{"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/torch-to-numpy","entry":"torch_to_numpy","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":11,"n_papers_ran":4,"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":9,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":12,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"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":"2606.05793","paper":"/paper/arxiv-2606-05793","title":"CollabBench: Benchmarking and Unleashing Collaborative Ability of LLMs with Diverse Players via Proactive Engagement","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"langfengQ/verl-agent","path":"agent_system/multi_turn_rollout/utils.py","file_url":"https://github.com/langfengQ/verl-agent/blob/HEAD/agent_system/multi_turn_rollout/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f724bee53f6bfb2b","mcp_get_code":{"code_sha256":"f724bee53f6bfb2b"}},{"arxiv_id":"2512.16848","paper":"/paper/arxiv-2512-16848","title":"Meta-RL Induces Exploration in Language Agents","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"mlbio-epfl/LaMer","path":"agent_system/multi_turn_rollout/utils.py","file_url":"https://github.com/mlbio-epfl/LaMer/blob/HEAD/agent_system/multi_turn_rollout/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f724bee53f6bfb2b","mcp_get_code":{"code_sha256":"f724bee53f6bfb2b"}},{"arxiv_id":"2509.21387","paper":"/paper/arxiv-2509-21387","title":"Do Sparse Subnetworks Exhibit Cognitively Aligned Attention? Effects of Pruning on Saliency Map Fidelity, Sparsity, and Concept Coherence","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"deel-ai/Craft","path":"craft/craft_torch.py","file_url":"https://github.com/deel-ai/Craft/blob/HEAD/craft/craft_torch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f585f3037c450f22","mcp_get_code":{"code_sha256":"f585f3037c450f22"}},{"arxiv_id":"2506.22803","paper":null,"title":"arXiv:2506.22803","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"XiGuaBo/CBM-HNMU","path":"train_base.py","file_url":"https://github.com/XiGuaBo/CBM-HNMU/blob/HEAD/train_base.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f585f3037c450f22","mcp_get_code":{"code_sha256":"f585f3037c450f22"}},{"arxiv_id":"2505.06744","paper":"/paper/lineflow-a-framework-to-learn-active-control","title":"LineFlow: A Framework to Learn Active Control of Production Lines","date":"2025-05-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hs-kempten/lineflow","path":"lineflow/helpers.py","file_url":"https://github.com/hs-kempten/lineflow/blob/HEAD/lineflow/helpers.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":"e3f7916aaca3507f","mcp_get_code":{"code_sha256":"e3f7916aaca3507f"}},{"arxiv_id":"2403.01807","paper":"/paper/viewdiff-3d-consistent-image-generation-with","title":"ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/viewdiff","path":"viewdiff/io_util.py","file_url":"https://github.com/facebookresearch/viewdiff/blob/HEAD/viewdiff/io_util.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"1c2f195d3284ee90","mcp_get_code":{"code_sha256":"1c2f195d3284ee90"}},{"arxiv_id":"2311.00213","paper":"/paper/consistent-video-to-video-transfer-using","title":"Consistent Video-to-Video Transfer Using Synthetic Dataset","date":"2023-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"amazon-science/instruct-video-to-video","path":"video_prompt_to_prompt.py","file_url":"https://github.com/amazon-science/instruct-video-to-video/blob/HEAD/video_prompt_to_prompt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT-0","inline_ok":true,"code_sha256_prefix":"dc99b4575f2bba57","mcp_get_code":{"code_sha256":"dc99b4575f2bba57"}},{"arxiv_id":"2305.18405","paper":"/paper/dink-net-neural-clustering-on-large-graphs","title":"Dink-Net: Neural Clustering on Large Graphs","date":"2023-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yueliu1999/awesome-deep-graph-clustering","path":"dgc/utils/data_processor.py","file_url":"https://github.com/yueliu1999/awesome-deep-graph-clustering/blob/HEAD/dgc/utils/data_processor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a030011abbbef71","mcp_get_code":{"code_sha256":"8a030011abbbef71"}},{"arxiv_id":"1912.09278","paper":"/paper/-net-systematic-evaluation-of-iterative-deep","title":"$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction","date":"2019-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"khammernik/sigmanet","path":"reconstruction/common/utils.py","file_url":"https://github.com/khammernik/sigmanet/blob/HEAD/reconstruction/common/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b595ddeeba2a015","mcp_get_code":{"code_sha256":"2b595ddeeba2a015"}},{"arxiv_id":"aaai_20726","paper":null,"title":"arXiv:aaai_20726","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yueliu1999/Awesome-Deep-Graph-Clustering","path":"dgc/utils/data_processor.py","file_url":"https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering/blob/HEAD/dgc/utils/data_processor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a030011abbbef71","mcp_get_code":{"code_sha256":"8a030011abbbef71"}},{"arxiv_id":"aaai_20726","paper":null,"title":"arXiv:aaai_20726","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"yueliu1999/DCRN","path":"utils.py","file_url":"https://github.com/yueliu1999/DCRN/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ef3c3f619b7837b","mcp_get_code":{"code_sha256":"7ef3c3f619b7837b"}},{"arxiv_id":"Guo_Smooth_Diffusion_Crafting_Smooth_Latent_Spaces_in_Diffusion_Models_CVPR_2024_paper","paper":null,"title":"arXiv:Guo_Smooth_Diffusion_Crafting_Smooth_Latent_Spaces_in_Diffusion_Models_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SHI-Labs/Smooth-Diffusion","path":"app_utils.py","file_url":"https://github.com/SHI-Labs/Smooth-Diffusion/blob/HEAD/app_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aba961822e006627","mcp_get_code":{"code_sha256":"aba961822e006627"}}]}