{"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/prepare-batch","entry":"prepare_batch","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":8,"n_papers_ran":7,"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":8,"n_samples_ran":7,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":1,"ran":4,"unverified":1},"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":"2506.13485","paper":"/paper/curriculum-learning-for-biological-sequence","title":"Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing","date":"2025-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"beam-labs/denovo","path":"ContraNovo/denovo/dataloaders.py","file_url":"https://github.com/beam-labs/denovo/blob/HEAD/ContraNovo/denovo/dataloaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e21ac907bcfd2287","mcp_get_code":{"code_sha256":"e21ac907bcfd2287"}},{"arxiv_id":"2410.13823","paper":"/paper/deep-generative-models-unveil-patterns-in","title":"Deep Generative Models Unveil Patterns in Medical Images Through Vision-Language Conditioning","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junzhin/dgm-vlc","path":"inference_whole.py","file_url":"https://github.com/junzhin/dgm-vlc/blob/HEAD/inference_whole.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0cd6c249e7c8b74d","mcp_get_code":{"code_sha256":"0cd6c249e7c8b74d"}},{"arxiv_id":"2406.09850","paper":"/paper/gradeadreamer-enhanced-text-to-3d-generation","title":"GradeADreamer: Enhanced Text-to-3D Generation Using Gaussian Splatting and Multi-View Diffusion","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trapoom555/gradeadreamer","path":"gradeadreamer/appearance.py","file_url":"https://github.com/trapoom555/gradeadreamer/blob/HEAD/gradeadreamer/appearance.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fb244349fc776dee","mcp_get_code":{"code_sha256":"fb244349fc776dee"}},{"arxiv_id":"2402.11363","paper":"/paper/transformer-based-de-novo-peptide-sequencing","title":"Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry","date":"2024-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biocomputing-research-group/diatrans","path":"casanovo_dia/denovo/dataloaders.py","file_url":"https://github.com/biocomputing-research-group/diatrans/blob/HEAD/casanovo_dia/denovo/dataloaders.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc003fb5c1f67fcd","mcp_get_code":{"code_sha256":"bc003fb5c1f67fcd"}},{"arxiv_id":"2401.05792","paper":"/paper/discovering-low-rank-subspaces-for-language","title":"Discovering Low-rank Subspaces for Language-agnostic Multilingual Representations","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fffffarmer/lsar","path":"src/utils_extract.py","file_url":"https://github.com/fffffarmer/lsar/blob/HEAD/src/utils_extract.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"30c14bbed320bfdc","mcp_get_code":{"code_sha256":"30c14bbed320bfdc"}},{"arxiv_id":"2311.18448","paper":"/paper/hold-category-agnostic-3d-reconstruction-of","title":"HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zc-alexfan/hold","path":"code/src/hold/hold_utils.py","file_url":"https://github.com/zc-alexfan/hold/blob/HEAD/code/src/hold/hold_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b81e9be8460937e1","mcp_get_code":{"code_sha256":"b81e9be8460937e1"}},{"arxiv_id":"2104.01542","paper":"/paper/synergies-between-affordance-and-geometry-6","title":"Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations","date":"2021-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UT-Austin-RPL/GIGA","path":"scripts/train_giga.py","file_url":"https://github.com/UT-Austin-RPL/GIGA/blob/HEAD/scripts/train_giga.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":"e2e039254a1c8310","mcp_get_code":{"code_sha256":"e2e039254a1c8310"}},{"arxiv_id":"2003.05425","paper":"/paper/gauge-equivariant-mesh-cnns-anisotropic","title":"Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs","date":"2020-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qualcomm-ai-research/gauge-equivariant-mesh-cnn","path":"experiments/shapes.py","file_url":"https://github.com/qualcomm-ai-research/gauge-equivariant-mesh-cnn/blob/HEAD/experiments/shapes.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause-Clear","inline_ok":false,"code_sha256_prefix":"7a30a3d65da7b103","mcp_get_code":{"code_sha256":"7a30a3d65da7b103"}}]}