{"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-sample-size","entry":"get_sample_size","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":16,"n_papers_ran":15,"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":3,"n_samples_fingerprinted":0,"n_places":20,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":2,"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":"2608.26430","paper":"/paper/arxiv-2608-26430","title":"Fine-Tuning of Transformer models with Frames","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"vsingh-group/FrameFT","path":"lm-evaluation-harness/lm_eval/evaluator_utils.py","file_url":"https://github.com/vsingh-group/FrameFT/blob/HEAD/lm-evaluation-harness/lm_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2607.21595","paper":"/paper/arxiv-2607-21595","title":"3D-Aware VLMs with Implicit and Explicit Geometries","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"Vegetebird/VLM-IE3D","path":"src/lmms_eval/evaluator_utils.py","file_url":"https://github.com/Vegetebird/VLM-IE3D/blob/HEAD/src/lmms_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2606.05917","paper":"/paper/arxiv-2606-05917","title":"MEMORYCARD: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"NEUIR/MemoryCard","path":"lmms_eval/evaluator_utils.py","file_url":"https://github.com/NEUIR/MemoryCard/blob/HEAD/lmms_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2602.01649","paper":"/paper/arxiv-2602-01649","title":"Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"LivingFutureLab/CaCoVID","path":"lmms_eval/lmms_eval/evaluator_utils.py","file_url":"https://github.com/LivingFutureLab/CaCoVID/blob/HEAD/lmms_eval/lmms_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2602.01649","paper":"/paper/arxiv-2602-01649","title":"Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"EvolvingLMMs-Lab/lmms-eval","path":"lmms_eval/evaluator_utils.py","file_url":"https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/HEAD/lmms_eval/evaluator_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f827e5b576859cb9","mcp_get_code":{"code_sha256":"f827e5b576859cb9"}},{"arxiv_id":"2601.22527","paper":"/paper/arxiv-2601-22527","title":"ρ-EOS: Training-free Bidirectional Variable-Length Control for Masked Diffusion LLMs","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"yjyddq/rho-EOS","path":"dllm_eval/evaluator_utils.py","file_url":"https://github.com/yjyddq/rho-EOS/blob/HEAD/dllm_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2601.07645","paper":"/paper/arxiv-2601-07645","title":"PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"wzj1718/PlaM","path":"Vision-Token-Masking/lmms_eval/evaluator_utils.py","file_url":"https://github.com/wzj1718/PlaM/blob/HEAD/Vision-Token-Masking/lmms_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2601.02236","paper":"/paper/arxiv-2601-02236","title":"CD 4 LM: Consistency Distillation and aDaptive Decoding for Diffusion Language Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"yihao-liang/CDLM","path":"evaluation/dllm_eval/evaluator_utils.py","file_url":"https://github.com/yihao-liang/CDLM/blob/HEAD/evaluation/dllm_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2512.06866","paper":"/paper/arxiv-2512-06866","title":"Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Prior","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"yu-lin-li/DyToK","path":"eval/lmms_eval/evaluator_utils.py","file_url":"https://github.com/yu-lin-li/DyToK/blob/HEAD/eval/lmms_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2501.19393","paper":"/paper/s1-simple-test-time-scaling","title":"s1: Simple test-time scaling","date":"2025-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"simplescaling/s1","path":"eval/lm-evaluation-harness/lm_eval/evaluator_utils.py","file_url":"https://github.com/simplescaling/s1/blob/HEAD/eval/lm-evaluation-harness/lm_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2412.14171","paper":"/paper/thinking-in-space-how-multimodal-large","title":"Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces","date":"2024-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vision-x-nyu/thinking-in-space","path":"lmms_eval/evaluator_utils.py","file_url":"https://github.com/vision-x-nyu/thinking-in-space/blob/HEAD/lmms_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2405.07938","paper":"/paper/econlogicqa-a-question-answering-benchmark","title":"EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning","date":"2024-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yinzhu-quan/lm-evaluation-harness","path":"lm_eval/evaluator_utils.py","file_url":"https://github.com/yinzhu-quan/lm-evaluation-harness/blob/HEAD/lm_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5b315604fb608f3b","mcp_get_code":{"code_sha256":"5b315604fb608f3b"}},{"arxiv_id":"2306.07285","paper":"/paper/transcoder-towards-unified-transferable-code","title":"TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qiushisun/transcoder","path":"learner.py","file_url":"https://github.com/qiushisun/transcoder/blob/HEAD/learner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3824baa1aca5bb25","mcp_get_code":{"code_sha256":"3824baa1aca5bb25"}},{"arxiv_id":"2005.14165","paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sypherd/lm-evaluation-harness","path":"lm_eval/evaluator_utils.py","file_url":"https://github.com/Sypherd/lm-evaluation-harness/blob/HEAD/lm_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}},{"arxiv_id":"2005.14165","paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengptx/lm-evaluation-harness","path":"lm_eval/evaluator_utils.py","file_url":"https://github.com/opengptx/lm-evaluation-harness/blob/HEAD/lm_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5b315604fb608f3b","mcp_get_code":{"code_sha256":"5b315604fb608f3b"}},{"arxiv_id":"2005.14165","paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"neuralmagic/lm-evaluation-harness","path":"lm_eval/evaluator_utils.py","file_url":"https://github.com/neuralmagic/lm-evaluation-harness/blob/HEAD/lm_eval/evaluator_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0db35c379a280bd8","mcp_get_code":{"code_sha256":"0db35c379a280bd8"}},{"arxiv_id":"2005.14165","paper":"/paper/language-models-are-few-shot-learners","title":"Language Models are Few-Shot Learners","date":"2020-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juletx/lm-evaluation-harness","path":"lm_eval/evaluator_utils.py","file_url":"https://github.com/juletx/lm-evaluation-harness/blob/HEAD/lm_eval/evaluator_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0fb487fd2495f33f","mcp_get_code":{"code_sha256":"0fb487fd2495f33f"}},{"arxiv_id":"1504.01227","paper":"/paper/chebyshev-polynomials-moment-matching-and","title":"Chebyshev polynomials, moment matching, and optimal estimation of the unseen","date":null,"month_inferred_from_arxiv_id":"2015-04","title_source":"archive","repo":"Albuso0/support","path":"support.py","file_url":"https://github.com/Albuso0/support/blob/HEAD/support.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3c21a2aea30fbfce","mcp_get_code":{"code_sha256":"3c21a2aea30fbfce"}},{"arxiv_id":"1407.0381","paper":"/paper/minimax-rates-of-entropy-estimation-on-large","title":"Minimax rates of entropy estimation on large alphabets via best polynomial approximation","date":null,"month_inferred_from_arxiv_id":"2014-07","title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"3c21a2aea30fbfce","mcp_get_code":{"code_sha256":"3c21a2aea30fbfce"}},{"arxiv_id":"2025.findings-acl.744","paper":null,"title":"arXiv:2025.findings-acl.744","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"szu-tera/RankedVotingSC","path":"lm-evaluation-harness/lm_eval/evaluator_utils.py","file_url":"https://github.com/szu-tera/RankedVotingSC/blob/HEAD/lm-evaluation-harness/lm_eval/evaluator_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00f5e7d9ee6636ba","mcp_get_code":{"code_sha256":"00f5e7d9ee6636ba"}}]}