{"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-values","entry":"get_values","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":2,"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":2,"n_samples_fingerprinted":1,"n_places":6,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":4},"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":"2406.11911","paper":"/paper/a-notion-of-complexity-for-theory-of-mind-via","title":"A Notion of Complexity for Theory of Mind via Discrete World Models","date":"2024-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flecart/complexity-tom-dwm","path":"main/tot_prompt.py","file_url":"https://github.com/flecart/complexity-tom-dwm/blob/HEAD/main/tot_prompt.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d082501c44608619","mcp_get_code":{"code_sha256":"d082501c44608619"}},{"arxiv_id":"2310.04668","paper":"/paper/label-free-node-classification-on-graphs-with","title":"Label-free Node Classification on Graphs with Large Language Models (LLMS)","date":"2023-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pmernyei/wiki-cs-dataset","path":"data_processing/pyscripts/mysqldump_to_csv.py","file_url":"https://github.com/pmernyei/wiki-cs-dataset/blob/HEAD/data_processing/pyscripts/mysqldump_to_csv.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b9ee849ce40d859","mcp_get_code":{"code_sha256":"2b9ee849ce40d859"}},{"arxiv_id":"2307.12375","paper":"/paper/in-context-learning-in-large-language-models","title":"In-Context Learning Learns Label Relationships but Is Not Conventional Learning","date":"2023-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jlko/in_context_learning","path":"llm/plot_utils.py","file_url":"https://github.com/jlko/in_context_learning/blob/HEAD/llm/plot_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a7b6896a43973ffb","mcp_get_code":{"code_sha256":"a7b6896a43973ffb"}},{"arxiv_id":"2304.04782","paper":"/paper/reinforcement-learning-from-passive-data-via","title":"Reinforcement Learning from Passive Data via Latent Intentions","date":"2023-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dibyaghosh/icvf_release","path":"experiments/antmaze/train_icvf.py","file_url":"https://github.com/dibyaghosh/icvf_release/blob/HEAD/experiments/antmaze/train_icvf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a2628498c8976d46","mcp_get_code":{"code_sha256":"a2628498c8976d46"}},{"arxiv_id":"2206.01079","paper":"/paper/when-does-return-conditioned-supervised","title":"When does return-conditioned supervised learning work for offline reinforcement learning?","date":"2022-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davidbrandfonbrener/rcsl-paper","path":"jax_continuous_rl/jaxrl/networks/rvs_critics.py","file_url":"https://github.com/davidbrandfonbrener/rcsl-paper/blob/HEAD/jax_continuous_rl/jaxrl/networks/rvs_critics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"37e948f523448090","mcp_get_code":{"code_sha256":"37e948f523448090"}},{"arxiv_id":"2203.06904","paper":"/paper/delta-tuning-a-comprehensive-study-of","title":"Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models","date":"2022-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thunlp/opendelta","path":"opendelta/auto_delta.py","file_url":"https://github.com/thunlp/opendelta/blob/HEAD/opendelta/auto_delta.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"61752df4de80a655","mcp_get_code":{"code_sha256":"61752df4de80a655"}}]}