{"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-metadata","entry":"get_metadata","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":14,"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":13,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":14,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":11},"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":"2608.15073","paper":"/paper/arxiv-2608-15073","title":"BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"rosenyu304/BOCoDe","path":"bocode/registry.py","file_url":"https://github.com/rosenyu304/BOCoDe/blob/HEAD/bocode/registry.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"762a6243458641cf","mcp_get_code":{"code_sha256":"762a6243458641cf"}},{"arxiv_id":"2604.15385","paper":"/paper/arxiv-2604-15385","title":"Prompt-Driven Code Summarization: A Systematic Literature Review","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"afia2023/prompt-engineering","path":"Prompt-Engineering_SLR/Filter/Springerlink_dataextractor.py","file_url":"https://github.com/afia2023/prompt-engineering/blob/HEAD/Prompt-Engineering_SLR/Filter/Springerlink_dataextractor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5af9b426bfc06adc","mcp_get_code":{"code_sha256":"5af9b426bfc06adc"}},{"arxiv_id":"2604.06559","paper":"/paper/arxiv-2604-06559","title":"ExplainFuzz: Explainable and Constraint-Conditioned Test Generation with Probabilistic Circuits","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"niMgnoeSeeL/ExplainFuzz","path":"xml_concretizer/xsd_to_metadata.py","file_url":"https://github.com/niMgnoeSeeL/ExplainFuzz/blob/HEAD/xml_concretizer/xsd_to_metadata.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9fdddd73b7e0b2e4","mcp_get_code":{"code_sha256":"9fdddd73b7e0b2e4"}},{"arxiv_id":"2507.22264","paper":null,"title":"arXiv:2507.22264","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"LAION-AI/CLIP_benchmark","path":"clip_benchmark/datasets/objectnet.py","file_url":"https://github.com/LAION-AI/CLIP_benchmark/blob/HEAD/clip_benchmark/datasets/objectnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f4eee6ad42516616","mcp_get_code":{"code_sha256":"f4eee6ad42516616"}},{"arxiv_id":"2412.00568","paper":"/paper/the-well-a-large-scale-collection-of-diverse","title":"The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning","date":"2024-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lanl/nubhlight","path":"script/analysis/accumulate_skynet_mass_fractions.py","file_url":"https://github.com/lanl/nubhlight/blob/HEAD/script/analysis/accumulate_skynet_mass_fractions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"c6c8065f319c5d7f","mcp_get_code":{"code_sha256":"c6c8065f319c5d7f"}},{"arxiv_id":"2401.03140","paper":"/paper/fair-sampling-in-diffusion-models-through","title":"Fair Sampling in Diffusion Models through Switching Mechanism","date":"2024-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uzn36/attributeswitching","path":"data.py","file_url":"https://github.com/uzn36/attributeswitching/blob/HEAD/data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f006efc85bb2817a","mcp_get_code":{"code_sha256":"f006efc85bb2817a"}},{"arxiv_id":"2310.16802","paper":"/paper/from-molecules-to-materials-pre-training","title":"From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction","date":"2023-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/JMP","path":"src/jmp/modules/metadata.py","file_url":"https://github.com/facebookresearch/JMP/blob/HEAD/src/jmp/modules/metadata.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6331fa6b3252f6e8","mcp_get_code":{"code_sha256":"6331fa6b3252f6e8"}},{"arxiv_id":"2309.01859","paper":"/paper/nllb-clip-train-performant-multilingual-image","title":"NLLB-CLIP -- train performant multilingual image retrieval model on a budget","date":"2023-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eify/clip_benchmark","path":"clip_benchmark/datasets/objectnet.py","file_url":"https://github.com/eify/clip_benchmark/blob/HEAD/clip_benchmark/datasets/objectnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f4eee6ad42516616","mcp_get_code":{"code_sha256":"f4eee6ad42516616"}},{"arxiv_id":"2303.04143","paper":"/paper/can-we-scale-transformers-to-predict","title":"Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models?","date":"2023-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samsungsailmontreal/ghn3","path":"ghn3/nn.py","file_url":"https://github.com/samsungsailmontreal/ghn3/blob/HEAD/ghn3/nn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31d6226bb81368e0","mcp_get_code":{"code_sha256":"31d6226bb81368e0"}},{"arxiv_id":"2302.04907","paper":"/paper/binarized-neural-machine-translation-1","title":"Binarized Neural Machine Translation","date":"2023-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google/aqt","path":"aqt/common/emulation_utils.py","file_url":"https://github.com/google/aqt/blob/HEAD/aqt/common/emulation_utils.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":"ee238f6213016698","mcp_get_code":{"code_sha256":"ee238f6213016698"}},{"arxiv_id":"2210.14868","paper":"/paper/multi-lingual-evaluation-of-code-generation","title":"Multi-lingual Evaluation of Code Generation Models","date":"2022-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-science/mxeval","path":"mxeval/data.py","file_url":"https://github.com/amazon-science/mxeval/blob/HEAD/mxeval/data.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":"c5bac9f25975c7c5","mcp_get_code":{"code_sha256":"c5bac9f25975c7c5"}},{"arxiv_id":"2109.12072","paper":"/paper/sd-qa-spoken-dialectal-question-answering-for","title":"SD-QA: Spoken Dialectal Question Answering for the Real World","date":"2021-09-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ffaisal93/sd-qa","path":"experiments/utils.py","file_url":"https://github.com/ffaisal93/sd-qa/blob/HEAD/experiments/utils.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":"d60c3869c082ce69","mcp_get_code":{"code_sha256":"d60c3869c082ce69"}},{"arxiv_id":"2109.08133","paper":"/paper/phrase-retrieval-learns-passage-retrieval-too","title":"Phrase Retrieval Learns Passage Retrieval, Too","date":"2021-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-nlp/DensePhrases","path":"densephrases/utils/embed_utils.py","file_url":"https://github.com/princeton-nlp/DensePhrases/blob/HEAD/densephrases/utils/embed_utils.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":"3149bf27eadf82a3","mcp_get_code":{"code_sha256":"3149bf27eadf82a3"}},{"arxiv_id":"2106.09708","paper":"/paper/multi-label-learning-from-single-positive","title":"Multi-Label Learning from Single Positive Labels","date":"2021-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elijahcole/single-positive-multi-label","path":"datasets.py","file_url":"https://github.com/elijahcole/single-positive-multi-label/blob/HEAD/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1255b14983c0b14e","mcp_get_code":{"code_sha256":"1255b14983c0b14e"}}]}