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get_sample_size

Syntologyentry name in harvested coderead from the graph 2026-09-24

get_sample_size appears in the code Syntology harvested for 16 papers, as 7 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 15 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named get_sample_size do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 3 of the 7 distinct code bodies named get_sample_size; 4 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
1ran · fixture could not drive it
2ran
4unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 of the 20 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

16 papers shown of 16, newest first; 20 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 8 papers added by Syntology; 1 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Fine-Tuning of Transformer models with Frames added by Syntology 2026-08 (from id) vsingh-group/FrameFT/lm-evaluation-harness/lm_eval/evaluator_utils.py 00f5e7d9ee6636ba ran MIT (permissive)
3D-Aware VLMs with Implicit and Explicit Geometries added by Syntology 2026-07 (from id) Vegetebird/VLM-IE3D/src/lmms_eval/evaluator_utils.py 00f5e7d9ee6636ba ran MIT (permissive)
MEMORYCARD: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering added by Syntology 2026-06 (from id) NEUIR/MemoryCard/lmms_eval/evaluator_utils.py 00f5e7d9ee6636ba ran MIT (permissive)
Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning added by Syntology 2026-02 (from id) LivingFutureLab/CaCoVID/lmms_eval/lmms_eval/evaluator_utils.py 00f5e7d9ee6636ba ran no licence file found · pointer only
Contribution-aware Token Compression for Efficient Video Understanding via Reinforcement Learning added by Syntology 2026-02 (from id) EvolvingLMMs-Lab/lmms-eval/lmms_eval/evaluator_utils.py f827e5b576859cb9 unverified licence not identified · pointer only
ρ-EOS: Training-free Bidirectional Variable-Length Control for Masked Diffusion LLMs added by Syntology 2026-01 (from id) yjyddq/rho-EOS/dllm_eval/evaluator_utils.py 00f5e7d9ee6636ba ran Apache-2.0 (permissive)
PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs added by Syntology 2026-01 (from id) wzj1718/PlaM/Vision-Token-Masking/lmms_eval/evaluator_utils.py 00f5e7d9ee6636ba ran no licence file found · pointer only
CD 4 LM: Consistency Distillation and aDaptive Decoding for Diffusion Language Models added by Syntology 2026-01 (from id) yihao-liang/CDLM/evaluation/dllm_eval/evaluator_utils.py 00f5e7d9ee6636ba ran Apache-2.0 (permissive)
Less Is More, but Where? Dynamic Token Compression via LLM-Guided Keyframe Prior added by Syntology 2025-12 (from id) yu-lin-li/DyToK/eval/lmms_eval/evaluator_utils.py 00f5e7d9ee6636ba ran Apache-2.0 (permissive)
s1: Simple test-time scaling 31 Jan 2025 simplescaling/s1/eval/lm-evaluation-harness/lm_eval/evaluator_utils.py 00f5e7d9ee6636ba ran Apache-2.0 (permissive)
Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces 18 Dec 2024 vision-x-nyu/thinking-in-space/lmms_eval/evaluator_utils.py 00f5e7d9ee6636ba ran Apache-2.0 (permissive)
EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning 13 May 2024 yinzhu-quan/lm-evaluation-harness/lm_eval/evaluator_utils.py 5b315604fb608f3b ran MIT (permissive)
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills 23 May 2023 qiushisun/transcoder/learner.py 3824baa1aca5bb25 unverified MIT (permissive)
Language Models are Few-Shot Learners 28 May 2020 Sypherd/lm-evaluation-harness/lm_eval/evaluator_utils.py 00f5e7d9ee6636ba ran MIT (permissive)
Language Models are Few-Shot Learners 28 May 2020 opengptx/lm-evaluation-harness/lm_eval/evaluator_utils.py 5b315604fb608f3b ran MIT (permissive)
Language Models are Few-Shot Learners 28 May 2020 neuralmagic/lm-evaluation-harness/lm_eval/evaluator_utils.py 0db35c379a280bd8 unverified MIT (permissive)
Language Models are Few-Shot Learners 28 May 2020 juletx/lm-evaluation-harness/lm_eval/evaluator_utils.py 0fb487fd2495f33f unverified MIT (permissive)
Chebyshev polynomials, moment matching, and optimal estimation of the unseen 2015-04 (from id) Albuso0/support/support.py 3c21a2aea30fbfce ran · fixture could not drive it no licence file found · pointer only
Minimax rates of entropy estimation on large alphabets via best polynomial approximation 2014-07 (from id) identical code first harvested elsewhere 3c21a2aea30fbfce ran · fixture could not drive it licence of this copy not recorded
arXiv:2025.findings-acl.744 szu-tera/RankedVotingSC/lm-evaluation-harness/lm_eval/evaluator_utils.py 00f5e7d9ee6636ba ran MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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