{"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/safe-log","entry":"safe_log","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":27,"n_papers_ran":20,"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":18,"n_samples_ran":11,"n_samples_fingerprinted":10,"n_places":27,"n_places_pointer_only":9,"by_status":{"ran_honours":3,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":6,"unverified":7},"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":"2605.25924","paper":"/paper/arxiv-2605-25924","title":"Does Continued Pretraining on a Learner Corpus Improve Automated Essay Scoring on English Proficiency Tests? Evidence from EFCAMDAT","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"tanloong/neosca","path":"src/neosca/ns_utils.py","file_url":"https://github.com/tanloong/neosca/blob/HEAD/src/neosca/ns_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"fe2b70fed8f5568f","mcp_get_code":{"code_sha256":"fe2b70fed8f5568f"}},{"arxiv_id":"2605.12961","paper":"/paper/arxiv-2605-12961","title":"Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"2017LI/GSEC","path":"ensemble_en4_jian.py","file_url":"https://github.com/2017LI/GSEC/blob/HEAD/ensemble_en4_jian.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c45ed8970d283495","mcp_get_code":{"code_sha256":"c45ed8970d283495"}},{"arxiv_id":"2605.06407","paper":"/paper/arxiv-2605-06407","title":"WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"yanghaha0908/WavCube","path":"vocos/modules.py","file_url":"https://github.com/yanghaha0908/WavCube/blob/HEAD/vocos/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5923f41e711c0b69","mcp_get_code":{"code_sha256":"5923f41e711c0b69"}},{"arxiv_id":"2602.22101","paper":"/paper/arxiv-2602-22101","title":"On Imbalanced Regression with Hoeffding Trees","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"marinaAlchirch/DSFA_2026","path":"src/bayes/utils.py","file_url":"https://github.com/marinaAlchirch/DSFA_2026/blob/HEAD/src/bayes/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9b7535feb8e6a621","mcp_get_code":{"code_sha256":"9b7535feb8e6a621"}},{"arxiv_id":"2408.16532","paper":"/paper/wavtokenizer-an-efficient-acoustic-discrete","title":"WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling","date":"2024-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jishengpeng/wavtokenizer","path":"decoder/modules.py","file_url":"https://github.com/jishengpeng/wavtokenizer/blob/HEAD/decoder/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5923f41e711c0b69","mcp_get_code":{"code_sha256":"5923f41e711c0b69"}},{"arxiv_id":"2407.05361","paper":"/paper/emilia-an-extensive-multilingual-and-diverse","title":"Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation","date":"2024-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"open-mmlab/Amphion","path":"models/codec/amphion_codec/vocos.py","file_url":"https://github.com/open-mmlab/Amphion/blob/HEAD/models/codec/amphion_codec/vocos.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5923f41e711c0b69","mcp_get_code":{"code_sha256":"5923f41e711c0b69"}},{"arxiv_id":"2406.07532","paper":"/paper/hearing-anything-anywhere","title":"Hearing Anything Anywhere","date":"2024-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maswang32/hearinganythinganywhere","path":"metrics.py","file_url":"https://github.com/maswang32/hearinganythinganywhere/blob/HEAD/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b218c35ae77c76b2","mcp_get_code":{"code_sha256":"b218c35ae77c76b2"}},{"arxiv_id":"2405.11273","paper":"/paper/uni-moe-scaling-unified-multimodal-llms-with","title":"Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts","date":"2024-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hitsz-tmg/umoe-scaling-unified-multimodal-llms","path":"Uni-MoE-2/decoder/modules.py","file_url":"https://github.com/hitsz-tmg/umoe-scaling-unified-multimodal-llms/blob/HEAD/Uni-MoE-2/decoder/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5923f41e711c0b69","mcp_get_code":{"code_sha256":"5923f41e711c0b69"}},{"arxiv_id":"2404.15766","paper":"/paper/unifying-bayesian-flow-networks-and-diffusion","title":"Unifying Bayesian Flow Networks and Diffusion Models through Stochastic Differential Equations","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ML-GSAI/BFN-Solver","path":"utils.py","file_url":"https://github.com/ML-GSAI/BFN-Solver/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"34cc47ee17f6c9ee","mcp_get_code":{"code_sha256":"34cc47ee17f6c9ee"}},{"arxiv_id":"2402.12208","paper":"/paper/language-codec-reducing-the-gaps-between","title":"Language-Codec: Bridging Discrete Codec Representations and Speech Language Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jishengpeng/languagecodec","path":"languagecodec_decoder/modules.py","file_url":"https://github.com/jishengpeng/languagecodec/blob/HEAD/languagecodec_decoder/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5923f41e711c0b69","mcp_get_code":{"code_sha256":"5923f41e711c0b69"}},{"arxiv_id":"2401.07711","paper":"/paper/efficient-nonparametric-tensor-decomposition","title":"Efficient Nonparametric Tensor Decomposition for Binary and Count Data","date":"2024-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taozerui/gptd","path":"src/model/solve_gptf_pg.py","file_url":"https://github.com/taozerui/gptd/blob/HEAD/src/model/solve_gptf_pg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"819b61c1d573e833","mcp_get_code":{"code_sha256":"819b61c1d573e833"}},{"arxiv_id":"2308.07037","paper":"/paper/bayesian-flow-networks","title":"Bayesian Flow Networks","date":"2023-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nnaisense/bayesian-flow-networks","path":"utils_model.py","file_url":"https://github.com/nnaisense/bayesian-flow-networks/blob/HEAD/utils_model.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":"34cc47ee17f6c9ee","mcp_get_code":{"code_sha256":"34cc47ee17f6c9ee"}},{"arxiv_id":"2306.00814","paper":"/paper/vocos-closing-the-gap-between-time-domain-and","title":"Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis","date":"2023-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gemelo-ai/vocos","path":"vocos/modules.py","file_url":"https://github.com/gemelo-ai/vocos/blob/HEAD/vocos/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5923f41e711c0b69","mcp_get_code":{"code_sha256":"5923f41e711c0b69"}},{"arxiv_id":"2210.00999","paper":"/paper/latent-state-marginalization-as-a-low-cost","title":"Latent State Marginalization as a Low-cost Approach for Improving Exploration","date":"2022-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zdhnarsil/stochastic-marginal-actor-critic","path":"lib/utils.py","file_url":"https://github.com/zdhnarsil/stochastic-marginal-actor-critic/blob/HEAD/lib/utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13bc23344ef8bd53","mcp_get_code":{"code_sha256":"13bc23344ef8bd53"}},{"arxiv_id":"2206.11218","paper":"/paper/hierarchical-context-tagging-for-utterance","title":"Hierarchical Context Tagging for Utterance Rewriting","date":"2022-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lisjin/hct","path":"sequence_tagger.py","file_url":"https://github.com/lisjin/hct/blob/HEAD/sequence_tagger.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f4b113a24e219244","mcp_get_code":{"code_sha256":"f4b113a24e219244"}},{"arxiv_id":"2111.08175","paper":"/paper/inverse-weighted-survival-games","title":"Inverse-Weighted Survival Games","date":"2021-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rajesh-lab/inverse-weighted-survival-games","path":"_nll.py","file_url":"https://github.com/rajesh-lab/inverse-weighted-survival-games/blob/HEAD/_nll.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81be6fa4b449a9c3","mcp_get_code":{"code_sha256":"81be6fa4b449a9c3"}},{"arxiv_id":"2109.13398","paper":"/paper/unrolling-sgd-understanding-factors","title":"Unrolling SGD: Understanding Factors Influencing Machine Unlearning","date":"2021-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cleverhans-lab/unrolling-sgd","path":"Privacy_risk_score/calculate_PRS.py","file_url":"https://github.com/cleverhans-lab/unrolling-sgd/blob/HEAD/Privacy_risk_score/calculate_PRS.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76a2d8f814f3f8ba","mcp_get_code":{"code_sha256":"76a2d8f814f3f8ba"}},{"arxiv_id":"2104.08315","paper":"/paper/surface-form-competition-why-the-highest","title":"Surface Form Competition: Why the Highest Probability Answer Isn't Always Right","date":"2021-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"soheeyang/unified-prompt-selection","path":"method/calibration.py","file_url":"https://github.com/soheeyang/unified-prompt-selection/blob/HEAD/method/calibration.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":"6def77c0d0f72096","mcp_get_code":{"code_sha256":"6def77c0d0f72096"}},{"arxiv_id":"2104.05700","paper":"/paper/macro-average-rare-types-are-important-too","title":"Macro-Average: Rare Types Are Important Too","date":"2021-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isi-nlp/sacrebleu","path":"sacrebleu/metrics/base.py","file_url":"https://github.com/isi-nlp/sacrebleu/blob/HEAD/sacrebleu/metrics/base.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":"773dd52da7e1cddf","mcp_get_code":{"code_sha256":"773dd52da7e1cddf"}},{"arxiv_id":"2102.05379","paper":"/paper/argmax-flows-and-multinomial-diffusion","title":"Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions","date":"2021-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"didriknielsen/argmax_flows","path":"model/CategoricalNF/flows/mixture_cdf_layer.py","file_url":"https://github.com/didriknielsen/argmax_flows/blob/HEAD/model/CategoricalNF/flows/mixture_cdf_layer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13bc23344ef8bd53","mcp_get_code":{"code_sha256":"13bc23344ef8bd53"}},{"arxiv_id":"2006.11524","paper":"/paper/neuro-symbolic-visual-reasoning-disentangling","title":"Neuro-Symbolic Visual Reasoning: Disentangling \"Visual\" from \"Reasoning\"","date":"2020-06-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/DFOL-VQA","path":"src/nsvqa/nn/interpreter/util.py","file_url":"https://github.com/microsoft/DFOL-VQA/blob/HEAD/src/nsvqa/nn/interpreter/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"91385a7e97f5425a","mcp_get_code":{"code_sha256":"91385a7e97f5425a"}},{"arxiv_id":"2004.12585","paper":"/paper/a-batch-normalized-inference-network-keeps","title":"A Batch Normalized Inference Network Keeps the KL Vanishing Away","date":"2020-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valdersoul/bn-vae","path":"modules/utils.py","file_url":"https://github.com/valdersoul/bn-vae/blob/HEAD/modules/utils.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6298a45a525420e8","mcp_get_code":{"code_sha256":"6298a45a525420e8"}},{"arxiv_id":"2003.14407","paper":"/paper/probabilistic-pixel-adaptive-refinement","title":"Probabilistic Pixel-Adaptive Refinement Networks","date":"2020-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"visinf/ppac_refinement","path":"src/prob_utils.py","file_url":"https://github.com/visinf/ppac_refinement/blob/HEAD/src/prob_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":"fc56993bcc5b3f8f","mcp_get_code":{"code_sha256":"fc56993bcc5b3f8f"}},{"arxiv_id":"2003.01941","paper":"/paper/gaussianization-flows","title":"Gaussianization Flows","date":"2020-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IPL-UV/rbig_jax","path":"rbig_jax/utils.py","file_url":"https://github.com/IPL-UV/rbig_jax/blob/HEAD/rbig_jax/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"105717a22f1e0c02","mcp_get_code":{"code_sha256":"105717a22f1e0c02"}},{"arxiv_id":"1906.06818","paper":"/paper/stacked-capsule-autoencoders","title":"Stacked Capsule Autoencoders","date":"2019-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akosiorek/stacked_capsule_autoencoders","path":"capsules/math_ops.py","file_url":"https://github.com/akosiorek/stacked_capsule_autoencoders/blob/HEAD/capsules/math_ops.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":"d06435ecd510267e","mcp_get_code":{"code_sha256":"d06435ecd510267e"}},{"arxiv_id":"1505.05770","paper":"/paper/variational-inference-with-normalizing-flows","title":"Variational Inference with Normalizing Flows","date":"2015-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ex4sperans/variational-inference-with-normalizing-flows","path":"flow.py","file_url":"https://github.com/ex4sperans/variational-inference-with-normalizing-flows/blob/HEAD/flow.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6298a45a525420e8","mcp_get_code":{"code_sha256":"6298a45a525420e8"}},{"arxiv_id":"openreview_0xJt4PqPJv","paper":null,"title":"arXiv:openreview_0xJt4PqPJv","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shi-ang/SALaD","path":"iwsg/util.py","file_url":"https://github.com/shi-ang/SALaD/blob/HEAD/iwsg/util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"81be6fa4b449a9c3","mcp_get_code":{"code_sha256":"81be6fa4b449a9c3"}}]}