{"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/is-square","entry":"is_square","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":2,"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":6,"n_samples_ran":2,"n_samples_fingerprinted":1,"n_places":8,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":1,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":4},"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":"2404.05729","paper":"/paper/finding-visual-task-vectors","title":"Finding Visual Task Vectors","date":"2024-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alhojel/visual_task_vectors","path":"evaluate/evaluate_reasoning.py","file_url":"https://github.com/alhojel/visual_task_vectors/blob/HEAD/evaluate/evaluate_reasoning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ce2513917f2cc4b5","mcp_get_code":{"code_sha256":"ce2513917f2cc4b5"}},{"arxiv_id":"2403.05645","paper":"/paper/geometric-neural-network-based-on-phase-space","title":"Geometric Neural Network based on Phase Space for BCI-EEG decoding","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"carraraig/Phase-SPDNet","path":"PhaseSPDnet/Connectivities.py","file_url":"https://github.com/carraraig/Phase-SPDNet/blob/HEAD/PhaseSPDnet/Connectivities.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5f6a9fa7cdbfdb27","mcp_get_code":{"code_sha256":"5f6a9fa7cdbfdb27"}},{"arxiv_id":"2311.03648","paper":"/paper/instruct-me-more-random-prompting-for-visual","title":"Instruct Me More! Random Prompting for Visual In-Context Learning","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jackieam/inmemo","path":"evaluate/evaluate_reasoning.py","file_url":"https://github.com/jackieam/inmemo/blob/HEAD/evaluate/evaluate_reasoning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ce2513917f2cc4b5","mcp_get_code":{"code_sha256":"ce2513917f2cc4b5"}},{"arxiv_id":"2303.10834","paper":"/paper/object-centric-slot-diffusion-1","title":"Object-Centric Slot Diffusion","date":"2023-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jindongjiang/latent-slot-diffusion","path":"src/models/slot_attn.py","file_url":"https://github.com/jindongjiang/latent-slot-diffusion/blob/HEAD/src/models/slot_attn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c348f6b1a3946d1b","mcp_get_code":{"code_sha256":"c348f6b1a3946d1b"}},{"arxiv_id":"2301.13670","paper":"/paper/what-makes-good-examples-for-visual-in-1","title":"What Makes Good Examples for Visual In-Context Learning?","date":"2023-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangyuanhan-ai/visual_prompt_retrieval","path":"evaluate/evaluate_reasoning.py","file_url":"https://github.com/zhangyuanhan-ai/visual_prompt_retrieval/blob/HEAD/evaluate/evaluate_reasoning.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"ce2513917f2cc4b5","mcp_get_code":{"code_sha256":"ce2513917f2cc4b5"}},{"arxiv_id":"2205.14334","paper":"/paper/teaching-models-to-express-their-uncertainty","title":"Teaching Models to Express Their Uncertainty in Words","date":"2022-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sylinrl/calibratedmath","path":"dataset.py","file_url":"https://github.com/sylinrl/calibratedmath/blob/HEAD/dataset.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"81e3d6e064a6f6aa","mcp_get_code":{"code_sha256":"81e3d6e064a6f6aa"}},{"arxiv_id":"1912.04958","paper":"/paper/analyzing-and-improving-the-image-quality-of","title":"Analyzing and Improving the Image Quality of StyleGAN","date":"2019-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kerryvernebegeman/Kerry-Verne-Begeman","path":"train_resnet.py","file_url":"https://github.com/kerryvernebegeman/Kerry-Verne-Begeman/blob/HEAD/train_resnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a691acff20d5a466","mcp_get_code":{"code_sha256":"a691acff20d5a466"}},{"arxiv_id":"1906.07538","paper":"/paper/locate-size-and-count-accurately-resolving","title":"Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection","date":"2019-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"val-iisc/lsc-cnn","path":"utils/nms.py","file_url":"https://github.com/val-iisc/lsc-cnn/blob/HEAD/utils/nms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"925f23b10b193d5d","mcp_get_code":{"code_sha256":"925f23b10b193d5d"}}]}