{"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-cache-dir","entry":"get_cache_dir","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":14,"n_papers_ran":3,"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":3,"n_samples_fingerprinted":0,"n_places":14,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"unverified":9},"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":"2606.04205","paper":"/paper/arxiv-2606-04205","title":"DetectZoo: A Unified Toolkit for AI-Generated Content Detection Across Text, Audio, and Image Modalities","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"sadjadeb/DetectZoo","path":"detectzoo/datasets/_download.py","file_url":"https://github.com/sadjadeb/DetectZoo/blob/HEAD/detectzoo/datasets/_download.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"261dd6b88c82273b","mcp_get_code":{"code_sha256":"261dd6b88c82273b"}},{"arxiv_id":"2602.12241","paper":"/paper/arxiv-2602-12241","title":"Moonshine v2: Ergodic Streaming Encoder ASR for Latency-Critical Speech Applications","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"moonshine-ai/moonshine","path":"language-bindings/python/src/moonshine_voice/download_file.py","file_url":"https://github.com/moonshine-ai/moonshine/blob/HEAD/language-bindings/python/src/moonshine_voice/download_file.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d216fc2ce888c4e8","mcp_get_code":{"code_sha256":"d216fc2ce888c4e8"}},{"arxiv_id":"2509.24496","paper":"/paper/arxiv-2509-24496","title":"LLM DNA: Tracing Model Evolution via Functional Representations","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"Xtra-Computing/LLM-DNA","path":"src/llm_dna/utils/DataUtils.py","file_url":"https://github.com/Xtra-Computing/LLM-DNA/blob/HEAD/src/llm_dna/utils/DataUtils.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":"186043b0bc0c9369","mcp_get_code":{"code_sha256":"186043b0bc0c9369"}},{"arxiv_id":"2507.06211","paper":"/paper/modern-methods-in-associative-memory","title":"Modern Methods in Associative Memory","date":"2025-07-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bhoov/amtutorial","path":"amtutorial/data_utils.py","file_url":"https://github.com/bhoov/amtutorial/blob/HEAD/amtutorial/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5a7dd5c4aa7dd248","mcp_get_code":{"code_sha256":"5a7dd5c4aa7dd248"}},{"arxiv_id":"2505.13380","paper":"/paper/competesmoe-statistically-guaranteed-mixture","title":"CompeteSMoE -- Statistically Guaranteed Mixture of Experts Training via Competition","date":"2025-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fsoft-aic/competesmoe","path":"evaluate/lmms_eval/tasks/_task_utils/video_loader.py","file_url":"https://github.com/fsoft-aic/competesmoe/blob/HEAD/evaluate/lmms_eval/tasks/_task_utils/video_loader.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b05e3f5d170026a2","mcp_get_code":{"code_sha256":"b05e3f5d170026a2"}},{"arxiv_id":"2412.10569","paper":"/paper/learning-to-merge-tokens-via-decoupled","title":"Learning to Merge Tokens via Decoupled Embedding for Efficient Vision Transformers","date":"2024-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huggingface/pytorch-image-models","path":"timm/models/_hub.py","file_url":"https://github.com/huggingface/pytorch-image-models/blob/HEAD/timm/models/_hub.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":"bff72f1ca085c243","mcp_get_code":{"code_sha256":"bff72f1ca085c243"}},{"arxiv_id":"2410.15608","paper":"/paper/moonshine-speech-recognition-for-live","title":"Moonshine: Speech Recognition for Live Transcription and Voice Commands","date":"2024-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"usefulsensors/moonshine","path":"language-bindings/python/src/moonshine_voice/download_file.py","file_url":"https://github.com/usefulsensors/moonshine/blob/HEAD/language-bindings/python/src/moonshine_voice/download_file.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d216fc2ce888c4e8","mcp_get_code":{"code_sha256":"d216fc2ce888c4e8"}},{"arxiv_id":"2309.03904","paper":"/paper/exploring-sparse-moe-in-gans-for-text","title":"Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhujiapeng/aurora","path":"utils/misc.py","file_url":"https://github.com/zhujiapeng/aurora/blob/HEAD/utils/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e6573e8618e29edb","mcp_get_code":{"code_sha256":"e6573e8618e29edb"}},{"arxiv_id":"2305.15020","paper":"/paper/an-efficient-multilingual-language-model","title":"An Efficient Multilingual Language Model Compression through Vocabulary Trimming","date":"2023-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"asahi417/lm-vocab-trimmer","path":"vocabtrimmer/util.py","file_url":"https://github.com/asahi417/lm-vocab-trimmer/blob/HEAD/vocabtrimmer/util.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f04b0fee3a05006","mcp_get_code":{"code_sha256":"6f04b0fee3a05006"}},{"arxiv_id":"2209.15200","paper":"/paper/an-efficient-encoder-decoder-architecture","title":"An efficient encoder-decoder architecture with top-down attention for speech separation","date":"2022-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JusperLee/TDANet","path":"look2hear/models/TDANet.py","file_url":"https://github.com/JusperLee/TDANet/blob/HEAD/look2hear/models/TDANet.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":"52b1562ec0936b6e","mcp_get_code":{"code_sha256":"52b1562ec0936b6e"}},{"arxiv_id":"2206.03452","paper":"/paper/can-cnns-be-more-robust-than-transformers","title":"Can CNNs Be More Robust Than Transformers?","date":"2022-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucsc-vlaa/robustcnn","path":"timm/models/hub.py","file_url":"https://github.com/ucsc-vlaa/robustcnn/blob/HEAD/timm/models/hub.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"da3ccccf419f89e7","mcp_get_code":{"code_sha256":"da3ccccf419f89e7"}},{"arxiv_id":"2107.09539","paper":"/paper/parametric-scattering-networks","title":"Parametric Scattering Networks","date":"2021-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bentherien/ParametricScatteringNetworks","path":"kymatio/caching.py","file_url":"https://github.com/bentherien/ParametricScatteringNetworks/blob/HEAD/kymatio/caching.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"a54d87d5ca6ee1ed","mcp_get_code":{"code_sha256":"a54d87d5ca6ee1ed"}},{"arxiv_id":"2005.07421","paper":"/paper/spelling-error-correction-with-soft-masked","title":"Spelling Error Correction with Soft-Masked BERT","date":"2020-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gitabtion/BertBasedCorrectionModels","path":"bbcm/utils/file_io.py","file_url":"https://github.com/gitabtion/BertBasedCorrectionModels/blob/HEAD/bbcm/utils/file_io.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":"9f0e70bc9b57f335","mcp_get_code":{"code_sha256":"9f0e70bc9b57f335"}},{"arxiv_id":"1812.11214","paper":"/paper/kymatio-scattering-transforms-in-python","title":"Kymatio: Scattering Transforms in Python","date":"2018-12-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kymatio/kymatio","path":"kymatio/caching.py","file_url":"https://github.com/kymatio/kymatio/blob/HEAD/kymatio/caching.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"a54d87d5ca6ee1ed","mcp_get_code":{"code_sha256":"a54d87d5ca6ee1ed"}}]}