{"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/power","entry":"power","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":16,"n_papers_ran":14,"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":11,"n_samples_fingerprinted":9,"n_places":16,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":6,"ran_fixture":0,"ran":5,"unverified":2},"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":"2411.04165","paper":"/paper/bio-xlstm-generative-modeling-representation","title":"Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences","date":"2024-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-jku/chem-xlstm","path":"chemxlstm/module_library/krylov.py","file_url":"https://github.com/ml-jku/chem-xlstm/blob/HEAD/chemxlstm/module_library/krylov.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":"5df4043c60e49613","mcp_get_code":{"code_sha256":"5df4043c60e49613"}},{"arxiv_id":"2409.03797","paper":"/paper/nestful-a-benchmark-for-evaluating-llms-on","title":"NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls","date":"2024-09-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/nestful","path":"data_v2/executable_functions/basic_functions.py","file_url":"https://github.com/ibm/nestful/blob/HEAD/data_v2/executable_functions/basic_functions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"29da7d03953ecf87","mcp_get_code":{"code_sha256":"29da7d03953ecf87"}},{"arxiv_id":"2408.12296","paper":"/paper/multiple-testing-for-signal-agnostic-searches","title":"Multiple testing for signal-agnostic searches of new physics with machine learning","date":"2024-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mletizia/multiple-testing-nplm","path":"tests.py","file_url":"https://github.com/mletizia/multiple-testing-nplm/blob/HEAD/tests.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"10b09496d9fcea23","mcp_get_code":{"code_sha256":"10b09496d9fcea23"}},{"arxiv_id":"2408.09051","paper":"/paper/ai-assisted-super-resolution-cosmological-3","title":"AI-assisted super-resolution cosmological simulations IV: An emulator for deterministic realizations","date":null,"month_inferred_from_arxiv_id":"2024-08","title_source":"archive","repo":"xwzhang98/SREmulator","path":"map2map/map2map/models/power.py","file_url":"https://github.com/xwzhang98/SREmulator/blob/HEAD/map2map/map2map/models/power.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"314049a86e92dfe1","mcp_get_code":{"code_sha256":"314049a86e92dfe1"}},{"arxiv_id":"2407.08751","paper":"/paper/latent-diffusion-for-neural-spiking-data","title":"Latent Diffusion for Neural Spiking Data","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mackelab/LDNS","path":"ldns/networks/blocks.py","file_url":"https://github.com/mackelab/LDNS/blob/HEAD/ldns/networks/blocks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e0326e9fa719a59","mcp_get_code":{"code_sha256":"8e0326e9fa719a59"}},{"arxiv_id":"2404.07177","paper":"/paper/scaling-laws-for-data-filtering-data-curation","title":"Scaling Laws for Data Filtering -- Data Curation cannot be Compute Agnostic","date":"2024-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"locuslab/scaling_laws_data_filtering","path":"estimate_best_pool.py","file_url":"https://github.com/locuslab/scaling_laws_data_filtering/blob/HEAD/estimate_best_pool.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d1520b6908d5986f","mcp_get_code":{"code_sha256":"d1520b6908d5986f"}},{"arxiv_id":"2403.10648","paper":"/paper/debiasing-with-diffusion-probabilistic","title":"Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies with CAMELS","date":null,"month_inferred_from_arxiv_id":"2024-03","title_source":"archive","repo":"victoriaono/variational-diffusion-cdm","path":"model/utils/utils.py","file_url":"https://github.com/victoriaono/variational-diffusion-cdm/blob/HEAD/model/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"93a9ed79fb993ed3","mcp_get_code":{"code_sha256":"93a9ed79fb993ed3"}},{"arxiv_id":"2311.08558","paper":"/paper/probabilistic-reconstruction-of-dark-matter","title":"Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models","date":"2023-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cfpark00/vdm4cdm","path":"src/utils.py","file_url":"https://github.com/cfpark00/vdm4cdm/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"93a9ed79fb993ed3","mcp_get_code":{"code_sha256":"93a9ed79fb993ed3"}},{"arxiv_id":"2305.12585","paper":"/paper/geometricimagenet-extending-convolutional","title":"GeometricImageNet: Extending convolutional neural networks to vector and tensor images","date":"2023-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wilsongregory/geometricconvolutions","path":"src/ginjax/utils.py","file_url":"https://github.com/wilsongregory/geometricconvolutions/blob/HEAD/src/ginjax/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"273ecb316e34892d","mcp_get_code":{"code_sha256":"273ecb316e34892d"}},{"arxiv_id":"2212.14052","paper":"/paper/hungry-hungry-hippos-towards-language","title":"Hungry Hungry Hippos: Towards Language Modeling with State Space Models","date":"2022-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hazyresearch/safari","path":"src/models/sequence/h3.py","file_url":"https://github.com/hazyresearch/safari/blob/HEAD/src/models/sequence/h3.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"55a38587423268a5","mcp_get_code":{"code_sha256":"55a38587423268a5"}},{"arxiv_id":"2212.08136","paper":"/paper/efficient-long-sequence-modeling-via-state","title":"Efficient Long Sequence Modeling via State Space Augmented Transformer","date":"2022-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/efficientlongsequencemodeling","path":"spade-modules/s4.py","file_url":"https://github.com/microsoft/efficientlongsequencemodeling/blob/HEAD/spade-modules/s4.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"348275db42a37bee","mcp_get_code":{"code_sha256":"348275db42a37bee"}},{"arxiv_id":"2204.01692","paper":"/paper/long-movie-clip-classification-with-state","title":"Long Movie Clip Classification with State-Space Video Models","date":"2022-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"md-mohaiminul/ViS4mer","path":"models.py","file_url":"https://github.com/md-mohaiminul/ViS4mer/blob/HEAD/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fae8dcc7964cac77","mcp_get_code":{"code_sha256":"fae8dcc7964cac77"}},{"arxiv_id":"2111.00396","paper":"/paper/efficiently-modeling-long-sequences-with-1","title":"Efficiently Modeling Long Sequences with Structured State Spaces","date":"2021-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"elgazzarr/fmri-s4","path":"src/models/sequence/ss/standalone/s4.py","file_url":"https://github.com/elgazzarr/fmri-s4/blob/HEAD/src/models/sequence/ss/standalone/s4.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"348275db42a37bee","mcp_get_code":{"code_sha256":"348275db42a37bee"}},{"arxiv_id":"2009.09761","paper":"/paper/diffwave-a-versatile-diffusion-model-for","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis","date":"2020-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"albertfgu/diffwave-sashimi","path":"models/sashimi.py","file_url":"https://github.com/albertfgu/diffwave-sashimi/blob/HEAD/models/sashimi.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"de72ca35554966ec","mcp_get_code":{"code_sha256":"de72ca35554966ec"}},{"arxiv_id":"1312.5602","paper":"/paper/playing-atari-with-deep-reinforcement","title":"Playing Atari with Deep Reinforcement Learning","date":"2013-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"proroklab/popgym","path":"popgym/baselines/models/s4d.py","file_url":"https://github.com/proroklab/popgym/blob/HEAD/popgym/baselines/models/s4d.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14bcfdbb8473e4b3","mcp_get_code":{"code_sha256":"14bcfdbb8473e4b3"}},{"arxiv_id":"2025.emnlp-main.1702","paper":null,"title":"arXiv:2025.emnlp-main.1702","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"IBM/NESTFUL","path":"data_v2/executable_functions/basic_functions.py","file_url":"https://github.com/IBM/NESTFUL/blob/HEAD/data_v2/executable_functions/basic_functions.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"29da7d03953ecf87","mcp_get_code":{"code_sha256":"29da7d03953ecf87"}}]}