{"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/get-sample","entry":"get_sample","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":14,"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":14,"n_samples_ran":8,"n_samples_fingerprinted":3,"n_places":14,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":3,"ran":2,"unverified":6},"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":"2509.23713","paper":"/paper/arxiv-2509-23713","title":"Text-to-Code Generation for Modular Building Layouts in Building Information Modeling","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"CI3LAB/Text2MBL","path":"Data_and_code/utils/fully_synthetic.py","file_url":"https://github.com/CI3LAB/Text2MBL/blob/HEAD/Data_and_code/utils/fully_synthetic.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6b4d21d29a26e4c1","mcp_get_code":{"code_sha256":"6b4d21d29a26e4c1"}},{"arxiv_id":"2412.11198","paper":"/paper/gem-a-generalizable-ego-vision-multimodal","title":"GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control","date":"2024-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vita-epfl/gem","path":"sample.py","file_url":"https://github.com/vita-epfl/gem/blob/HEAD/sample.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":"bb11e400c2693780","mcp_get_code":{"code_sha256":"bb11e400c2693780"}},{"arxiv_id":"2412.05586","paper":"/paper/towards-learning-to-reason-comparing-llms","title":"Towards Learning to Reason: Comparing LLMs with Neuro-Symbolic on Arithmetic Relations in Abstract Reasoning","date":"2024-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/raven-large-language-models","path":"src/datasets/generation/iraven_task.py","file_url":"https://github.com/ibm/raven-large-language-models/blob/HEAD/src/datasets/generation/iraven_task.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":"47ad94bbde448eaf","mcp_get_code":{"code_sha256":"47ad94bbde448eaf"}},{"arxiv_id":"2410.12456","paper":"/paper/training-neural-samplers-with-reverse","title":"Training Neural Samplers with Reverse Diffusive KL Divergence","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiajunhe98/DiKL","path":"DiKL/train_utils.py","file_url":"https://github.com/jiajunhe98/DiKL/blob/HEAD/DiKL/train_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b1acbf2faa52b3e8","mcp_get_code":{"code_sha256":"b1acbf2faa52b3e8"}},{"arxiv_id":"2407.01489","paper":"/paper/agentless-demystifying-llm-based-software","title":"Agentless: Demystifying LLM-based Software Engineering Agents","date":"2024-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenAutoCoder/Agentless","path":"agentless/repair/rerank.py","file_url":"https://github.com/OpenAutoCoder/Agentless/blob/HEAD/agentless/repair/rerank.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f12cc79072e9048","mcp_get_code":{"code_sha256":"6f12cc79072e9048"}},{"arxiv_id":"2406.02645","paper":"/paper/astral-training-physics-informed-neural","title":"Astral: training physics-informed neural networks with error majorants","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"4gnskq5g2s-collab/Astral","path":"datasets/magnetostatics.py","file_url":"https://github.com/4gnskq5g2s-collab/Astral/blob/HEAD/datasets/magnetostatics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"code_sha256_prefix":"476c537d47b2e48b","mcp_get_code":{"code_sha256":"476c537d47b2e48b"}},{"arxiv_id":"2403.11013","paper":"/paper/improved-algorithm-and-bounds-for-successive","title":"Improved Algorithm and Bounds for Successive Projection","date":"2024-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gabriel78110/vertexhunting","path":"vertexH.py","file_url":"https://github.com/gabriel78110/vertexhunting/blob/HEAD/vertexH.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"77e58ba613633c6d","mcp_get_code":{"code_sha256":"77e58ba613633c6d"}},{"arxiv_id":"2402.05585","paper":"/paper/neural-functional-a-posteriori-error","title":"Neural functional a posteriori error estimates","date":null,"month_inferred_from_arxiv_id":"2024-02","title_source":"archive","repo":"VLSF/UQNO","path":"datasets/PiNN_datasets/Anisotropic_diffusion.py","file_url":"https://github.com/VLSF/UQNO/blob/HEAD/datasets/PiNN_datasets/Anisotropic_diffusion.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"code_sha256_prefix":"d71f64e6c531e7a8","mcp_get_code":{"code_sha256":"d71f64e6c531e7a8"}},{"arxiv_id":"2308.13175","paper":"/paper/gridpull-towards-scalability-in-learning","title":"GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Clouds","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenchao15/GridPull","path":"sample_func.py","file_url":"https://github.com/chenchao15/GridPull/blob/HEAD/sample_func.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"391e5e25e0fdcc33","mcp_get_code":{"code_sha256":"391e5e25e0fdcc33"}},{"arxiv_id":"2305.17626","paper":"/paper/in-context-analogical-reasoning-with-pre","title":"In-Context Analogical Reasoning with Pre-Trained Language Models","date":"2023-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hxiaoyang/lm-raven","path":"task.py","file_url":"https://github.com/hxiaoyang/lm-raven/blob/HEAD/task.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fb271e4c166d5129","mcp_get_code":{"code_sha256":"fb271e4c166d5129"}},{"arxiv_id":"2303.01471","paper":"/paper/quantum-hamiltonian-descent","title":"Quantum Hamiltonian Descent","date":"2023-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaqileng/quantum-hamiltonian-descent","path":"plot/fig2/NonCVX2d_evolution.py","file_url":"https://github.com/jiaqileng/quantum-hamiltonian-descent/blob/HEAD/plot/fig2/NonCVX2d_evolution.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4ebbb94bdcce2731","mcp_get_code":{"code_sha256":"4ebbb94bdcce2731"}},{"arxiv_id":"2109.01135","paper":"/paper/sequence-to-sequence-learning-with-latent","title":"Sequence-to-Sequence Learning with Latent Neural Grammars","date":"2021-09-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yli1/CGPS","path":"adj_data_generator.py","file_url":"https://github.com/yli1/CGPS/blob/HEAD/adj_data_generator.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":"ad335e7ef703cc26","mcp_get_code":{"code_sha256":"ad335e7ef703cc26"}},{"arxiv_id":"1908.05318","paper":"/paper/jedi-net-a-jet-identification-algorithm-based","title":"JEDI-net: a jet identification algorithm based on interaction networks","date":null,"month_inferred_from_arxiv_id":"2019-08","title_source":"archive","repo":"jmduarte/JEDInet-code","path":"python/JetImageClassifier_INTop_FinalTraining.py","file_url":"https://github.com/jmduarte/JEDInet-code/blob/HEAD/python/JetImageClassifier_INTop_FinalTraining.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"761ba570d4de7eb4","mcp_get_code":{"code_sha256":"761ba570d4de7eb4"}},{"arxiv_id":"1901.02731","paper":"/paper/a-comprehensive-guide-to-bayesian","title":"A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference","date":"2019-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kumar-shridhar/PyTorch-BayesianCNN","path":"uncertainty_estimation.py","file_url":"https://github.com/kumar-shridhar/PyTorch-BayesianCNN/blob/HEAD/uncertainty_estimation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6b6f997bdbf99918","mcp_get_code":{"code_sha256":"6b6f997bdbf99918"}}]}