{"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/r","entry":"R","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":6,"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":8,"n_samples_ran":3,"n_samples_fingerprinted":3,"n_places":8,"n_places_pointer_only":1,"by_status":{"ran_honours":2,"ran_violates":1,"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":"2304.05293","paper":"/paper/equivariant-graph-neural-networks-for-charged","title":"Equivariant Graph Neural Networks for Charged Particle Tracking","date":"2023-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ameya1101/equivariant-tracking","path":"equivariance_test-IN.py","file_url":"https://github.com/ameya1101/equivariant-tracking/blob/HEAD/equivariance_test-IN.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":"a115a9638ba3c906","mcp_get_code":{"code_sha256":"a115a9638ba3c906"}},{"arxiv_id":"2110.07472","paper":"/paper/capacity-of-group-invariant-linear-readouts-1","title":"Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?","date":"2021-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"msf235/group-invariant-perceptron-capacity","path":"manifold_plots.py","file_url":"https://github.com/msf235/group-invariant-perceptron-capacity/blob/HEAD/manifold_plots.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce05c7ddf2e6b54e","mcp_get_code":{"code_sha256":"ce05c7ddf2e6b54e"}},{"arxiv_id":"2110.07472","paper":"/paper/capacity-of-group-invariant-linear-readouts-1","title":"Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?","date":"2021-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"msf235/group-invariant-perceptron-capacity","path":"manifold_plots.py","file_url":"https://github.com/msf235/group-invariant-perceptron-capacity/blob/HEAD/manifold_plots.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"625055d7f26bf4bd","mcp_get_code":{"code_sha256":"625055d7f26bf4bd"}},{"arxiv_id":"2106.03926","paper":"/paper/reconciling-rewards-with-predictive-state","title":"Reconciling Rewards with Predictive State Representations","date":"2021-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abaisero/rl-rpsr","path":"rl_rpsr/matrices.py","file_url":"https://github.com/abaisero/rl-rpsr/blob/HEAD/rl_rpsr/matrices.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce1deaebbda0c6ff","mcp_get_code":{"code_sha256":"ce1deaebbda0c6ff"}},{"arxiv_id":"2104.00569","paper":"/paper/learning-to-measure-adaptive-informationally","title":"Learning to Measure: Adaptive Informationally Complete Generalized Measurements for Quantum Algorithms","date":null,"month_inferred_from_arxiv_id":"2021-04","title_source":"archive","repo":"matteoacrossi/adapt_ic-povm","path":"tomography/likelihood_maximisation.py","file_url":"https://github.com/matteoacrossi/adapt_ic-povm/blob/HEAD/tomography/likelihood_maximisation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ae9022a4a7f9eb75","mcp_get_code":{"code_sha256":"ae9022a4a7f9eb75"}},{"arxiv_id":"1909.13846","paper":"/paper/universal-approximation-with-certified-1","title":"Universal Approximation with Certified Networks","date":"2019-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eth-sri/UniversalCertificationTheory","path":"PythonConstruction.py","file_url":"https://github.com/eth-sri/UniversalCertificationTheory/blob/HEAD/PythonConstruction.py","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"214493b0384505b4","mcp_get_code":{"code_sha256":"214493b0384505b4"}},{"arxiv_id":"1802.08219","paper":"/paper/tensor-field-networks-rotation-and","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","date":"2018-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luca310795/tensor-field-networks","path":"models/tfn/layers.py","file_url":"https://github.com/luca310795/tensor-field-networks/blob/HEAD/models/tfn/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ca77a472415f3cf6","mcp_get_code":{"code_sha256":"ca77a472415f3cf6"}},{"arxiv_id":"1802.08219","paper":"/paper/tensor-field-networks-rotation-and","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","date":"2018-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tensorfieldnetworks/tensorfieldnetworks","path":"tensorfieldnetworks/layers.py","file_url":"https://github.com/tensorfieldnetworks/tensorfieldnetworks/blob/HEAD/tensorfieldnetworks/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5f86ec36b0ab3b47","mcp_get_code":{"code_sha256":"5f86ec36b0ab3b47"}}]}