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load_csv

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

load_csv appears in the code Syntology harvested for 32 papers, as 30 distinct code bodies found in 40 places (a place is one code body under one paper). At least one of them ran in 13 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 load_csv 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 12 of the 30 distinct code bodies named load_csv; 18 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
5ran · our draft was wrong
0ran · fixture could not drive it
7ran
18unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 15 of the 40 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

32 papers shown of 32, newest first; 40 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 6 papers added by Syntology; 4 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
EfficientRollout: System-Aware Self-Speculative Decoding for RL Rollouts added by Syntology 2026-06 (from id) furiosa-ai/EfficientRollout/sd_toggle/fit.py 5ed7d80f88d8f831 ran Apache-2.0 (permissive)
Opportunistic Target Selection: Early Directional Commitment for Query-Efficient Black-Box Adversarial Attacks added by Syntology 2026-05 (from id) Tariolle/opportunistic-target-selection/analysis/analyze_lockmatch.py 7e89e69f7971931c ran MIT (permissive)
Scientific Theory of a Black-Box: A Life Cycle-Scale XAI Framework Based on Constructive Empiricism added by Syntology 2026-02 (from id) semueller/stobb_cobot/show_example.py d403a53e43a72df5 unverified no licence file found · pointer only
UniFinEval: Towards Unified Evaluation of Financial Multimodal Models across Text, Images and Videos added by Syntology 2026-01 (from id) aifinlab/UniFinEval/evaluate_py/data_loader.py 80031dd34b37c131 unverified Apache-2.0 (permissive)
Private Prediction via PAC Privacy added by Syntology 2026-01 (from id) zhxchd/pac/benchmark/pac_microbench/analyze_results.py 8ed50327556b3ce0 unverified AGPL-3.0 (copyleft) · pointer only
PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality added by Syntology 2025-09 (from id) hoeng4/PruneCD/1_factual_layer_search/tfqa_mc.py 2a4c0ecd291e1795 unverified no licence file found · pointer only
PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality added by Syntology 2025-09 (from id) hoeng4/PruneCD/2_benchmark/1_tfqa_gen.py a35a1be39e4bef73 unverified no licence file found · pointer only
PruneCD: Contrasting Pruned Self Model to Improve Decoding Factuality added by Syntology 2025-09 (from id) hoeng4/PruneCD/2_benchmark/3_4_triviaqa_natural_questions.py 872f1d147b70aced unverified no licence file found · pointer only
Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation 3 Jun 2025 CuSO4-Chen/PLI/src/evaluation/truthfulqa_eval.py 2a4c0ecd291e1795 unverified MIT (permissive)
arXiv:2504.04635 2025-04 (from id) patqdasilva/steering-off-course/DoLa/factor_eval.py 78392b8414df821e ran · our draft was wrong MIT (permissive)
arXiv:2504.04635 2025-04 (from id) patqdasilva/steering-off-course/DoLa/tfqa_mc_eval.py 2a4c0ecd291e1795 unverified MIT (permissive)
arXiv:2504.04635 2025-04 (from id) patqdasilva/steering-off-course/DoLa/tfqa_eval.py a35a1be39e4bef73 unverified MIT (permissive)
SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models 1 Nov 2024 JayZhang42/SLED/utils/utils_factor.py 78392b8414df821e ran · our draft was wrong no licence file found · pointer only
Normalization Layer Per-Example Gradients are Sufficient to Predict Gradient Noise Scale in Transformers 1 Nov 2024 cerebrasresearch/nanogns/exact/csv_tools.py f3552f440f4a76f3 unverified no licence file found · pointer only
BenTo: Benchmark Task Reduction with In-Context Transferability 17 Oct 2024 tianyi-lab/bento/benchmark-reduction/analysis_compare_methods_.py a4439ebb3e6536fd ran Apache-2.0 (permissive)
BenTo: Benchmark Task Reduction with In-Context Transferability 17 Oct 2024 tianyi-lab/bento/benchmark-reduction/analysis_compare_methods_flan.py 1b7a6ef6b06e147c ran Apache-2.0 (permissive)
BenTo: Benchmark Task Reduction with In-Context Transferability 17 Oct 2024 tianyi-lab/bento/benchmark-reduction/analysis.py 5caec032cc3549d1 ran Apache-2.0 (permissive)
U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models 2 Oct 2024 tony10101105/ExpEmergence/evaluation/abstract_narrative_understanding/abstract_narrative_understanding_question_grouping.py dbdc88cccaa015e1 ran MIT (permissive)
Improving Factuality in Large Language Models via Decoding-Time Hallucinatory and Truthful Comparators 22 Aug 2024 ydk122024/cdt/src/benchmark_evaluation/truthfulqa_eval.py 2a4c0ecd291e1795 unverified MIT (permissive)
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation 3 Mar 2024 hkust-nlp/activation_decoding/eval_tqa.py 297c84ffca2864db ran · our draft was wrong no licence file found · pointer only
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully 11 Jan 2024 0-kaikai-0/sh2/tfqa_keys.py 2a4c0ecd291e1795 unverified no licence file found · pointer only
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully 11 Jan 2024 0-kaikai-0/sh2/factor_eval.py c47f09f5ffb69e69 unverified no licence file found · pointer only
SH2: Self-Highlighted Hesitation Helps You Decode More Truthfully 11 Jan 2024 0-kaikai-0/sh2/tfqa_mc_eval.py 91ad5d3cac5d67de unverified no licence file found · pointer only
Alleviating Hallucinations of Large Language Models through Induced Hallucinations 25 Dec 2023 hillzhang1999/icd/src/benchmark_evaluation/truthfulqa_eval.py 2a4c0ecd291e1795 unverified MIT (permissive)
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers 7 Dec 2023 nuochenpku/llama_analysis/factural_eval.py 2a4c0ecd291e1795 unverified no licence file found · pointer only
Corrupting Neuron Explanations of Deep Visual Features 25 Oct 2023 Trustworthy-ML-Lab/corrupting_neuron_explanations/network-dissection/loader/data_loader.py c8b1a48d7aa8bf6a ran MIT (permissive)
DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models 7 Sep 2023 voidism/DoLa/factor_eval.py 78392b8414df821e ran · our draft was wrong no licence file found · pointer only
Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblocks 30 Jun 2023 balevinstein/probes/Train_CCSProbe.py 490da4aec73c34bb ran · our draft was wrong MIT (permissive)
Are we certain it's anomalous? 16 Nov 2022 aleflabo/HypAD/utils/data.py 7996920fef9af9d0 unverified MIT (permissive)
Are Large Pre-Trained Language Models Leaking Your Personal Information? 25 May 2022 jeffhj/lm_personalinfoleak/pred.py 16b6899a69cd97b7 unverified Apache-2.0 (permissive)
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series 16 Feb 2022 D3-AI/Orion/orion/data.py 04aff2c366db2eed unverified MIT (permissive)
Modeling Human Exploration Through Resource-Rational Reinforcement Learning 27 Jan 2022 marcelbinz/resource-rational-reinforcement-learning/utils.py e19e1433392eca4e ran · our draft was wrong no licence file found · pointer only
Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection 14 Jul 2021 TaoRuijie/TalkNet_ASD/utils/get_ava_active_speaker_performance.py bdc52ecccc77b9e1 unverified MIT (permissive)
Neural Distributed Source Coding 5 Jun 2021 acnagle/neural-dsc/plot_rd_curves.py 9a3b377007d57821 unverified MIT (permissive)
Unsupervised Learning from Video with Deep Neural Embeddings 28 May 2019 neuroailab/VIE/build_data/kinetics/download_videos.py 03177c36e1973e33 ran · our draft was wrong no licence file found · pointer only
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders 17 Jan 2019 emilemathieu/pvae/pvae/datasets/datasets.py a8eb4a941274e36a unverified MIT (permissive)
Deep Reinforcement Learning for Imbalanced Classification 5 Jan 2019 Denbergvanthijs/imbDRL/imbDRL/data.py 692b71a43ff1f48c unverified Apache-2.0 (permissive)
arXiv:aaai_6793 activitynet/ActivityNet/Evaluation/get_ava_active_speaker_performance.py 2b0fca044dd4be81 unverified MIT (permissive)
arXiv:aaai_19900 cg1177/DCAN/dcan_anet/Eval/get_ava_active_speaker_performance.py c277623807229f9f unverified Apache-2.0 (permissive)
arXiv:2024.findings-naacl.294 balevinstein/Probes/Train_CCSProbe.py 490da4aec73c34bb ran · our draft was wrong 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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections