{"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/get-autocast","entry":"get_autocast","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":5,"n_papers_ran":0,"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":0,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":2},"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":"2507.12998","paper":null,"title":"arXiv:2507.12998","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"MediaBrain-SJTU/DISSect","path":"src/precision.py","file_url":"https://github.com/MediaBrain-SJTU/DISSect/blob/HEAD/src/precision.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"94a4076103662faf","mcp_get_code":{"code_sha256":"94a4076103662faf"}},{"arxiv_id":"2412.08580","paper":"/paper/laion-sg-an-enhanced-large-scale-dataset-for","title":"LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural Annotations","date":"2024-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mengcye/LAION-SG","path":"sgEncoderTraining/training/precision.py","file_url":"https://github.com/mengcye/LAION-SG/blob/HEAD/sgEncoderTraining/training/precision.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f988e73e85f4ea4a","mcp_get_code":{"code_sha256":"f988e73e85f4ea4a"}},{"arxiv_id":"2409.01936","paper":"/paper/optimizing-clip-models-for-image-retrieval","title":"Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment","date":"2024-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Visual-Computing/MCIP","path":"MCIP/src/realignment/precision.py","file_url":"https://github.com/Visual-Computing/MCIP/blob/HEAD/MCIP/src/realignment/precision.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"94a4076103662faf","mcp_get_code":{"code_sha256":"94a4076103662faf"}},{"arxiv_id":"2405.08815","paper":"/paper/efficient-vision-language-pre-training-by","title":"Efficient Vision-Language Pre-training by Cluster Masking","date":"2024-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zi-hao-wei/efficient-vision-language-pre-training-by-cluster-masking","path":"training/precision.py","file_url":"https://github.com/zi-hao-wei/efficient-vision-language-pre-training-by-cluster-masking/blob/HEAD/training/precision.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"94a4076103662faf","mcp_get_code":{"code_sha256":"94a4076103662faf"}},{"arxiv_id":"2311.04219","paper":"/paper/otterhd-a-high-resolution-multi-modality","title":"OtterHD: A High-Resolution Multi-modality Model","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luodian/otter","path":"pipeline/benchmarks/public_datasets_suite/models/otter.py","file_url":"https://github.com/luodian/otter/blob/HEAD/pipeline/benchmarks/public_datasets_suite/models/otter.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"94a4076103662faf","mcp_get_code":{"code_sha256":"94a4076103662faf"}}]}