{"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/dynamic-evaluate","entry":"dynamic_evaluate","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":6,"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":3,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":8,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"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":"2402.00033","paper":"/paper/lf-vit-reducing-spatial-redundancy-in-vision","title":"LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition","date":"2024-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edgeai1/lf-vit","path":"dynamic_inference.py","file_url":"https://github.com/edgeai1/lf-vit/blob/HEAD/dynamic_inference.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8f49cc1ffec8b4b7","mcp_get_code":{"code_sha256":"8f49cc1ffec8b4b7"}},{"arxiv_id":"2203.03821","paper":"/paper/coarse-to-fine-vision-transformer","title":"CF-ViT: A General Coarse-to-Fine Method for Vision Transformer","date":"2022-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"8f49cc1ffec8b4b7","mcp_get_code":{"code_sha256":"8f49cc1ffec8b4b7"}},{"arxiv_id":"2201.03014","paper":"/paper/glance-and-focus-networks-for-dynamic-visual","title":"Glance and Focus Networks for Dynamic Visual Recognition","date":"2022-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"8f49cc1ffec8b4b7","mcp_get_code":{"code_sha256":"8f49cc1ffec8b4b7"}},{"arxiv_id":"2105.15075","paper":"/paper/not-all-images-are-worth-16x16-words-dynamic","title":"Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition","date":"2021-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"blackfeather-wang/Dynamic-Vision-Transformer","path":"inference.py","file_url":"https://github.com/blackfeather-wang/Dynamic-Vision-Transformer/blob/HEAD/inference.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8f49cc1ffec8b4b7","mcp_get_code":{"code_sha256":"8f49cc1ffec8b4b7"}},{"arxiv_id":"2104.09286","paper":"/paper/learning-to-cascade-confidence-calibration","title":"Learning to Cascade: Confidence Calibration for Improving the Accuracy and Computational Cost of Cascade Inference Systems","date":"2021-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"s-enmt/Learning_to_Cascade","path":"adaptive_inference.py","file_url":"https://github.com/s-enmt/Learning_to_Cascade/blob/HEAD/adaptive_inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a0bc69cf5bdf76ed","mcp_get_code":{"code_sha256":"a0bc69cf5bdf76ed"}},{"arxiv_id":"2103.16403","paper":"/paper/dynamic-domain-adaptation-for-efficient","title":"Dynamic Domain Adaptation for Efficient Inference","date":"2021-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BIT-DA/DDA","path":"adaptive_inference.py","file_url":"https://github.com/BIT-DA/DDA/blob/HEAD/adaptive_inference.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3f216bd2fda3e57e","mcp_get_code":{"code_sha256":"3f216bd2fda3e57e"}},{"arxiv_id":"2010.05300","paper":"/paper/glance-and-focus-a-dynamic-approach-to","title":"Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification","date":"2020-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"blackfeather-wang/gfnet-pytorch","path":"inference.py","file_url":"https://github.com/blackfeather-wang/gfnet-pytorch/blob/HEAD/inference.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8f49cc1ffec8b4b7","mcp_get_code":{"code_sha256":"8f49cc1ffec8b4b7"}},{"arxiv_id":"aaai_25860","paper":null,"title":"arXiv:aaai_25860","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ChenMnZ/CF-ViT","path":"dynamic_inference.py","file_url":"https://github.com/ChenMnZ/CF-ViT/blob/HEAD/dynamic_inference.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8f49cc1ffec8b4b7","mcp_get_code":{"code_sha256":"8f49cc1ffec8b4b7"}}]}