning for Fast and Accurate Open-Vocabulary Segmentation","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"VILA-Lab/ActiveSAM","path":"activesam/imagecorruptions/corruptions.py","file_url":"https://github.com/VILA-Lab/ActiveSAM/blob/HEAD/activesam/imagecorruptions/corruptions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc8b7f1d3348c560","mcp_get_code":{"code_sha256":"bc8b7f1d3348c560"}},{"arxiv_id":"2508.12082","paper":"/paper/arxiv-2508-12082","title":"Automated Model Evaluation for Object Detection via Prediction Consistency and Reliability","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"YonseiML/autoeval-det","path":"imagenet_c/corruptions.py","file_url":"https://github.com/YonseiML/autoeval-det/blob/HEAD/imagenet_c/corruptions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc8b7f1d3348c560","mcp_get_code":{"code_sha256":"bc8b7f1d3348c560"}},{"arxiv_id":"2311.14837","paper":"/paper/benchmarking-robustness-of-text-image","title":"Benchmarking Robustness of Text-Image Composed Retrieval","date":"2023-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"suntongtongtong/benchmark-robustness-text-image-compose-retrieval","path":"corrupt/utils.py","file_url":"https://github.com/suntongtongtong/benchmark-robustness-text-image-compose-retrieval/blob/HEAD/corrupt/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc8b7f1d3348c560","mcp_get_code":{"code_sha256":"bc8b7f1d3348c560"}},{"arxiv_id":"2211.15937","paper":"/paper/robustness-disparities-in-face-detection","title":"Robustness Disparities in Face Detection","date":"2022-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dooleys/Robustness-Disparities-in-Commercial-Face-Detection","path":"code/imagenet_c_big/corruptions.py","file_url":"https://github.com/dooleys/Robustness-Disparities-in-Commercial-Face-Detection/blob/HEAD/code/imagenet_c_big/corruptions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bc8b7f1d3348c560","mcp_get_code":{"code_sha256":"bc8b7f1d3348c560"}},{"arxiv_id":"2211.02578","paper":"/paper/data-models-for-dataset-drift-controls-in","title":"Data Models for Dataset Drift Controls in Machine Learning With Optical Images","date":"2022-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aiaudit-org/raw2logit","path":"utils/hendrycks_robustness.py","file_url":"https://github.com/aiaudit-org/raw2logit/blob/HEAD/utils/hendrycks_robustness.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc8b7f1d3348c560","mcp_get_code":{"code_sha256":"bc8b7f1d3348c560"}},{"arxiv_id":"2207.13916","paper":"/paper/a-novel-data-augmentation-technique-for-out","title":"A Novel Data Augmentation Technique for Out-of-Distribution Sample Detection using Compounded Corruptions","date":"2022-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cnc-ood/cnc_ood","path":"augmentations/corruptions.py","file_url":"https://github.com/cnc-ood/cnc_ood/blob/HEAD/augmentations/corruptions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc8b7f1d3348c560","mcp_get_code":{"code_sha256":"bc8b7f1d3348c560"}},{"arxiv_id":"2110.13771","paper":"/paper/augmax-adversarial-composition-of-random","title":"AugMax: Adversarial Composition of Random Augmentations for Robust Training","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/AugMax","path":"augmax_modules/corruptions_IN.py","file_url":"https://github.com/VITA-Group/AugMax/blob/HEAD/augmax_modules/corruptions_IN.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc8b7f1d3348c560","mcp_get_code":{"code_sha256":"bc8b7f1d3348c560"}},{"arxiv_id":"1906.02337","paper":"/paper/mnist-c-a-robustness-benchmark-for-computer","title":"MNIST-C: A Robustness Benchmark for Computer Vision","date":"2019-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"testingautomated-usi/fashion-mnist-c","path":"mnist_c.py","file_url":"https://github.com/testingautomated-usi/fashion-mnist-c/blob/HEAD/mnist_c.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"07a9b58ec8d910eb","mcp_get_code":{"code_sha256":"07a9b58ec8d910eb"}}]}