{"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/construct-graph","entry":"construct_graph","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":0,"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":0,"n_samples_fingerprinted":0,"n_places":9,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"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":"2410.22149","paper":"/paper/capacity-control-is-an-effective-memorization","title":"Capacity Control is an Effective Memorization Mitigation Mechanism in Text-Conditional Diffusion Models","date":"2024-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raman1121/diffusion_memorization_hpo","path":"extraction_attack.py","file_url":"https://github.com/raman1121/diffusion_memorization_hpo/blob/HEAD/extraction_attack.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"80b0901668ac3b4b","mcp_get_code":{"code_sha256":"80b0901668ac3b4b"}},{"arxiv_id":"2409.01143","paper":"/paper/flashflex-accommodating-large-language-model","title":"HexiScale: Accommodating Large Language Model Training over Heterogeneous Environment","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":"archive","repo":"relaxed-system-lab/flashflex","path":"experimental/graph.py","file_url":"https://github.com/relaxed-system-lab/flashflex/blob/HEAD/experimental/graph.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":"a25d59327ea592b2","mcp_get_code":{"code_sha256":"a25d59327ea592b2"}},{"arxiv_id":"2210.09338","paper":"/paper/deep-bidirectional-language-knowledge-graph","title":"Deep Bidirectional Language-Knowledge Graph Pretraining","date":"2022-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michiyasunaga/dragon","path":"preprocess_utils/conceptnet.py","file_url":"https://github.com/michiyasunaga/dragon/blob/HEAD/preprocess_utils/conceptnet.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":"be8c35c9c1d52b66","mcp_get_code":{"code_sha256":"be8c35c9c1d52b66"}},{"arxiv_id":"2104.06378","paper":"/paper/qa-gnn-reasoning-with-language-models-and","title":"QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering","date":"2021-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michiyasunaga/qagnn","path":"utils/conceptnet.py","file_url":"https://github.com/michiyasunaga/qagnn/blob/HEAD/utils/conceptnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be8c35c9c1d52b66","mcp_get_code":{"code_sha256":"be8c35c9c1d52b66"}},{"arxiv_id":"2010.11465","paper":"/paper/beta-embeddings-for-multi-hop-logical","title":"Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"snap-stanford/KGReasoning","path":"create_queries.py","file_url":"https://github.com/snap-stanford/KGReasoning/blob/HEAD/create_queries.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"75304c63d0f3ce0c","mcp_get_code":{"code_sha256":"75304c63d0f3ce0c"}},{"arxiv_id":"2010.03790","paper":"/paper/text-based-rl-agents-with-commonsense","title":"Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines","date":"2020-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/commonsense-rl","path":"utils/kg.py","file_url":"https://github.com/IBM/commonsense-rl/blob/HEAD/utils/kg.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":"a8fb47a8145651ed","mcp_get_code":{"code_sha256":"a8fb47a8145651ed"}},{"arxiv_id":"aaai_26578","paper":null,"title":"arXiv:aaai_26578","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mlvlab/QAT","path":"utils/conceptnet.py","file_url":"https://github.com/mlvlab/QAT/blob/HEAD/utils/conceptnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be8c35c9c1d52b66","mcp_get_code":{"code_sha256":"be8c35c9c1d52b66"}},{"arxiv_id":"2023.findings-emnlp.540","paper":null,"title":"arXiv:2023.findings-emnlp.540","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lingchen0331/KEEP","path":"preprocessing/conceptnet.py","file_url":"https://github.com/lingchen0331/KEEP/blob/HEAD/preprocessing/conceptnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be8c35c9c1d52b66","mcp_get_code":{"code_sha256":"be8c35c9c1d52b66"}},{"arxiv_id":"2022.findings-naacl.131","paper":null,"title":"arXiv:2022.findings-naacl.131","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"RUCAIBox/SAFE","path":"utils/conceptnet.py","file_url":"https://github.com/RUCAIBox/SAFE/blob/HEAD/utils/conceptnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be8c35c9c1d52b66","mcp_get_code":{"code_sha256":"be8c35c9c1d52b66"}}]}