{"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-solver","entry":"get_solver","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":10,"n_papers_ran":0,"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":5,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":10,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":5},"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":"2608.15073","paper":"/paper/arxiv-2608-15073","title":"BOCoDe: Engineering-Centered Benchmarking for Bayesian Optimization","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"yunshengtian/DGEMO","path":"mobo/factory.py","file_url":"https://github.com/yunshengtian/DGEMO/blob/HEAD/mobo/factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"642bd668f157ee74","mcp_get_code":{"code_sha256":"642bd668f157ee74"}},{"arxiv_id":"2605.10076","paper":"/paper/arxiv-2605-10076","title":"A Stability Benchmark of Generative Regularizers for Inverse Problems","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"alexdenker/GenRegBench","path":"reconstruction_methods/flow_dps.py","file_url":"https://github.com/alexdenker/GenRegBench/blob/HEAD/reconstruction_methods/flow_dps.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4f078fd69624c31f","mcp_get_code":{"code_sha256":"4f078fd69624c31f"}},{"arxiv_id":"2601.04791","paper":"/paper/arxiv-2601-04791","title":"Measurement-Consistent Langevin Corrector for Stabilizing Latent Diffusion Inverse Problem Solvers","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"FlowDPS-Inverse/FlowDPS","path":"sd3_sampler.py","file_url":"https://github.com/FlowDPS-Inverse/FlowDPS/blob/HEAD/sd3_sampler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4f078fd69624c31f","mcp_get_code":{"code_sha256":"4f078fd69624c31f"}},{"arxiv_id":"2510.20872","paper":"/paper/arxiv-2510-20872","title":"MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"Alaleh/PDBO","path":"mobo/factory.py","file_url":"https://github.com/Alaleh/PDBO/blob/HEAD/mobo/factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"545970a789de193c","mcp_get_code":{"code_sha256":"545970a789de193c"}},{"arxiv_id":"2410.07838","paper":"/paper/minorityprompt-text-to-minority-image","title":"Minority-Focused Text-to-Image Generation via Prompt Optimization","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anonymous5293/minorityprompt","path":"latent_diffusion.py","file_url":"https://github.com/anonymous5293/minorityprompt/blob/HEAD/latent_diffusion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4f078fd69624c31f","mcp_get_code":{"code_sha256":"4f078fd69624c31f"}},{"arxiv_id":"2406.08799","paper":"/paper/pareto-front-diverse-batch-multi-objective","title":"Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alaleh/pdbo","path":"mobo/factory.py","file_url":"https://github.com/alaleh/pdbo/blob/HEAD/mobo/factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"545970a789de193c","mcp_get_code":{"code_sha256":"545970a789de193c"}},{"arxiv_id":"2406.07658","paper":"/paper/treeffuser-probabilistic-predictions-via","title":"Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees","date":"2024-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"blei-lab/treeffuser","path":"src/treeffuser/sde/base_solver.py","file_url":"https://github.com/blei-lab/treeffuser/blob/HEAD/src/treeffuser/sde/base_solver.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8646bb45df3df0e","mcp_get_code":{"code_sha256":"c8646bb45df3df0e"}},{"arxiv_id":"2210.08495","paper":"/paper/pareto-set-learning-for-expensive-multi","title":"Pareto Set Learning for Expensive Multi-Objective Optimization","date":"2022-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xi-l/psl-mobo","path":"mobo/factory.py","file_url":"https://github.com/xi-l/psl-mobo/blob/HEAD/mobo/factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"642bd668f157ee74","mcp_get_code":{"code_sha256":"642bd668f157ee74"}},{"arxiv_id":"2010.08666","paper":"/paper/active-domain-adaptation-via-clustering","title":"Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings","date":"2020-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"virajprabhu/clue","path":"adapt/solvers/solver.py","file_url":"https://github.com/virajprabhu/clue/blob/HEAD/adapt/solvers/solver.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a049409bc8f279e","mcp_get_code":{"code_sha256":"6a049409bc8f279e"}},{"arxiv_id":"Prabhu_Active_Domain_Adaptation_via_Clustering_Uncertainty-Weighted_Embeddings_ICCV_2021_paper","paper":null,"title":"arXiv:Prabhu_Active_Domain_Adaptation_via_Clustering_Uncertainty-Weighted_Embeddings_ICCV_2021_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"virajprabhu/CLUE","path":"adapt/solvers/solver.py","file_url":"https://github.com/virajprabhu/CLUE/blob/HEAD/adapt/solvers/solver.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a049409bc8f279e","mcp_get_code":{"code_sha256":"6a049409bc8f279e"}}]}