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get_result

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

get_result appears in the code Syntology harvested for 30 papers, as 31 distinct code bodies found in 40 places (a place is one code body under one paper). At least one of them ran in 18 of the papers; 3 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_result 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 18 of the 31 distinct code bodies named get_result; 13 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
0ran · fixture could not drive it
17ran
13unverified
3fingerprinted

Licence is a property of each copy, so it is counted per place: 11 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

30 papers shown of 30, 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 2 papers added by Syntology; 2 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
Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents added by Syntology 2026-06 (from id) snow10072740/Learning_from_Failure/run_autoglm.py fa90c9947264141c ran Apache-2.0 (permissive)
Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents added by Syntology 2026-06 (from id) snow10072740/Learning_from_Failure/run_autoglm_v.py 50f980bbd7348dea ran Apache-2.0 (permissive)
PCL-Reasoner-V1.5: Advancing Math Reasoning with Offline Reinforcement Learning added by Syntology 2026-01 (from id) PCL-Reasoner/V1.5/MindSpeed-LLM/evaluation.py ebe7ee2f07cfd847 unverified no licence file found · pointer only
Intent Factored Generation: Unleashing the Diversity in Your Language Model 11 Jun 2025 flairox/ifg/hendrycks_math/experiment_pipelines/hparam_sweeper.py adc1130833a4f725 unverified Apache-2.0 (permissive)
WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning 22 May 2025 weizhepei/webagent-r1/WebAgent-R1/Eval/score.py 5372a3148cae304b unverified Apache-2.0 (permissive)
Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora 10 Apr 2025 babylm/evaluation-pipeline/lm_eval/models/openai_completions.py 48fb64bd72378059 unverified MIT (permissive)
Attacking Vision-Language Computer Agents via Pop-ups 4 Nov 2024 SALT-NLP/PopupAttack/OSWorld/show_result.py 6474985b86ed175b unverified no licence file found · pointer only
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency 7 Oct 2024 zhqwqwq/Learning-Parity-with-CoT/Figures/Fig1/fig1.1.py 74f79a1e9c94f8ce ran MIT (permissive)
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency 7 Oct 2024 zhqwqwq/Learning-Parity-with-CoT/Figures/Fig1/fig1.2.py f933eda64995a961 ran MIT (permissive)
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency 7 Oct 2024 zhqwqwq/Learning-Parity-with-CoT/Figures/Fig10/fig10.py 51c4a892dac37e02 ran MIT (permissive)
From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency 7 Oct 2024 zhqwqwq/Learning-Parity-with-CoT/Figures/Fig3/fig3.1.py 7a1c0e1ab0c7c812 ran fingerprinted MIT (permissive)
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale 12 Sep 2024 microsoft/windowsagentarena/src/win-arena-container/client/show_result.py c84f9385b9ff870c ran fingerprinted MIT (permissive)
VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents 12 Aug 2024 thudm/visualagentbench/VAB-WebArena-Lite/new/score.py 5372a3148cae304b unverified Apache-2.0 (permissive)
CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks 20 Jun 2024 tsinghua-fib-lab/citybench/citybench/geoqa/metrics.py 7a47bc523075e6d4 ran MIT (permissive)
BlockPruner: Fine-grained Pruning for Large Language Models 15 Jun 2024 MrGGLS/BlockPruner/lm_eval/lm_eval/models/gguf.py 53354f082d9addc6 ran no licence file found · pointer only
BlockPruner: Fine-grained Pruning for Large Language Models 15 Jun 2024 MrGGLS/BlockPruner/lm_eval/lm_eval/models/gpt3.py d253589f2c78dad0 ran no licence file found · pointer only
EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning 13 May 2024 yinzhu-quan/lm-evaluation-harness/lm_eval/models/gguf.py 53354f082d9addc6 ran MIT (permissive)
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments 11 Apr 2024 xlang-ai/OSWorld/mm_agents/maestro/osworld_run_maestro.py 189a37dc14c3d30a ran Apache-2.0 (permissive)
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments 11 Apr 2024 xlang-ai/OSWorld/show_result.py 0f5d30c04a47f46a ran Apache-2.0 (permissive)
Distributionally Generative Augmentation for Fair Facial Attribute Classification 11 Mar 2024 heqianpei/DiGA/BYOL/train_LC.py 4d741352ba58146a ran no licence file found · pointer only
A Comprehensive Evaluation of Quantization Strategies for Large Language Models 26 Feb 2024 cordercorder/quant_eval/quant/SpQR/lm-evaluation-harness/lm_eval/models/gpt3.py d253589f2c78dad0 ran no licence file found · pointer only
SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security 26 Dec 2023 zefang-liu/lm-evaluation-harness/lm_eval/models/gguf.py 53354f082d9addc6 ran MIT (permissive)
SecQA: A Concise Question-Answering Dataset for Evaluating Large Language Models in Computer Security 26 Dec 2023 zefang-liu/lm-evaluation-harness/lm_eval/models/openai_completions.py 8cdc4b3572832c60 ran MIT (permissive)
DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents 16 Nov 2023 yale-nlp/docmath-eval/evaluation.py 87ea38e7e0bd8ee8 ran no licence file found · pointer only
FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains 16 Nov 2023 yale-nlp/knowledgemath/cot_evaluation_with_calculator.py f4f818c6f8c7f710 ran no licence file found · pointer only
FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains 16 Nov 2023 yale-nlp/knowledgemath/evaluation.py e7a7c7026476f66b ran no licence file found · pointer only
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback 29 Jul 2023 nlp-uoregon/mlmm-evaluation/lm_eval/models/gpt3.py d253589f2c78dad0 ran Apache-2.0 (permissive)
Do prompt positions really matter? 23 May 2023 milliemaoo/prompt-position/lm_eval/models/gpt3.py d253589f2c78dad0 ran MIT (permissive)
Evaluating GPT-3.5 and GPT-4 Models on Brazilian University Admission Exams 29 Mar 2023 piresramon/gpt-4-enem/lm_eval/models/chatgpt.py d253589f2c78dad0 ran MIT (permissive)
UAVM: Towards Unifying Audio and Visual Models 29 Jul 2022 YuanGongND/uavm/src/attention_diff/plt_att_diff.py 9ff67c442329e8a7 unverified BSD-2-Clause (permissive)
A Comparative Study of Graph Matching Algorithms in Computer Vision 1 Jul 2022 vislearn/gmbench/python/gmbench/analyzer/generate_table.py 698c78a80ac5ab35 unverified MIT (permissive)
A Few More Examples May Be Worth Billions of Parameters 8 Oct 2021 yuvalkirstain/lm-evaluation-harness/lm_eval/models/gpt3.py 65ed12119df56f3b unverified MIT (permissive)
Language Models are Few-Shot Learners 28 May 2020 neuralmagic/lm-evaluation-harness/lm_eval/models/gguf.py 53354f082d9addc6 ran MIT (permissive)
Language Models are Few-Shot Learners 28 May 2020 insait-institute/lm-evaluation-harness-bg/lm_eval/models/openai_completions.py 5decf8cec7692d54 unverified MIT (permissive)
Language Models are Few-Shot Learners 28 May 2020 vilm-ai/viet-llm-eval/lm_eval/models/openai_completions.py 89b22f3da55a855c unverified MIT (permissive)
Revisiting Membership Inference Under Realistic Assumptions 21 May 2020 bargavj/EvaluatingDPML/improved_ai/interpret_results.py 13622d0e53332de3 unverified MIT (permissive)
Listen, Attend and Spell 5 Aug 2015 msalhab96/Listen-Attend-and-Spell/inference.py 8dea3694156cfb07 ran · our draft was wrong fingerprinted no licence file found · pointer only
Fully Convolutional Networks for Semantic Segmentation 14 Nov 2014 YigeunLee/fcn32/fully_cnn.py 73d1eec357fb97af unverified no licence file found · pointer only
arXiv:2025.findings-emnlp.638 liyaooi/LongTableBench/pred.py 6cba06e249f97f39 unverified MIT (permissive)
arXiv:2025.findings-acl.744 szu-tera/RankedVotingSC/lm-evaluation-harness/lm_eval/models/gguf.py 53354f082d9addc6 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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