{"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/generate-input","entry":"generate_input","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":6,"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":6,"n_samples_ran":5,"n_samples_fingerprinted":1,"n_places":7,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":5,"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":"2410.12409","paper":"/paper/revealing-the-barriers-of-language-agents-in","title":"Revealing the Barriers of Language Agents in Planning","date":"2024-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hsaest/Agent-Planning-Analysis","path":"code/AttrScoreCalc/blocksWorld.py","file_url":"https://github.com/hsaest/Agent-Planning-Analysis/blob/HEAD/code/AttrScoreCalc/blocksWorld.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fed79a834dc4ccbc","mcp_get_code":{"code_sha256":"fed79a834dc4ccbc"}},{"arxiv_id":"2410.08339","paper":"/paper/simultaneous-weight-and-architecture","title":"Simultaneous Weight and Architecture Optimization for Neural Networks","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zitonghuangcynthia/Simultaneous-Weight-and-Architecture-Optimization","path":"training_autoencoder/data.py","file_url":"https://github.com/zitonghuangcynthia/Simultaneous-Weight-and-Architecture-Optimization/blob/HEAD/training_autoencoder/data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"249b88fd9aa9b61b","mcp_get_code":{"code_sha256":"249b88fd9aa9b61b"}},{"arxiv_id":"2410.08339","paper":"/paper/simultaneous-weight-and-architecture","title":"Simultaneous Weight and Architecture Optimization for Neural Networks","date":"2024-10-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zitonghuangcynthia/Simultaneous-Weight-and-Architecture-Optimization","path":"training_MLP/create_dataset/data.py","file_url":"https://github.com/zitonghuangcynthia/Simultaneous-Weight-and-Architecture-Optimization/blob/HEAD/training_MLP/create_dataset/data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ef7254b3e714e9db","mcp_get_code":{"code_sha256":"ef7254b3e714e9db"}},{"arxiv_id":"2407.15352","paper":"/paper/maven-fact-a-large-scale-event-factuality","title":"MAVEN-Fact: A Large-scale Event Factuality Detection Dataset","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THU-KEG/MAVEN-FACT","path":"trainEFD/llm.py","file_url":"https://github.com/THU-KEG/MAVEN-FACT/blob/HEAD/trainEFD/llm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"489f90e4435198b1","mcp_get_code":{"code_sha256":"489f90e4435198b1"}},{"arxiv_id":"2405.18166","paper":"/paper/defending-large-language-models-against-2","title":"Defending Large Language Models Against Jailbreak Attacks via Layer-specific Editing","date":"2024-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ledllm/ledllm","path":"utils/utils.py","file_url":"https://github.com/ledllm/ledllm/blob/HEAD/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f465ce5300a3f4d","mcp_get_code":{"code_sha256":"6f465ce5300a3f4d"}},{"arxiv_id":"2312.07876","paper":"/paper/causality-analysis-for-evaluating-the","title":"Causality Analysis for Evaluating the Security of Large Language Models","date":"2023-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"casperllm/casper","path":"utils/utils.py","file_url":"https://github.com/casperllm/casper/blob/HEAD/utils/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6f465ce5300a3f4d","mcp_get_code":{"code_sha256":"6f465ce5300a3f4d"}},{"arxiv_id":"2305.12524","paper":"/paper/theoremqa-a-theorem-driven-question-answering","title":"TheoremQA: A Theorem-driven Question Answering dataset","date":"2023-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"THUDM/VisualGLM-6B","path":"model/infer_util.py","file_url":"https://github.com/THUDM/VisualGLM-6B/blob/HEAD/model/infer_util.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":"761f8ed7104063cc","mcp_get_code":{"code_sha256":"761f8ed7104063cc"}}]}