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ensure_valid_input

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

ensure_valid_input appears in the code Syntology harvested for 15 papers, as 2 distinct code bodies found in 15 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 ensure_valid_input 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 2 of the 2 distinct code bodies named ensure_valid_input; 0 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
0ran · fixture could not drive it
2ran
0unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 7 of the 15 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

15 papers shown of 15, newest first; 15 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; 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
Looped Transformers for Length Generalization 24 Sep 2024 UW-Madison-Lee-Lab/looped-tf/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran no licence file found · pointer only
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning 1 Jul 2024 LINs-lab/CapaBoost/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran Apache-2.0 (permissive)
Learning Syntax Without Planting Trees: Understanding When and Why Transformers Generalize Hierarchically 25 Apr 2024 kabirahuja2431/transformers-hg/transformers/src/transformers/convert_graph_to_onnx.py b220e701be05a184 ran no licence file found · pointer only
Improving Open-Ended Text Generation via Adaptive Decoding 28 Feb 2024 zwhong714/adaptive_decoding/transformers-main/src/transformers/convert_graph_to_onnx.py b220e701be05a184 ran no licence file found · pointer only
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning 22 Feb 2024 johnathan-xie/sma/src/transformers/convert_graph_to_onnx.py b220e701be05a184 ran Apache-2.0 (permissive)
Token-wise Decomposition of Autoregressive Language Model Hidden States for Analyzing Model Predictions 17 May 2023 byungdoh/llm_decomposition/huggingface/src/transformers/convert_graph_to_onnx.py b220e701be05a184 ran Apache-2.0 (permissive)
Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisal 21 Dec 2022 byungdoh/attn_dist/huggingface/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran Apache-2.0 (permissive)
AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization 2 Dec 2022 bhargaviparanjape/robust-transformers/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran Apache-2.0 recorded; this copy not marked cleared · pointer only
EvEntS ReaLM: Event Reasoning of Entity States via Language Models 10 Nov 2022 spilioeve/eventsrealm/transformers-single-all-attribute-prompt-experiments/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran MIT (permissive)
Locally Typical Sampling 1 Feb 2022 cimeister/typical-sampling/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran Apache-2.0 recorded; this copy not marked cleared · pointer only
Attention Approximates Sparse Distributed Memory 10 Nov 2021 trentbrick/attention-approximates-sdm/HugFace/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran MIT (permissive)
DSEE: Dually Sparsity-embedded Efficient Tuning of Pre-trained Language Models 30 Oct 2021 vita-group/dsee/non-GPT-2/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran MIT (permissive)
Learned Token Pruning for Transformers 2 Jul 2021 kssteven418/ltp/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran Apache-2.0 recorded; this copy not marked cleared · pointer only
AdapterHub: A Framework for Adapting Transformers 15 Jul 2020 mklimasz/language-arithmetic/src/transformers/convert_graph_to_onnx.py b220e701be05a184 ran Apache-2.0 recorded; this copy not marked cleared · pointer only
arXiv:2022.naacl-main.130 parovicm/BADX/src/transformers/convert_graph_to_onnx.py d770e1dd1d799a47 ran Apache-2.0 (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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