{"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-fname","entry":"get_fname","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":5,"n_papers_ran":5,"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":5,"n_samples_fingerprinted":2,"n_places":5,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":4,"unverified":0},"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":"2405.20543","paper":"/paper/towards-a-general-gnn-framework-for","title":"Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenkelf/copt","path":"graphgym/configs_gen.py","file_url":"https://github.com/wenkelf/copt/blob/HEAD/graphgym/configs_gen.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f22df9b4f0f5f694","mcp_get_code":{"code_sha256":"f22df9b4f0f5f694"}},{"arxiv_id":"2402.11821","paper":"/paper/microstructures-and-accuracy-of-graph-recall","title":"Microstructures and Accuracy of Graph Recall by Large Language Models","date":"2024-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abel0828/llm-graph-recall","path":"network_recall.py","file_url":"https://github.com/abel0828/llm-graph-recall/blob/HEAD/network_recall.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"11cba458079c4ef0","mcp_get_code":{"code_sha256":"11cba458079c4ef0"}},{"arxiv_id":"2312.04693","paper":"/paper/graphmetro-mitigating-complex-distribution","title":"GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts","date":"2023-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wuyxin/graphmetro","path":"graphmetro/config.py","file_url":"https://github.com/wuyxin/graphmetro/blob/HEAD/graphmetro/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00c77e7f9cc8177c","mcp_get_code":{"code_sha256":"00c77e7f9cc8177c"}},{"arxiv_id":"2312.01537","paper":"/paper/unlocking-the-potential-of-federated-learning-1","title":"Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents","date":"2023-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"feddg23/feddg-main","path":"fed_main.py","file_url":"https://github.com/feddg23/feddg-main/blob/HEAD/fed_main.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c82bc162f7258c96","mcp_get_code":{"code_sha256":"c82bc162f7258c96"}},{"arxiv_id":"2309.06642","paper":"/paper/adapt-and-diffuse-sample-adaptive","title":"Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models","date":"2023-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"z-fabian/flash-diffusion","path":"data_utils/image_data.py","file_url":"https://github.com/z-fabian/flash-diffusion/blob/HEAD/data_utils/image_data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f8dee44cef535c69","mcp_get_code":{"code_sha256":"f8dee44cef535c69"}}]}