{"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/download-file","entry":"download_file","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":2,"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":21,"n_samples_ran":2,"n_samples_fingerprinted":1,"n_places":22,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":2,"unverified":19},"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":"2609.03673","paper":"/paper/arxiv-2609-03673","title":"Do Video Generators Track the World Across Segments? A Benchmark and Method for World-State Reasoning in Video Continuation","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"AMAP-ML/StateAgent","path":"generators/dashscope_task.py","file_url":"https://github.com/AMAP-ML/StateAgent/blob/HEAD/generators/dashscope_task.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b9944ac2d5b4b948","mcp_get_code":{"code_sha256":"b9944ac2d5b4b948"}},{"arxiv_id":"2606.21488","paper":"/paper/arxiv-2606-21488","title":"Robustness Cannot be Reduced to Regularization: Studying Adversarial Training Beyond the Linear Case","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"RobustBench/robustbench","path":"robustbench/zenodo_download.py","file_url":"https://github.com/RobustBench/robustbench/blob/HEAD/robustbench/zenodo_download.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5692fc738f6efb70","mcp_get_code":{"code_sha256":"5692fc738f6efb70"}},{"arxiv_id":"2606.10403","paper":"/paper/arxiv-2606-10403","title":"KCSAT-ML: Probing Reasoning Models with Nationwide-Cohort Human Difficulty","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"naver-ai/KCSAT-ML","path":"src/generator.py","file_url":"https://github.com/naver-ai/KCSAT-ML/blob/HEAD/src/generator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"8c4bcc32509a5203","mcp_get_code":{"code_sha256":"8c4bcc32509a5203"}},{"arxiv_id":"2606.02754","paper":"/paper/arxiv-2606-02754","title":"Ψ-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"Hanpx20/Psi-Bench","path":"psi_bench/download_data.py","file_url":"https://github.com/Hanpx20/Psi-Bench/blob/HEAD/psi_bench/download_data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e507a30d12534318","mcp_get_code":{"code_sha256":"e507a30d12534318"}},{"arxiv_id":"2605.23118","paper":"/paper/arxiv-2605-23118","title":"Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"MIC-DKFZ/LongiSeg","path":"longiseg/model_sharing/model_download.py","file_url":"https://github.com/MIC-DKFZ/LongiSeg/blob/HEAD/longiseg/model_sharing/model_download.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":"a60d87902bdba079","mcp_get_code":{"code_sha256":"a60d87902bdba079"}},{"arxiv_id":"2605.03903","paper":"/paper/arxiv-2605-03903","title":"CC-OCR v2: Fine-Grained Attribution of LMM Failures in Real-World Visual Document Understanding","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"eioss/CC-OCR-V2","path":"datasets_process/process_funsd.py","file_url":"https://github.com/eioss/CC-OCR-V2/blob/HEAD/datasets_process/process_funsd.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d18aa3036d3fc5e9","mcp_get_code":{"code_sha256":"d18aa3036d3fc5e9"}},{"arxiv_id":"2411.06500","paper":"/paper/towards-graph-neural-network-surrogates","title":"Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response","date":"2024-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scicompmod/memilio","path":"pycode/memilio-epidata/memilio/epidata/getDataIntoPandasDataFrame.py","file_url":"https://github.com/scicompmod/memilio/blob/HEAD/pycode/memilio-epidata/memilio/epidata/getDataIntoPandasDataFrame.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":"4f08f4d0029b0955","mcp_get_code":{"code_sha256":"4f08f4d0029b0955"}},{"arxiv_id":"2410.03969","paper":"/paper/embrace-rejection-kernel-matrix-approximation","title":"Embrace rejection: Kernel matrix approximation by accelerated randomly pivoted Cholesky","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eepperly/randomly-pivoted-cholesky","path":"download_data.py","file_url":"https://github.com/eepperly/randomly-pivoted-cholesky/blob/HEAD/download_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7ac9f0f697d4764d","mcp_get_code":{"code_sha256":"7ac9f0f697d4764d"}},{"arxiv_id":"2406.06512","paper":"/paper/merlin-a-vision-language-foundation-model-for","title":"Merlin: A Vision Language Foundation Model for 3D Computed Tomography","date":"2024-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stanfordmimi/merlin","path":"merlin/utils/huggingface_download.py","file_url":"https://github.com/stanfordmimi/merlin/blob/HEAD/merlin/utils/huggingface_download.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d9b7e589764478c3","mcp_get_code":{"code_sha256":"d9b7e589764478c3"}},{"arxiv_id":"2405.07940","paper":"/paper/raid-a-shared-benchmark-for-robust-evaluation","title":"RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors","date":"2024-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liamdugan/raid","path":"raid/utils.py","file_url":"https://github.com/liamdugan/raid/blob/HEAD/raid/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"12cb325ce58c2da8","mcp_get_code":{"code_sha256":"12cb325ce58c2da8"}},{"arxiv_id":"2404.10297","paper":"/paper/future-language-modeling-from-temporal","title":"Future Language Modeling from Temporal Document History","date":"2024-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jlab-nlp/future-language-modeling","path":"data_process/data_process.py","file_url":"https://github.com/jlab-nlp/future-language-modeling/blob/HEAD/data_process/data_process.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":"49b19d90659d51a6","mcp_get_code":{"code_sha256":"49b19d90659d51a6"}},{"arxiv_id":"2403.20330","paper":"/paper/are-we-on-the-right-way-for-evaluating-large","title":"Are We on the Right Way for Evaluating Large Vision-Language Models?","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MMStar-Benchmark/MMStar","path":"eval/vlmeval/smp/file.py","file_url":"https://github.com/MMStar-Benchmark/MMStar/blob/HEAD/eval/vlmeval/smp/file.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"180c13ba0cccedee","mcp_get_code":{"code_sha256":"180c13ba0cccedee"}},{"arxiv_id":"2402.10962","paper":"/paper/measuring-and-controlling-instruction-in","title":"Measuring and Controlling Instruction (In)Stability in Language Model Dialogs","date":"2024-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"likenneth/persona_drift","path":"hundred_system_prompts.py","file_url":"https://github.com/likenneth/persona_drift/blob/HEAD/hundred_system_prompts.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5730e2e6f045537a","mcp_get_code":{"code_sha256":"5730e2e6f045537a"}},{"arxiv_id":"2312.07577","paper":"/paper/benchmarking-distribution-shift-in-tabular-1","title":"Benchmarking Distribution Shift in Tabular Data with TableShift","date":"2023-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mlfoundations/tableshift","path":"tableshift/core/utils.py","file_url":"https://github.com/mlfoundations/tableshift/blob/HEAD/tableshift/core/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bbdf216a1a512106","mcp_get_code":{"code_sha256":"bbdf216a1a512106"}},{"arxiv_id":"2303.14444","paper":"/paper/multitalent-a-multi-dataset-approach-to","title":"MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation","date":"2023-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mic-dkfz/multitalent","path":"multitalent/model_sharing/model_download.py","file_url":"https://github.com/mic-dkfz/multitalent/blob/HEAD/multitalent/model_sharing/model_download.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":"a60d87902bdba079","mcp_get_code":{"code_sha256":"a60d87902bdba079"}},{"arxiv_id":"2302.11893","paper":"/paper/a-framework-for-benchmarking-class-out-of-1","title":"A framework for benchmarking class-out-of-distribution detection and its application to ImageNet","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdabbah/COOD_benchmarking","path":"download_dummy_dataset.py","file_url":"https://github.com/mdabbah/COOD_benchmarking/blob/HEAD/download_dummy_dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"42bc76618fad0419","mcp_get_code":{"code_sha256":"42bc76618fad0419"}},{"arxiv_id":"2009.12534","paper":"/paper/inltk-natural-language-toolkit-for-indic","title":"iNLTK: Natural Language Toolkit for Indic Languages","date":"2020-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"goru001/inltk","path":"inltk/download_assets.py","file_url":"https://github.com/goru001/inltk/blob/HEAD/inltk/download_assets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1d17b0f13465166d","mcp_get_code":{"code_sha256":"1d17b0f13465166d"}},{"arxiv_id":"2006.08924","paper":"/paper/gcns-net-a-graph-convolutional-neural-network","title":"GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals","date":"2020-06-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SuperBruceJia/EEG-DL","path":"Download_Raw_EEG_Data/MIND_Get_EDF.py","file_url":"https://github.com/SuperBruceJia/EEG-DL/blob/HEAD/Download_Raw_EEG_Data/MIND_Get_EDF.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a8cd2d184c87af68","mcp_get_code":{"code_sha256":"a8cd2d184c87af68"}},{"arxiv_id":"1904.01038","paper":"/paper/fairseq-a-fast-extensible-toolkit-for","title":"fairseq: A Fast, Extensible Toolkit for Sequence Modeling","date":"2019-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guxm2021/MM_ALT","path":"speechbrain/lobes/models/fairseq_wav2vec.py","file_url":"https://github.com/guxm2021/MM_ALT/blob/HEAD/speechbrain/lobes/models/fairseq_wav2vec.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":"8cc369a929f16e9a","mcp_get_code":{"code_sha256":"8cc369a929f16e9a"}},{"arxiv_id":"1902.06704","paper":"/paper/towards-non-saturating-recurrent-units-for","title":"Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies","date":"2019-01-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apsarath/NRU","path":"nru_project/task/load_mnist.py","file_url":"https://github.com/apsarath/NRU/blob/HEAD/nru_project/task/load_mnist.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":"cc21f73dafd45cce","mcp_get_code":{"code_sha256":"cc21f73dafd45cce"}},{"arxiv_id":"1603.07027","paper":"/paper/moon-a-mixed-objective-optimization-network","title":"MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes","date":"2016-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"likelyzhao/peron_attribute","path":"training/common/util.py","file_url":"https://github.com/likelyzhao/peron_attribute/blob/HEAD/training/common/util.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":"3ef479bb5a86e5cc","mcp_get_code":{"code_sha256":"3ef479bb5a86e5cc"}},{"arxiv_id":"ijcai2022_0132","paper":null,"title":"arXiv:ijcai2022_0132","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"woshidandan/TANet","path":"code/AVA/util.py","file_url":"https://github.com/woshidandan/TANet/blob/HEAD/code/AVA/util.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":"7ba09772c3e08e23","mcp_get_code":{"code_sha256":"7ba09772c3e08e23"}}]}