{"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/generation","entry":"generation","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":9,"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":7,"n_samples_ran":6,"n_samples_fingerprinted":1,"n_places":10,"n_places_pointer_only":7,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":3,"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":"2606.05183","paper":"/paper/arxiv-2606-05183","title":"The Granularity Gap: A Multi-Dimensional Cross-Generational Audit of Sycophancy in Gemini Models","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"pskeough/The-Granularity-Gap","path":"pipeline/00_build_sample.py","file_url":"https://github.com/pskeough/The-Granularity-Gap/blob/HEAD/pipeline/00_build_sample.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f2b3b8f94cfdd5e8","mcp_get_code":{"code_sha256":"f2b3b8f94cfdd5e8"}},{"arxiv_id":"2505.05242","paper":"/paper/enhancing-treatment-effect-estimation-via","title":"Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective","date":"2025-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uqhwen2/fccm","path":"CMNIST/fccm.py","file_url":"https://github.com/uqhwen2/fccm/blob/HEAD/CMNIST/fccm.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"edcc2cb680388d24","mcp_get_code":{"code_sha256":"edcc2cb680388d24"}},{"arxiv_id":"2503.00522","paper":"/paper/periodic-materials-generation-using-text","title":"Periodic Materials Generation using Text-Guided Joint Diffusion Model","date":"2025-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kdmsit/TGDMat","path":"csp_task/evaluate.py","file_url":"https://github.com/kdmsit/TGDMat/blob/HEAD/csp_task/evaluate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5d418b6dcded34ba","mcp_get_code":{"code_sha256":"5d418b6dcded34ba"}},{"arxiv_id":"2312.10763","paper":"/paper/m3dbench-let-s-instruct-large-models-with","title":"M3DBench: Let's Instruct Large Models with Multi-modal 3D Prompts","date":"2023-12-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenM3D/M3DBench","path":"models/llm_decoder/generation_utils.py","file_url":"https://github.com/OpenM3D/M3DBench/blob/HEAD/models/llm_decoder/generation_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ffbc06852635802f","mcp_get_code":{"code_sha256":"ffbc06852635802f"}},{"arxiv_id":"2311.18651","paper":"/paper/ll3da-visual-interactive-instruction-tuning","title":"LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planning","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"open3da/ll3da","path":"models/ll3da/generation_utils.py","file_url":"https://github.com/open3da/ll3da/blob/HEAD/models/ll3da/generation_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ffbc06852635802f","mcp_get_code":{"code_sha256":"ffbc06852635802f"}},{"arxiv_id":"2311.12904","paper":"/paper/learning-to-compute-grobner-bases","title":"Learning to Compute Gröbner Bases","date":"2023-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HiroshiKERA/transformer-groebner","path":"src/evaluation/generation.py","file_url":"https://github.com/HiroshiKERA/transformer-groebner/blob/HEAD/src/evaluation/generation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f469b8d56291d428","mcp_get_code":{"code_sha256":"f469b8d56291d428"}},{"arxiv_id":"2308.02165","paper":"/paper/diffusion-probabilistic-models-enhance","title":"Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling","date":"2023-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"2c52dde33acf320d","mcp_get_code":{"code_sha256":"2c52dde33acf320d"}},{"arxiv_id":"2302.08981","paper":"/paper/black-box-batch-active-learning-for","title":"Black-Box Batch Active Learning for Regression","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"edcc2cb680388d24","mcp_get_code":{"code_sha256":"edcc2cb680388d24"}},{"arxiv_id":"2302.08981","paper":"/paper/black-box-batch-active-learning-for","title":"Black-Box Batch Active Learning for Regression","date":"2023-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uqhwen2/MACAL","path":"TOY/ExactGPR_Sim.py","file_url":"https://github.com/uqhwen2/MACAL/blob/HEAD/TOY/ExactGPR_Sim.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c7e225a53d449976","mcp_get_code":{"code_sha256":"c7e225a53d449976"}},{"arxiv_id":"2110.06197","paper":"/paper/crystal-diffusion-variational-autoencoder-for-1","title":"Crystal Diffusion Variational Autoencoder for Periodic Material Generation","date":"2021-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"txie-93/cdvae","path":"scripts/evaluate.py","file_url":"https://github.com/txie-93/cdvae/blob/HEAD/scripts/evaluate.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":"2c52dde33acf320d","mcp_get_code":{"code_sha256":"2c52dde33acf320d"}}]}