{"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/setup-wandb","entry":"setup_wandb","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":13,"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":11,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":13,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":3,"unverified":8},"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":"2601.05637","paper":"/paper/arxiv-2601-05637","title":"GenCtrl -A Formal Controllability Toolkit for Generative Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"apple/ml-genctrl","path":"genctrl/utils/setup_utils.py","file_url":"https://github.com/apple/ml-genctrl/blob/HEAD/genctrl/utils/setup_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"acb35d9e264d5b20","mcp_get_code":{"code_sha256":"acb35d9e264d5b20"}},{"arxiv_id":"2507.02092","paper":"/paper/energy-based-transformers-are-scalable","title":"Energy-Based Transformers are Scalable Learners and Thinkers","date":"2025-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexiglad/EBT","path":"train_model.py","file_url":"https://github.com/alexiglad/EBT/blob/HEAD/train_model.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":"ee4f12db0f2ec743","mcp_get_code":{"code_sha256":"ee4f12db0f2ec743"}},{"arxiv_id":"2506.15725","paper":null,"title":"arXiv:2506.15725","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"mninniri/GrIDDD","path":"griddd/utils.py","file_url":"https://github.com/mninniri/GrIDDD/blob/HEAD/griddd/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"be7738474afcfd15","mcp_get_code":{"code_sha256":"be7738474afcfd15"}},{"arxiv_id":"2506.12822","paper":"/paper/enhancing-rating-based-reinforcement-learning","title":"Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models","date":"2025-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tunglm2203/erlvlm","path":"logger.py","file_url":"https://github.com/tunglm2203/erlvlm/blob/HEAD/logger.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0df23daf0145741e","mcp_get_code":{"code_sha256":"0df23daf0145741e"}},{"arxiv_id":"2408.11804","paper":"/paper/approaching-deep-learning-through-the","title":"Approaching Deep Learning through the Spectral Dynamics of Weights","date":"2024-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dyunis/spectral_dynamics","path":"language_modeling/config.py","file_url":"https://github.com/dyunis/spectral_dynamics/blob/HEAD/language_modeling/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c00218b3148eba4","mcp_get_code":{"code_sha256":"2c00218b3148eba4"}},{"arxiv_id":"2407.02490","paper":"/paper/minference-1-0-accelerating-pre-filling-for","title":"MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention","date":"2024-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/LLMLingua","path":"experiments/securitylingua/train_roberta.py","file_url":"https://github.com/microsoft/LLMLingua/blob/HEAD/experiments/securitylingua/train_roberta.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bdd16b22daad11a5","mcp_get_code":{"code_sha256":"bdd16b22daad11a5"}},{"arxiv_id":"2406.17341","paper":"/paper/generative-modelling-of-structurally","title":"Generative Modelling of Structurally Constrained Graphs","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manuelmlmadeira/ConStruct","path":"ConStruct/utils.py","file_url":"https://github.com/manuelmlmadeira/ConStruct/blob/HEAD/ConStruct/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"727a5e6e5f9f6115","mcp_get_code":{"code_sha256":"727a5e6e5f9f6115"}},{"arxiv_id":"2405.15593","paper":"/paper/microadam-accurate-adaptive-optimization-with","title":"MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence","date":"2024-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ist-daslab/microadam","path":"huggingface_glue_mnli/helpers.py","file_url":"https://github.com/ist-daslab/microadam/blob/HEAD/huggingface_glue_mnli/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":"a5159521471b0e58","mcp_get_code":{"code_sha256":"a5159521471b0e58"}},{"arxiv_id":"2403.06020","paper":"/paper/multi-conditioned-graph-diffusion-for-neural","title":"Multi-conditioned Graph Diffusion for Neural Architecture Search","date":"2024-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rohanasthana/dinas","path":"main_reg_free.py","file_url":"https://github.com/rohanasthana/dinas/blob/HEAD/main_reg_free.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cb06d97097718c4f","mcp_get_code":{"code_sha256":"cb06d97097718c4f"}},{"arxiv_id":"2402.16302","paper":"/paper/graph-diffusion-policy-optimization","title":"Graph Diffusion Policy Optimization","date":"2024-02-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/gdpo","path":"main_ppo.py","file_url":"https://github.com/sail-sg/gdpo/blob/HEAD/main_ppo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cb06d97097718c4f","mcp_get_code":{"code_sha256":"cb06d97097718c4f"}},{"arxiv_id":"2311.02142","paper":"/paper/sparse-training-of-discrete-diffusion-models","title":"Sparse Training of Discrete Diffusion Models for Graph Generation","date":"2023-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qym7/sparsediff","path":"sparse_diffusion/utils.py","file_url":"https://github.com/qym7/sparsediff/blob/HEAD/sparse_diffusion/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"938ae36c029d92da","mcp_get_code":{"code_sha256":"938ae36c029d92da"}},{"arxiv_id":"2302.09048","paper":"/paper/midi-mixed-graph-and-3d-denoising-diffusion","title":"MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvignac/midi","path":"midi/utils.py","file_url":"https://github.com/cvignac/midi/blob/HEAD/midi/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"be7738474afcfd15","mcp_get_code":{"code_sha256":"be7738474afcfd15"}},{"arxiv_id":"2209.14977","paper":"/paper/transformer-meets-boundary-value-inverse","title":"Transformer Meets Boundary Value Inverse Problems","date":"2022-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zenki229/tmcfpinn","path":"src/utils/experiments.py","file_url":"https://github.com/zenki229/tmcfpinn/blob/HEAD/src/utils/experiments.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d562a082cac25629","mcp_get_code":{"code_sha256":"d562a082cac25629"}}]}