{"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-main-process","entry":"is_main_process","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":9,"n_papers_ran":8,"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":8,"n_samples_ran":7,"n_samples_fingerprinted":1,"n_places":9,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":7,"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":"2606.29706","paper":"/paper/arxiv-2606-29706","title":"ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"heshandevaka/ARMOR","path":"retriever_training/train_armor.py","file_url":"https://github.com/heshandevaka/ARMOR/blob/HEAD/retriever_training/train_armor.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":"b8a27cf66abebb43","mcp_get_code":{"code_sha256":"b8a27cf66abebb43"}},{"arxiv_id":"2606.22627","paper":"/paper/arxiv-2606-22627","title":"Orthogonal Representation Editing: Decoupling Semantic Entanglement in Batch Knowledge Editing of LLMs","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"YVVH/ORE","path":"ORE/ORE_main.py","file_url":"https://github.com/YVVH/ORE/blob/HEAD/ORE/ORE_main.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f7b345fe9b853cc","mcp_get_code":{"code_sha256":"9f7b345fe9b853cc"}},{"arxiv_id":"2601.15014","paper":"/paper/arxiv-2601-15014","title":"Efficient and Minimax Optimal In-context Nonparametric Regression with Transformers","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"tianyima2000/ICL_LocPol","path":"ICL_locpol.py","file_url":"https://github.com/tianyima2000/ICL_LocPol/blob/HEAD/ICL_locpol.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6cb2ed4c2900417c","mcp_get_code":{"code_sha256":"6cb2ed4c2900417c"}},{"arxiv_id":"2405.06264","paper":"/paper/selective-focus-investigating-semantics","title":"Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection","date":"2024-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PannenetsF/SelectiveFocus","path":"pad/utils/runners/lane_det_quant_trainer.py","file_url":"https://github.com/PannenetsF/SelectiveFocus/blob/HEAD/pad/utils/runners/lane_det_quant_trainer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e4d71e90ee09ce61","mcp_get_code":{"code_sha256":"e4d71e90ee09ce61"}},{"arxiv_id":"2403.18913","paper":"/paper/unidepth-universal-monocular-metric-depth","title":"UniDepth: Universal Monocular Metric Depth Estimation","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lpiccinelli-eth/unidepth","path":"unidepth/models/unidepthv1/unidepthv1.py","file_url":"https://github.com/lpiccinelli-eth/unidepth/blob/HEAD/unidepth/models/unidepthv1/unidepthv1.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e17a1e0c67eaf7b8","mcp_get_code":{"code_sha256":"e17a1e0c67eaf7b8"}},{"arxiv_id":"2403.18684","paper":"/paper/scaling-laws-for-dense-retrieval","title":"Scaling Laws For Dense Retrieval","date":"2024-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingtaozhan/drscale","path":"training_arguments.py","file_url":"https://github.com/jingtaozhan/drscale/blob/HEAD/training_arguments.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7cbdad4c2ba7d4ec","mcp_get_code":{"code_sha256":"7cbdad4c2ba7d4ec"}},{"arxiv_id":"2309.15639","paper":"/paper/enhancing-sharpness-aware-optimization","title":"Enhancing Sharpness-Aware Optimization Through Variance Suppression","date":"2023-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bingcongli/vasso","path":"utils/dist.py","file_url":"https://github.com/bingcongli/vasso/blob/HEAD/utils/dist.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5045fff9712deacb","mcp_get_code":{"code_sha256":"5045fff9712deacb"}},{"arxiv_id":"2309.14888","paper":"/paper/nearest-neighbor-guidance-for-out-of-1","title":"Nearest Neighbor Guidance for Out-of-Distribution Detection","date":"2023-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingkang50/openood","path":"openood/postprocessors/nnguide_postprocessor.py","file_url":"https://github.com/jingkang50/openood/blob/HEAD/openood/postprocessors/nnguide_postprocessor.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3305c78c72a2232b","mcp_get_code":{"code_sha256":"3305c78c72a2232b"}},{"arxiv_id":"2204.00185","paper":"/paper/distill-vq-learning-retrieval-oriented-vector","title":"Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings","date":"2022-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"staoxiao/libvq","path":"LibVQ/utils.py","file_url":"https://github.com/staoxiao/libvq/blob/HEAD/LibVQ/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7cbdad4c2ba7d4ec","mcp_get_code":{"code_sha256":"7cbdad4c2ba7d4ec"}}]}