{"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/load-module","entry":"load_module","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":21,"n_papers_ran":8,"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":24,"n_samples_ran":9,"n_samples_fingerprinted":1,"n_places":24,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":7,"unverified":15},"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":"2609.03109","paper":"/paper/arxiv-2609-03109","title":"SLIDEFORGE: An LLM Agent for Controllable Editing of Slides as Structured Artifacts","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"UIUC-MONET/SLIDEFORGE","path":"decomposition/sam3_worker.py","file_url":"https://github.com/UIUC-MONET/SLIDEFORGE/blob/HEAD/decomposition/sam3_worker.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c4730e5fdd7d089d","mcp_get_code":{"code_sha256":"c4730e5fdd7d089d"}},{"arxiv_id":"2607.28478","paper":"/paper/arxiv-2607-28478","title":"Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"Wuzheng02/SaliTrap","path":"run_pipeline.py","file_url":"https://github.com/Wuzheng02/SaliTrap/blob/HEAD/run_pipeline.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0930c9d5476d5050","mcp_get_code":{"code_sha256":"0930c9d5476d5050"}},{"arxiv_id":"2607.28478","paper":"/paper/arxiv-2607-28478","title":"Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"Wuzheng02/SaliTrap","path":"v3_registry/phystrap_v3_candidate_registry.py","file_url":"https://github.com/Wuzheng02/SaliTrap/blob/HEAD/v3_registry/phystrap_v3_candidate_registry.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8906b04cc90f9e7","mcp_get_code":{"code_sha256":"f8906b04cc90f9e7"}},{"arxiv_id":"2606.07289","paper":"/paper/arxiv-2606-07289","title":"Closed-Form Spectral Regularization for Multi-Task Model Merging","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"WalkerWorldPeace/MLLMerging","path":"LLaMA-Factory/benchmark_mllm_merge_cost.py","file_url":"https://github.com/WalkerWorldPeace/MLLMerging/blob/HEAD/LLaMA-Factory/benchmark_mllm_merge_cost.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c722e6776be8c9ac","mcp_get_code":{"code_sha256":"c722e6776be8c9ac"}},{"arxiv_id":"2605.18959","paper":"/paper/arxiv-2605-18959","title":"Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"giampaolo/psutil","path":"_bootstrap.py","file_url":"https://github.com/giampaolo/psutil/blob/HEAD/_bootstrap.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"9ef0023d57b96cc3","mcp_get_code":{"code_sha256":"9ef0023d57b96cc3"}},{"arxiv_id":"2508.18211","paper":"/paper/arxiv-2508-18211","title":"Flexibility-conditioned protein structure design with flow matching","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"graeter-group/flips","path":"flips/models/flex_utils.py","file_url":"https://github.com/graeter-group/flips/blob/HEAD/flips/models/flex_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d7b911bab5fea9fb","mcp_get_code":{"code_sha256":"d7b911bab5fea9fb"}},{"arxiv_id":"2508.18211","paper":"/paper/arxiv-2508-18211","title":"Flexibility-conditioned protein structure design with flow matching","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"graeter-group/flips","path":"flips/models/load_module.py","file_url":"https://github.com/graeter-group/flips/blob/HEAD/flips/models/load_module.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6b6fbe89a1d01b70","mcp_get_code":{"code_sha256":"6b6fbe89a1d01b70"}},{"arxiv_id":"2506.01374","paper":"/paper/compiler-optimization-via-llm-reasoning-for","title":"Compiler Optimization via LLM Reasoning for Efficient Model Serving","date":"2025-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"he-actlab/REASONING_COMPILER","path":"python/tvm/runtime/module.py","file_url":"https://github.com/he-actlab/REASONING_COMPILER/blob/HEAD/python/tvm/runtime/module.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":"833de9a5dfdc5c20","mcp_get_code":{"code_sha256":"833de9a5dfdc5c20"}},{"arxiv_id":"2412.12971","paper":"/paper/archesweather-archesweathergen-a","title":"ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting","date":"2024-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"inria/geoarches","path":"geoarches/lightning_modules/base_module.py","file_url":"https://github.com/inria/geoarches/blob/HEAD/geoarches/lightning_modules/base_module.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"5645df10582e8e91","mcp_get_code":{"code_sha256":"5645df10582e8e91"}},{"arxiv_id":"2411.05238","paper":"/paper/generating-highly-designable-proteins-with","title":"Generating Highly Designable Proteins with Geometric Algebra Flow Matching","date":"2024-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hits-mli/gafl","path":"gafl/models/utils.py","file_url":"https://github.com/hits-mli/gafl/blob/HEAD/gafl/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0e4712382c310b3b","mcp_get_code":{"code_sha256":"0e4712382c310b3b"}},{"arxiv_id":"2410.18987","paper":"/paper/generative-topology-for-shape-synthesis","title":"Generative Topology for Shape Synthesis","date":"2024-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aidos-lab/inner-product-transforms","path":"src/loaders.py","file_url":"https://github.com/aidos-lab/inner-product-transforms/blob/HEAD/src/loaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"f4cb5f98651570ff","mcp_get_code":{"code_sha256":"f4cb5f98651570ff"}},{"arxiv_id":"2407.10114","paper":"/paper/tokenshap-interpreting-large-language-models","title":"TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation","date":"2024-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ronigold/TokenSHAP","path":"experiments/agentshap/exp6_model_comparison.py","file_url":"https://github.com/ronigold/TokenSHAP/blob/HEAD/experiments/agentshap/exp6_model_comparison.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0cd7798a11c1b258","mcp_get_code":{"code_sha256":"0cd7798a11c1b258"}},{"arxiv_id":"2402.10011","paper":"/paper/clifford-group-equivariant-simplicial-message","title":"Clifford Group Equivariant Simplicial Message Passing Networks","date":"2024-02-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"congliuuva/clifford-group-equivariant-simplicial-message-passing-networks","path":"engineer/utils/load_module.py","file_url":"https://github.com/congliuuva/clifford-group-equivariant-simplicial-message-passing-networks/blob/HEAD/engineer/utils/load_module.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5041ad05fc51f152","mcp_get_code":{"code_sha256":"5041ad05fc51f152"}},{"arxiv_id":"2312.04559","paper":"/paper/primdiffusion-volumetric-primitives-diffusion-1","title":"PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frozenburning/primdiffusion","path":"dva/io.py","file_url":"https://github.com/frozenburning/primdiffusion/blob/HEAD/dva/io.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6ee8f3f5648074e6","mcp_get_code":{"code_sha256":"6ee8f3f5648074e6"}},{"arxiv_id":"2104.07916","paper":"/paper/polynomial-networks-in-deep-classifiers","title":"Augmenting Deep Classifiers with Polynomial Neural Networks","date":"2021-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"grigorisg9gr/polynomials-for-augmenting-nns","path":"utils.py","file_url":"https://github.com/grigorisg9gr/polynomials-for-augmenting-nns/blob/HEAD/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e5a47a9ef50941b5","mcp_get_code":{"code_sha256":"e5a47a9ef50941b5"}},{"arxiv_id":"2010.15727","paper":"/paper/attentive-clustering-processes","title":"Amortized Probabilistic Detection of Communities in Graphs","date":"2020-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aripakman/amortized_community_detection","path":"acp/train_acp.py","file_url":"https://github.com/aripakman/amortized_community_detection/blob/HEAD/acp/train_acp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b1050fb2a277612d","mcp_get_code":{"code_sha256":"b1050fb2a277612d"}},{"arxiv_id":"2010.15727","paper":"/paper/attentive-clustering-processes","title":"Amortized Probabilistic Detection of Communities in Graphs","date":"2020-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aripakman/attentive_clustering_processes","path":"acp/train_acp.py","file_url":"https://github.com/aripakman/attentive_clustering_processes/blob/HEAD/acp/train_acp.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8f772e11e7c48dd5","mcp_get_code":{"code_sha256":"8f772e11e7c48dd5"}},{"arxiv_id":"2006.09791","paper":"/paper/optimizing-grouped-convolutions-on-edge","title":"Optimizing Grouped Convolutions on Edge Devices","date":"2020-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gecLAB/tvm-GSPC","path":"python/tvm/runtime/module.py","file_url":"https://github.com/gecLAB/tvm-GSPC/blob/HEAD/python/tvm/runtime/module.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":"670d33f60a876bd1","mcp_get_code":{"code_sha256":"670d33f60a876bd1"}},{"arxiv_id":"2005.04078","paper":"/paper/a-sim2real-deep-learning-approach-for-the","title":"A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View","date":"2020-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ika-rwth-aachen/Cam2BEV","path":"model/utils.py","file_url":"https://github.com/ika-rwth-aachen/Cam2BEV/blob/HEAD/model/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6965945ec615f691","mcp_get_code":{"code_sha256":"6965945ec615f691"}},{"arxiv_id":"2003.05477","paper":"/paper/unified-image-and-video-saliency-modeling","title":"Unified Image and Video Saliency Modeling","date":"2020-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rdroste/unisal","path":"unisal/utils.py","file_url":"https://github.com/rdroste/unisal/blob/HEAD/unisal/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":"11091601dda5e015","mcp_get_code":{"code_sha256":"11091601dda5e015"}},{"arxiv_id":"2003.02989","paper":"/paper/tensorflow-quantum-a-software-framework-for","title":"TensorFlow Quantum: A Software Framework for Quantum Machine Learning","date":"2020-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tensorflow/quantum","path":"tensorflow_quantum/core/ops/load_module.py","file_url":"https://github.com/tensorflow/quantum/blob/HEAD/tensorflow_quantum/core/ops/load_module.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":"2da91ce66ebf05a5","mcp_get_code":{"code_sha256":"2da91ce66ebf05a5"}},{"arxiv_id":"1904.01774","paper":"/paper/image-generation-from-small-datasets-via","title":"Image Generation From Small Datasets via Batch Statistics Adaptation","date":"2019-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nogu-atsu/small-dataset-image-generation","path":"source/yaml_utils.py","file_url":"https://github.com/nogu-atsu/small-dataset-image-generation/blob/HEAD/source/yaml_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f5837ba49e24a57d","mcp_get_code":{"code_sha256":"f5837ba49e24a57d"}},{"arxiv_id":"1810.00846","paper":"/paper/classification-from-positive-unlabeled-and","title":"Classification from Positive, Unlabeled and Biased Negative Data","date":"2018-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZaydH/covariate_shift_risk_estimation","path":"pubn/model.py","file_url":"https://github.com/ZaydH/covariate_shift_risk_estimation/blob/HEAD/pubn/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2dacab11904431a7","mcp_get_code":{"code_sha256":"2dacab11904431a7"}},{"arxiv_id":"aaai_30106","paper":null,"title":"arXiv:aaai_30106","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ZaydH/feature-partition","path":"src/bound/learner_submodel.py","file_url":"https://github.com/ZaydH/feature-partition/blob/HEAD/src/bound/learner_submodel.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d598aa98483d697","mcp_get_code":{"code_sha256":"1d598aa98483d697"}}]}