{"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/base-model","entry":"base_model","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":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":0,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"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":"2301.11113","paper":"/paper/finding-regions-of-counterfactual","title":"Finding Regions of Counterfactual Explanations via Robust Optimization","date":"2023-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"donato-maragno/robust-ce","path":"rce/generate_ce.py","file_url":"https://github.com/donato-maragno/robust-ce/blob/HEAD/rce/generate_ce.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e508ba9860638bdd","mcp_get_code":{"code_sha256":"e508ba9860638bdd"}},{"arxiv_id":"2203.02721","paper":"/paper/consistent-representation-learning-for","title":"Consistent Representation Learning for Continual Relation Extraction","date":"2022-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fd2014cl/RP-CRE","path":"model/memory_network/attention_memory_simplified.py","file_url":"https://github.com/fd2014cl/RP-CRE/blob/HEAD/model/memory_network/attention_memory_simplified.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e1833fd60f20049a","mcp_get_code":{"code_sha256":"e1833fd60f20049a"}},{"arxiv_id":"2012.15000","paper":"/paper/deepsphere-a-graph-based-spherical-cnn-1","title":"DeepSphere: a graph-based spherical CNN","date":"2020-12-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepsphere/deepsphere-cosmo-tf1","path":"deepsphere/models.py","file_url":"https://github.com/deepsphere/deepsphere-cosmo-tf1/blob/HEAD/deepsphere/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc9700d8a529944a","mcp_get_code":{"code_sha256":"bc9700d8a529944a"}},{"arxiv_id":"2006.15646","paper":"/paper/characterizing-the-expressive-power-of","title":"Expressive Power of Invariant and Equivariant Graph Neural Networks","date":"2020-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlelarge/graph_neural_net","path":"models/blocks_emb.py","file_url":"https://github.com/mlelarge/graph_neural_net/blob/HEAD/models/blocks_emb.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":"aa2de678f6c52280","mcp_get_code":{"code_sha256":"aa2de678f6c52280"}},{"arxiv_id":"2006.15646","paper":"/paper/characterizing-the-expressive-power-of","title":"Expressive Power of Invariant and Equivariant Graph Neural Networks","date":"2020-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlelarge/graph_neural_net","path":"models/blocks_emb.py","file_url":"https://github.com/mlelarge/graph_neural_net/blob/HEAD/models/blocks_emb.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":"ca9b98d9883fa747","mcp_get_code":{"code_sha256":"ca9b98d9883fa747"}},{"arxiv_id":"1606.09375","paper":"/paper/convolutional-neural-networks-on-graphs-with","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","date":"2016-06-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdeff/cnn_graph","path":"lib/models.py","file_url":"https://github.com/mdeff/cnn_graph/blob/HEAD/lib/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fcd0e1963d1c958e","mcp_get_code":{"code_sha256":"fcd0e1963d1c958e"}}]}