{"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/build-blocks","entry":"build_blocks","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":3,"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":5,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":1,"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":"2404.10518","paper":"/paper/mobilenetv4-universal-models-for-the-mobile","title":"MobileNetV4 -- Universal Models for the Mobile Ecosystem","date":"2024-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jaiwei98/MobileNetV4-pytorch","path":"mobilenet/mobilenetv4.py","file_url":"https://github.com/jaiwei98/MobileNetV4-pytorch/blob/HEAD/mobilenet/mobilenetv4.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a7ca97f43224d68c","mcp_get_code":{"code_sha256":"a7ca97f43224d68c"}},{"arxiv_id":"2310.19961","paper":"/paper/expt-synthetic-pretraining-for-few-shot-1","title":"ExPT: Synthetic Pretraining for Few-Shot Experimental Design","date":"2023-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tung-nd/ExPT","path":"model/vae.py","file_url":"https://github.com/tung-nd/ExPT/blob/HEAD/model/vae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0726d07e3884f498","mcp_get_code":{"code_sha256":"0726d07e3884f498"}},{"arxiv_id":"2303.01233","paper":"/paper/domain-aware-triplet-loss-in-domain","title":"Domain-aware Triplet loss in Domain Generalization","date":"2023-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"workerbcd/dct","path":"domainbed/backbones/new_networks.py","file_url":"https://github.com/workerbcd/dct/blob/HEAD/domainbed/backbones/new_networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"83e280af851073dd","mcp_get_code":{"code_sha256":"83e280af851073dd"}},{"arxiv_id":"1811.11431","paper":"/paper/espnetv2-a-light-weight-power-efficient-and","title":"ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network","date":"2018-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zh320/realtime-semantic-segmentation-pytorch","path":"models/espnetv2.py","file_url":"https://github.com/zh320/realtime-semantic-segmentation-pytorch/blob/HEAD/models/espnetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"43f8cfd6793d23f0","mcp_get_code":{"code_sha256":"43f8cfd6793d23f0"}},{"arxiv_id":"1811.08201","paper":"/paper/cgnet-a-light-weight-context-guided-network","title":"CGNet: A Light-weight Context Guided Network for Semantic Segmentation","date":"2018-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zh320/realtime-semantic-segmentation-pytorch","path":"models/cgnet.py","file_url":"https://github.com/zh320/realtime-semantic-segmentation-pytorch/blob/HEAD/models/cgnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5a62e086416203cb","mcp_get_code":{"code_sha256":"5a62e086416203cb"}}]}