{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/preprocess-dataset","entry":"preprocess_dataset","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":18,"n_papers_ran":7,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":17,"n_samples_ran":7,"n_samples_fingerprinted":0,"n_places":18,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":6,"unverified":10},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2609.13737","paper":"/paper/arxiv-2609-13737","title":"ForeSight: Enhancing Risk Monitoring via Early Safety Signal Distillation","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"Scabbards1500/Foresight","path":"train/preprocess.py","file_url":"https://github.com/Scabbards1500/Foresight/blob/HEAD/train/preprocess.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"02a30e29a00dfe0e","mcp_get_code":{"code_sha256":"02a30e29a00dfe0e"}},{"arxiv_id":"2602.06462","paper":"/paper/arxiv-2602-06462","title":"Diffusion-State Policy Optimization for Masked Diffusion Language Models","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"dllm-reasoning/d1","path":"SFT/sft_trainer.py","file_url":"https://github.com/dllm-reasoning/d1/blob/HEAD/SFT/sft_trainer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a3e7b9a227925647","mcp_get_code":{"code_sha256":"a3e7b9a227925647"}},{"arxiv_id":"2507.08267","paper":"/paper/a-practical-two-stage-recipe-for-mathematical","title":"A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement Learning","date":"2025-07-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"analokmaus/kaggle-aimo2-fast-math-r1","path":"experiments/train_token_scheduler.py","file_url":"https://github.com/analokmaus/kaggle-aimo2-fast-math-r1/blob/HEAD/experiments/train_token_scheduler.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"37c8141d07e7590d","mcp_get_code":{"code_sha256":"37c8141d07e7590d"}},{"arxiv_id":"2507.01299","paper":null,"title":"arXiv:2507.01299","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"alibaba/EfficientAI","path":"masquant/custom_dataset.py","file_url":"https://github.com/alibaba/EfficientAI/blob/HEAD/masquant/custom_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e0b50702ed514d61","mcp_get_code":{"code_sha256":"e0b50702ed514d61"}},{"arxiv_id":"2503.14434","paper":"/paper/llm-fe-automated-feature-engineering-for","title":"LLM-FE: Automated Feature Engineering for Tabular Data with LLMs as Evolutionary Optimizers","date":"2025-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nikhilsab/llmfe","path":"preprocessing.py","file_url":"https://github.com/nikhilsab/llmfe/blob/HEAD/preprocessing.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13487f38a3a33dfd","mcp_get_code":{"code_sha256":"13487f38a3a33dfd"}},{"arxiv_id":"2502.11435","paper":"/paper/smart-self-aware-agent-for-tool-overuse","title":"SMART: Self-Aware Agent for Tool Overuse Mitigation","date":"2025-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qiancheng0/open-smartagent","path":"inference/inference_smart.py","file_url":"https://github.com/qiancheng0/open-smartagent/blob/HEAD/inference/inference_smart.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad047902cc0458e3","mcp_get_code":{"code_sha256":"ad047902cc0458e3"}},{"arxiv_id":"2411.00300","paper":"/paper/rationale-guided-retrieval-augmented","title":"Rationale-Guided Retrieval Augmented Generation for Medical Question Answering","date":"2024-11-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dmis-lab/rag2","path":"classifier/utils.py","file_url":"https://github.com/dmis-lab/rag2/blob/HEAD/classifier/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"662378e999a32811","mcp_get_code":{"code_sha256":"662378e999a32811"}},{"arxiv_id":"2410.03523","paper":"/paper/a-probabilistic-perspective-on-unlearning-and","title":"A Probabilistic Perspective on Unlearning and Alignment for Large Language Models","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yascho/probabilistic-unlearning","path":"finetuning/preprocessing.py","file_url":"https://github.com/yascho/probabilistic-unlearning/blob/HEAD/finetuning/preprocessing.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d701c28cf7e2b3bf","mcp_get_code":{"code_sha256":"d701c28cf7e2b3bf"}},{"arxiv_id":"2408.06993","paper":"/paper/llms-can-schedule","title":"LLMs can Schedule","date":"2024-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"starjob42/datasetjsp","path":"utils/data_preprocessing.py","file_url":"https://github.com/starjob42/datasetjsp/blob/HEAD/utils/data_preprocessing.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cb71b262f560b386","mcp_get_code":{"code_sha256":"cb71b262f560b386"}},{"arxiv_id":"2408.02226","paper":"/paper/2408-02226","title":"ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation","date":"2024-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"agentic-learning-ai-lab/procreate-diffusion-public","path":"src/utils.py","file_url":"https://github.com/agentic-learning-ai-lab/procreate-diffusion-public/blob/HEAD/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0d87b52314272f36","mcp_get_code":{"code_sha256":"0d87b52314272f36"}},{"arxiv_id":"2406.02317","paper":"/paper/generative-conditional-distributions-by","title":"Generative Conditional Distributions by Neural (Entropic) Optimal Transport","date":"2024-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nguyenngocbaocmt02/gentle","path":"utils.py","file_url":"https://github.com/nguyenngocbaocmt02/gentle/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e83b17c5d6107e8c","mcp_get_code":{"code_sha256":"e83b17c5d6107e8c"}},{"arxiv_id":"2405.20974","paper":"/paper/sayself-teaching-llms-to-express-confidence","title":"SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xu1868/SaySelf","path":"evaluation/generate_reasons_for_evaluation.py","file_url":"https://github.com/xu1868/SaySelf/blob/HEAD/evaluation/generate_reasons_for_evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a942e23bce929ae1","mcp_get_code":{"code_sha256":"a942e23bce929ae1"}},{"arxiv_id":"2405.10994","paper":"/paper/what-do-you-want-from-theory-alone","title":"\"What do you want from theory alone?\" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation","date":null,"month_inferred_from_arxiv_id":"2024-05","title_source":"archive","repo":"spalabucr/synth-audit","path":"attacks/querybased.py","file_url":"https://github.com/spalabucr/synth-audit/blob/HEAD/attacks/querybased.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0746485cba167aed","mcp_get_code":{"code_sha256":"0746485cba167aed"}},{"arxiv_id":"2403.14403","paper":"/paper/adaptive-rag-learning-to-adapt-retrieval","title":"Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"starsuzi/Adaptive-RAG","path":"classifier/utils.py","file_url":"https://github.com/starsuzi/Adaptive-RAG/blob/HEAD/classifier/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"662378e999a32811","mcp_get_code":{"code_sha256":"662378e999a32811"}},{"arxiv_id":"2205.14135","paper":"/paper/flashattention-fast-and-memory-efficient","title":"FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness","date":"2022-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/long-range-arena","path":"lra_benchmarks/listops/input_pipeline.py","file_url":"https://github.com/google-research/long-range-arena/blob/HEAD/lra_benchmarks/listops/input_pipeline.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"59e4e06672a6c268","mcp_get_code":{"code_sha256":"59e4e06672a6c268"}},{"arxiv_id":"2204.13091","paper":"/paper/attention-consistency-on-visual-corruptions","title":"Attention Consistency on Visual Corruptions for Single-Source Domain Generalization","date":"2022-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"explainableml/acvc","path":"preprocessing/Datasets.py","file_url":"https://github.com/explainableml/acvc/blob/HEAD/preprocessing/Datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"23d5781026b28cf9","mcp_get_code":{"code_sha256":"23d5781026b28cf9"}},{"arxiv_id":"2106.06039","paper":"/paper/neural-higher-order-pattern-motif-prediction","title":"Neural Predicting Higher-order Patterns in Temporal Networks","date":"2021-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Graph-COM/Neural_Higher-order_Pattern-Motif_Prediction_in_Temporal_Networks","path":"find_pattern.py","file_url":"https://github.com/Graph-COM/Neural_Higher-order_Pattern-Motif_Prediction_in_Temporal_Networks/blob/HEAD/find_pattern.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9aad56561e90757d","mcp_get_code":{"code_sha256":"9aad56561e90757d"}},{"arxiv_id":"2024.findings-acl.712","paper":null,"title":"arXiv:2024.findings-acl.712","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"bjoernengelmann/BATS","path":"workspace/run_apply_lf.py","file_url":"https://github.com/bjoernengelmann/BATS/blob/HEAD/workspace/run_apply_lf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0014e63da4f605b","mcp_get_code":{"code_sha256":"b0014e63da4f605b"}}]}