{"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/sample-vectors","entry":"sample_vectors","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":15,"n_papers_ran":15,"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":5,"n_samples_ran":5,"n_samples_fingerprinted":5,"n_places":15,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":3,"ran":2,"unverified":0},"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":"2604.22225","paper":"/paper/arxiv-2604-22225","title":"TTS-PRISM: A Perceptual Reasoning and Interpretable Speech Model for Fine-Grained Diagnosis","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"xiaomi-research/tts-prism","path":"models/mimo_audio_tokenizer/quantization.py","file_url":"https://github.com/xiaomi-research/tts-prism/blob/HEAD/models/mimo_audio_tokenizer/quantization.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":"1f512cf5b731fcaa","mcp_get_code":{"code_sha256":"1f512cf5b731fcaa"}},{"arxiv_id":"2506.21803","paper":null,"title":"arXiv:2506.21803","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"HKU-MedAI/MELP","path":"src/melp/backbone/norm_ema_quantizer.py","file_url":"https://github.com/HKU-MedAI/MELP/blob/HEAD/src/melp/backbone/norm_ema_quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"2409.00101","paper":"/paper/neurolm-a-universal-multi-task-foundation","title":"NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals","date":"2024-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"935963004/neurolm","path":"model/norm_ema_quantizer.py","file_url":"https://github.com/935963004/neurolm/blob/HEAD/model/norm_ema_quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"2407.05361","paper":"/paper/emilia-an-extensive-multilingual-and-diverse","title":"Emilia: An Extensive, Multilingual, and Diverse Speech Dataset for Large-Scale Speech Generation","date":"2024-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"open-mmlab/Amphion","path":"models/codec/amphion_codec/quantize/vector_quantize.py","file_url":"https://github.com/open-mmlab/Amphion/blob/HEAD/models/codec/amphion_codec/quantize/vector_quantize.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"2407.09533","paper":"/paper/video-occupancy-models","title":"Video Occupancy Models","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"manantomar/video-occupancy-models","path":"dino/norm_ema_quantizer.py","file_url":"https://github.com/manantomar/video-occupancy-models/blob/HEAD/dino/norm_ema_quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"2405.18765","paper":"/paper/large-brain-model-for-learning-generic","title":"Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI","date":"2024-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"935963004/labram","path":"norm_ema_quantizer.py","file_url":"https://github.com/935963004/labram/blob/HEAD/norm_ema_quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"2405.04752","paper":"/paper/hilcodec-high-fidelity-and-lightweight-neural","title":"HILCodec: High-Fidelity and Lightweight Neural Audio Codec","date":"2024-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aask1357/hilcodec","path":"models/hilcodec/vector_quantize.py","file_url":"https://github.com/aask1357/hilcodec/blob/HEAD/models/hilcodec/vector_quantize.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01c3ffb2465073f7","mcp_get_code":{"code_sha256":"01c3ffb2465073f7"}},{"arxiv_id":"2311.17245","paper":"/paper/lightgaussian-unbounded-3d-gaussian","title":"LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS","date":"2023-11-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/LightGaussian","path":"vectree/vectree.py","file_url":"https://github.com/VITA-Group/LightGaussian/blob/HEAD/vectree/vectree.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0fb2b35fef53b5da","mcp_get_code":{"code_sha256":"0fb2b35fef53b5da"}},{"arxiv_id":"2309.16429","paper":"/paper/diverse-and-aligned-audio-to-video-generation","title":"Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guyyariv/TempoTokens","path":"modules/beats/quantizer.py","file_url":"https://github.com/guyyariv/TempoTokens/blob/HEAD/modules/beats/quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"2308.16692","paper":"/paper/speechtokenizer-unified-speech-tokenizer-for","title":"SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models","date":"2023-08-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZhangXInFD/SpeechTokenizer","path":"speechtokenizer/quantization/core_vq.py","file_url":"https://github.com/ZhangXInFD/SpeechTokenizer/blob/HEAD/speechtokenizer/quantization/core_vq.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"09f10cd96592b37d","mcp_get_code":{"code_sha256":"09f10cd96592b37d"}},{"arxiv_id":"2308.02117","paper":"/paper/vqgraph-graph-vector-quantization-for","title":"VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs","date":"2023-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangling0818/vqgraph","path":"vq.py","file_url":"https://github.com/yangling0818/vqgraph/blob/HEAD/vq.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"2306.06546","paper":"/paper/high-fidelity-audio-compression-with-improved","title":"High-Fidelity Audio Compression with Improved RVQGAN","date":"2023-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangxinfd/speechtokenizer","path":"speechtokenizer/model.py","file_url":"https://github.com/zhangxinfd/speechtokenizer/blob/HEAD/speechtokenizer/model.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"09f10cd96592b37d","mcp_get_code":{"code_sha256":"09f10cd96592b37d"}},{"arxiv_id":"2305.13050","paper":"/paper/audiotoken-adaptation-of-text-conditioned-1","title":"AudioToken: Adaptation of Text-Conditioned Diffusion Models for Audio-to-Image Generation","date":"2023-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guyyariv/AudioToken","path":"modules/BEATs/quantizer.py","file_url":"https://github.com/guyyariv/AudioToken/blob/HEAD/modules/BEATs/quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"2212.09058","paper":"/paper/beats-audio-pre-training-with-acoustic","title":"BEATs: Audio Pre-Training with Acoustic Tokenizers","date":"2022-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"phuriches/genrepasd","path":"beats/quantizer.py","file_url":"https://github.com/phuriches/genrepasd/blob/HEAD/beats/quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}},{"arxiv_id":"Liu_EEGMirror_Leveraging_EEG_Data_in_the_Wild_via_Montage-Agnostic_Self-Supervision_ICCV_2025_paper","paper":null,"title":"arXiv:Liu_EEGMirror_Leveraging_EEG_Data_in_the_Wild_via_Montage-Agnostic_Self-Supervision_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"XuanhaoLiu/EEGMirror","path":"eegmirror/quantizer.py","file_url":"https://github.com/XuanhaoLiu/EEGMirror/blob/HEAD/eegmirror/quantizer.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ada1d74afbd92171","mcp_get_code":{"code_sha256":"ada1d74afbd92171"}}]}