{"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/get-last-checkpoint","entry":"get_last_checkpoint","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":22,"n_papers_ran":17,"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":12,"n_samples_ran":7,"n_samples_fingerprinted":5,"n_places":22,"n_places_pointer_only":7,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":5,"unverified":5},"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":"2601.02031","paper":"/paper/arxiv-2601-02031","title":"Output Embedding Centering for Stable LLM Pretraining","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"flxst/output-embedding-centering","path":"nanoGPT/config/script_create_config_val.py","file_url":"https://github.com/flxst/output-embedding-centering/blob/HEAD/nanoGPT/config/script_create_config_val.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fb3e1f3ddc2017c","mcp_get_code":{"code_sha256":"2fb3e1f3ddc2017c"}},{"arxiv_id":"2410.20081","paper":"/paper/emg2qwerty-a-large-dataset-with-baselines-for","title":"emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography","date":"2024-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/emg2qwerty","path":"emg2qwerty/utils.py","file_url":"https://github.com/facebookresearch/emg2qwerty/blob/HEAD/emg2qwerty/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"11f5d3e303abe99d","mcp_get_code":{"code_sha256":"11f5d3e303abe99d"}},{"arxiv_id":"2407.11062","paper":"/paper/efficientqat-efficient-quantization-aware","title":"EfficientQAT: Efficient Quantization-Aware Training for Large Language Models","date":"2024-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/efficientqat","path":"main_e2e_qp.py","file_url":"https://github.com/opengvlab/efficientqat/blob/HEAD/main_e2e_qp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89ce33341af66037","mcp_get_code":{"code_sha256":"89ce33341af66037"}},{"arxiv_id":"2406.11280","paper":"/paper/i-srt-aligning-large-multimodal-models-for","title":"ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO","date":"2024-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yonseivnl/vlm-rlaif","path":"RLAIF/lora_utils.py","file_url":"https://github.com/yonseivnl/vlm-rlaif/blob/HEAD/RLAIF/lora_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"89ce33341af66037","mcp_get_code":{"code_sha256":"89ce33341af66037"}},{"arxiv_id":"2405.11165","paper":"/paper/automated-multi-level-preference-for-mllms","title":"Automated Multi-level Preference for MLLMs","date":"2024-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"takomc/amp","path":"lora_utils.py","file_url":"https://github.com/takomc/amp/blob/HEAD/lora_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"89ce33341af66037","mcp_get_code":{"code_sha256":"89ce33341af66037"}},{"arxiv_id":"2401.06066","paper":"/paper/deepseekmoe-towards-ultimate-expert","title":"DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models","date":"2024-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepseek-ai/deepseek-moe","path":"finetune/finetune.py","file_url":"https://github.com/deepseek-ai/deepseek-moe/blob/HEAD/finetune/finetune.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"436288f52a3803bb","mcp_get_code":{"code_sha256":"436288f52a3803bb"}},{"arxiv_id":"2312.00374","paper":"/paper/unleashing-cheapfakes-through-trojan-plugins","title":"The Philosopher's Stone: Trojaning Plugins of Large Language Models","date":null,"month_inferred_from_arxiv_id":"2023-12","title_source":"archive","repo":"chichidd/llm-lora-trojan","path":"utils.py","file_url":"https://github.com/chichidd/llm-lora-trojan/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"52994a0cce5a9a54","mcp_get_code":{"code_sha256":"52994a0cce5a9a54"}},{"arxiv_id":"2311.04072","paper":"/paper/beyond-imitation-leveraging-fine-grained","title":"Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rucaibox/figa","path":"trainer_utils.py","file_url":"https://github.com/rucaibox/figa/blob/HEAD/trainer_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9f1130d32c6430f8","mcp_get_code":{"code_sha256":"9f1130d32c6430f8"}},{"arxiv_id":"2311.01015","paper":null,"title":"arXiv:2311.01015","date":null,"month_inferred_from_arxiv_id":"2023-11","title_source":null,"repo":"jpthu17/GraphMotion","path":"GraphMotion/launch/prepare.py","file_url":"https://github.com/jpthu17/GraphMotion/blob/HEAD/GraphMotion/launch/prepare.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"937fa0d85f36fce1","mcp_get_code":{"code_sha256":"937fa0d85f36fce1"}},{"arxiv_id":"2310.12178","paper":"/paper/prediction-and-control-of-spatiotemporal","title":"Prediction and control of spatiotemporal chaos by learning conjugate tubular neighborhoods","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":"archive","repo":"burakbudanur/conjnet","path":"training/train_conjnet_KS.py","file_url":"https://github.com/burakbudanur/conjnet/blob/HEAD/training/train_conjnet_KS.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a41ccb40863d7b03","mcp_get_code":{"code_sha256":"a41ccb40863d7b03"}},{"arxiv_id":"2310.05910","paper":"/paper/salmon-self-alignment-with-principle","title":"SALMON: Self-Alignment with Instructable Reward Models","date":"2023-10-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibm/salmon","path":"training/qlora_utils.py","file_url":"https://github.com/ibm/salmon/blob/HEAD/training/qlora_utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"89ce33341af66037","mcp_get_code":{"code_sha256":"89ce33341af66037"}},{"arxiv_id":"2306.02986","paper":"/paper/brain-tumor-segmentation-using-synthetic-mr","title":"Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models","date":"2023-06-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"muhamadusman/assist","path":"Segmentation/assist/check_trainings.py","file_url":"https://github.com/muhamadusman/assist/blob/HEAD/Segmentation/assist/check_trainings.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3134b56f1e63aa9b","mcp_get_code":{"code_sha256":"3134b56f1e63aa9b"}},{"arxiv_id":"2305.14314","paper":"/paper/qlora-efficient-finetuning-of-quantized-llms","title":"QLoRA: Efficient Finetuning of Quantized LLMs","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"artidoro/qlora","path":"examples/guanaco_generate.py","file_url":"https://github.com/artidoro/qlora/blob/HEAD/examples/guanaco_generate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89ce33341af66037","mcp_get_code":{"code_sha256":"89ce33341af66037"}},{"arxiv_id":"2305.11846","paper":"/paper/any-to-any-generation-via-composable","title":"Any-to-Any Generation via Composable Diffusion","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/i-Code","path":"i-Code-Doc/core/common/utils.py","file_url":"https://github.com/microsoft/i-Code/blob/HEAD/i-Code-Doc/core/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f1130d32c6430f8","mcp_get_code":{"code_sha256":"9f1130d32c6430f8"}},{"arxiv_id":"2212.02623","paper":"/paper/unifying-vision-text-and-layout-for-universal","title":"Unifying Vision, Text, and Layout for Universal Document Processing","date":"2022-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DS4SD/MarkushGrapher","path":"markushgrapher/core/common/utils.py","file_url":"https://github.com/DS4SD/MarkushGrapher/blob/HEAD/markushgrapher/core/common/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f1130d32c6430f8","mcp_get_code":{"code_sha256":"9f1130d32c6430f8"}},{"arxiv_id":"2202.13669","paper":"/paper/lilt-a-simple-yet-effective-language","title":"LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding","date":"2022-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jpWang/LiLT","path":"LiLTfinetune/evaluation.py","file_url":"https://github.com/jpWang/LiLT/blob/HEAD/LiLTfinetune/evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f1130d32c6430f8","mcp_get_code":{"code_sha256":"9f1130d32c6430f8"}},{"arxiv_id":"2104.09124","paper":"/paper/disco-remedy-self-supervised-learning-on","title":"DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning","date":"2021-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyqcom/DisCo","path":"MoCo/train_ms.py","file_url":"https://github.com/lyqcom/DisCo/blob/HEAD/MoCo/train_ms.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":"18c07acccd255281","mcp_get_code":{"code_sha256":"18c07acccd255281"}},{"arxiv_id":"2001.03799","paper":"/paper/dudornet-learning-a-dual-domain-recurrent","title":"DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior","date":"2020-01-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bbbbbbzhou/DuDoRNet","path":"utils/misc.py","file_url":"https://github.com/bbbbbbzhou/DuDoRNet/blob/HEAD/utils/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"50f0e5990ded5202","mcp_get_code":{"code_sha256":"50f0e5990ded5202"}},{"arxiv_id":"1912.06430","paper":"/paper/end-to-end-learning-of-visual-representations","title":"End-to-End Learning of Visual Representations from Uncurated Instructional Videos","date":"2019-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"antoine77340/MIL-NCE_HowTo100M","path":"main_distributed.py","file_url":"https://github.com/antoine77340/MIL-NCE_HowTo100M/blob/HEAD/main_distributed.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4d241892c1ca731a","mcp_get_code":{"code_sha256":"4d241892c1ca731a"}},{"arxiv_id":"1906.03327","paper":"/paper/howto100m-learning-a-text-video-embedding-by","title":"HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips","date":"2019-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"4d241892c1ca731a","mcp_get_code":{"code_sha256":"4d241892c1ca731a"}},{"arxiv_id":"2025.acl-long.498","paper":null,"title":"arXiv:2025.acl-long.498","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"OpenGVLab/EfficientQAT","path":"main_e2e_qp.py","file_url":"https://github.com/OpenGVLab/EfficientQAT/blob/HEAD/main_e2e_qp.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89ce33341af66037","mcp_get_code":{"code_sha256":"89ce33341af66037"}},{"arxiv_id":"2023.findings-emnlp.107","paper":null,"title":"arXiv:2023.findings-emnlp.107","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"chenxn2020/GOSE","path":"GOSEfinetune/evaluation.py","file_url":"https://github.com/chenxn2020/GOSE/blob/HEAD/GOSEfinetune/evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9f1130d32c6430f8","mcp_get_code":{"code_sha256":"9f1130d32c6430f8"}}]}