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format_example

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

format_example appears in the code Syntology harvested for 43 papers, as 34 distinct code bodies found in 50 places (a place is one code body under one paper). At least one of them ran in 33 of the papers; 2 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 format_example 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 20 of the 34 distinct code bodies named format_example; 14 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
9ran · our draft was wrong
0ran · fixture could not drive it
11ran
14unverified
2fingerprinted

Licence is a property of each copy, so it is counted per place: 19 of the 50 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

43 papers shown of 43, newest first; 50 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 5 papers added by Syntology; 3 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
Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs added by Syntology 2026-05 (from id) chenxinrui-tsinghua/LinearPatch/eval/mmlu_eval.py cd763eaf1ac287e7 ran · our draft was wrong MIT (permissive)
REAM: Merging Improves Pruning of Experts in LLMs added by Syntology 2026-04 (from id) zai-org/glm-simple-evals/evals/mmlu_pro_eval.py 24ff49e5a1d92207 unverified no licence file found · pointer only
SnapMLA: Efficient Long-Context MLA Decoding via Hardware-Aware FP8 Quantized Pipelining added by Syntology 2026-02 (from id) meituan-longcat/SGLang-FluentLLM/benchmark/mmlu/bench_sglang.py cd763eaf1ac287e7 ran · our draft was wrong Apache-2.0 (permissive)
MRAG: Benchmarking Retrieval-Augmented Generation for Bio-medicine added by Syntology 2026-01 (from id) hendrycks/test/evaluate.py cd763eaf1ac287e7 ran · our draft was wrong MIT (permissive)
When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling WHEN TO ENSEMBLE: IDENTIFYING TOKEN-LEVEL POINTS FOR STABLE AND FAST LLM ENSEMBLING added by Syntology 2025-10 (from id) yoon6503/SAFE/run_safe.py c8947f39a6f3b186 unverified no licence file found · pointer only
When to Ensemble: Identifying Token-Level Points for Stable and Fast LLM Ensembling WHEN TO ENSEMBLE: IDENTIFYING TOKEN-LEVEL POINTS FOR STABLE AND FAST LLM ENSEMBLING added by Syntology 2025-10 (from id) yoon6503/SAFE/run_safe_mmlu.py 914abaca965ea62f unverified no licence file found · pointer only
arXiv:2507.11953 2025-07 (from id) QwenLM/Qwen/eval/evaluate_ceval.py 15a59658a1a9f53d unverified Apache-2.0 (permissive)
When Large Multimodal Models Confront Evolving Knowledge:Challenges and Pathways 30 May 2025 pjlab-sys4nlp/llama-moe/smoe/entrypoint/eval/eval_mmlu_moe_0.py cd763eaf1ac287e7 ran · our draft was wrong Apache-2.0 (permissive)
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator 22 May 2025 ml-stat-Sustech/Disagreement-Aware-Calibration/common/datasets.py 718d1ba9049ace00 unverified MIT (permissive)
Disentangling Memory and Reasoning Ability in Large Language Models 20 Nov 2024 mingyuj666/disentangling-memory-and-reasoning/load_data/data_agent.py b82b37c35d49aafd unverified no licence file found · pointer only
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment 28 Oct 2024 AboveParadise/LLMCBench/evaluate_mmlu.py cd763eaf1ac287e7 ran · our draft was wrong MIT (permissive)
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment 28 Oct 2024 AboveParadise/LLMCBench/evaluate_advglue.py 984d3567b5126f65 unverified MIT (permissive)
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment 28 Oct 2024 AboveParadise/LLMCBench/evaluate_mnli.py 7538653cf2565390 unverified MIT (permissive)
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment 28 Oct 2024 AboveParadise/LLMCBench/evaluate_qnli.py dafa37632f3f91bf unverified MIT (permissive)
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models 28 Oct 2024 KL4805/ShoppingMMLU/task_wise_eval/utils.py c78d363cf62ef64c unverified Apache-2.0 (permissive)
Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning 5 Oct 2024 liugangcode/Llamole/launch.py 3c781f40e814fc9a ran Apache-2.0 (permissive)
MMLU-Pro+: Evaluating Higher-Order Reasoning and Shortcut Learning in LLMs 3 Sep 2024 asgsaeid/mmlu-pro-plus/evaluate_from_api.py df80a0e9f84176ff ran · our draft was wrong fingerprinted no licence file found · pointer only
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference 19 Aug 2024 identical code first harvested elsewhere cd763eaf1ac287e7 ran · our draft was wrong licence of this copy not recorded
Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering 15 Aug 2024 Oxford-AI-for-Society/human-learning-strategies/training/fine_tuning/shared_utils.py 6ea1d20b1706f31c ran GPL-3.0 (copyleft) · pointer only
A deeper look at depth pruning of LLMs 23 Jul 2024 shoaibahmed/llm_depth_pruning/evals/mmlu.py cd763eaf1ac287e7 ran · our draft was wrong no licence file found · pointer only
RouteLLM: Learning to Route LLMs with Preference Data 26 Jun 2024 lm-sys/routellm/routellm/evals/mmlu/generate_responses.py cd763eaf1ac287e7 ran · our draft was wrong Apache-2.0 (permissive)
Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging 24 Jun 2024 sempraety/pruning-via-merging/pipeline.py e65eff7d9e1ab5f3 ran · our draft was wrong no licence file found · pointer only
CityGPT: Empowering Urban Spatial Cognition of Large Language Models 20 Jun 2024 tsinghua-fib-lab/citygpt/evaluate/city_eval/run_eval.py f6d6626dd2774d1c ran · our draft was wrong MIT (permissive)
CityBench: Evaluating the Capabilities of Large Language Models for Urban Tasks 20 Jun 2024 tsinghua-fib-lab/citybench/citybench/geoqa/run_eval.py fbde55d407dfbc1f ran MIT (permissive)
Data Contamination Can Cross Language Barriers 19 Jun 2024 ShangDataLab/Deep-Contam/inject/run_sft_mmlu.py 16d87a980525188f ran no licence file found · pointer only
MedExQA: Medical Question Answering Benchmark with Multiple Explanations 10 Jun 2024 knowlab/medexqa/evaluate_MedExQA.py 1a8789ccdb8f2890 ran · our draft was wrong no licence file found · pointer only
MedExQA: Medical Question Answering Benchmark with Multiple Explanations 10 Jun 2024 knowlab/medexqa/evaluate_pipe_MedExQA.py 2836f4edcd88af80 ran · our draft was wrong no licence file found · pointer only
DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs 3 Jun 2024 Hsu1023/DuQuant/mmlu_eval.py cd763eaf1ac287e7 ran · our draft was wrong MIT (permissive)
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark 3 Jun 2024 tiger-ai-lab/mmlu-pro/evaluate_from_api.py 1b3ff879d7f6d22b ran · our draft was wrong fingerprinted Apache-2.0 (permissive)
Multiple-Choice Questions are Efficient and Robust LLM Evaluators 20 May 2024 geralt-targaryen/mc-evaluation/dataset_mc.py 3d5d3f0243075f18 ran MIT (permissive)
Bridging the Gap between Different Vocabularies for LLM Ensemble 15 Apr 2024 xydaytoy/eva/ensemble/eva_multi.py ef417469242d0e7f ran · our draft was wrong no licence file found · pointer only
Advancing LLM Reasoning Generalists with Preference Trees 2 Apr 2024 openbmb/eurus/eval/mmlu/evaluate_mmlu.py cd763eaf1ac287e7 ran · our draft was wrong Apache-2.0 (permissive)
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal 2 Mar 2024 DeepLearnXMU/SSR/mmlu_test/evaluate.py cd763eaf1ac287e7 ran · our draft was wrong Apache-2.0 (permissive)
Learning or Self-aligning? Rethinking Instruction Fine-tuning 28 Feb 2024 renmengjie7/self-aligning/eval/my_benchmark_eval.py 32b8aeb6f6c62b62 ran no licence file found · pointer only
Learning or Self-aligning? Rethinking Instruction Fine-tuning 28 Feb 2024 renmengjie7/self-aligning/eval/my_domain_eval.py 64ff6430c270b1c9 ran no licence file found · pointer only
Sparse is Enough in Fine-tuning Pre-trained Large Language Models 19 Dec 2023 song-wx/SIFT/exp/mmlu/eval_mmlu.py cd763eaf1ac287e7 ran · our draft was wrong no licence file found · pointer only
SGLang: Efficient Execution of Structured Language Model Programs 12 Dec 2023 tginart/sglang/benchmark/mmlu/bench_sglang.py cd763eaf1ac287e7 ran · our draft was wrong Apache-2.0 (permissive)
Generative Parameter-Efficient Fine-Tuning 1 Dec 2023 savadikarc/gift/language_modeling/math_code_instruct/eval_mmlu.py dec15cc75848a98e ran MIT (permissive)
R-Tuning: Instructing Large Language Models to Say `I Don't Know' 16 Nov 2023 shizhediao/r-tuning/evaluation/MMLU/evaluate.py 434b759fa0cba061 ran no licence file found · pointer only
Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE 14 Oct 2023 isen-zhang/aclue/src/utils.py c7ad8ee11528e78b ran MIT (permissive)
We are what we repeatedly do: Inducing and deploying habitual schemas in persona-based responses 10 Oct 2023 bkane2/habitual-response-generation/src/generate_response.py 24893f346f97c656 ran no licence file found · pointer only
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models 9 Oct 2023 FranxYao/chain-of-thought-hub/MMLU/run_mmlu_llama.py cd763eaf1ac287e7 ran · our draft was wrong MIT (permissive)
FinGPT: Open-Source Financial Large Language Models 9 Jun 2023 ai4finance-foundation/finnlp/finnlp/benchmarks/fiqa.py 1b801af1b3425cfa unverified MIT (permissive)
Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance 26 May 2023 franxyao/chain-of-thought-hub/MMLU/run_mmlu_llama.py cd763eaf1ac287e7 ran · our draft was wrong MIT (permissive)
Measuring Massive Multitask Chinese Understanding 25 Apr 2023 Felixgithub2017/MMCU/TestBloomz.py a4f2a52c39dd4a34 ran · our draft was wrong no licence file found · pointer only
GLM-130B: An Open Bilingual Pre-trained Model 5 Oct 2022 jackaduma/ChatGLM-LoRA-RLHF-PyTorch/cover_alpaca2jsonl.py 1b801af1b3425cfa unverified MIT (permissive)
Measuring Massive Multitask Language Understanding 7 Sep 2020 ollmer/mmlu/evaluate.py cd763eaf1ac287e7 ran · our draft was wrong MIT (permissive)
arXiv:2025.findings-emnlp.484 UbiquitousLearning/DroidCall/gen_complex_instructions.py 2a66957120b8345c unverified MIT (permissive)
arXiv:2025.findings-emnlp.484 UbiquitousLearning/DroidCall/gen_instructions.py 6544f5aba9b4a618 unverified MIT (permissive)
arXiv:2025.emnlp-main.67 isyuhaochen/RRC-DSCD/code/sft/sft.py 27282e44111d98c4 unverified 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