{"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/interp","entry":"interp","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":16,"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":13,"n_samples_ran":8,"n_samples_fingerprinted":2,"n_places":22,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":1,"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":"2503.16709","paper":"/paper/quartdepth-post-training-quantization-for-1","title":"QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge","date":"2025-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shawnricecake/quart-depth","path":"metric3d/mono/model/decode_heads/RAFTDepthNormalDPTDecoder5_quant.py","file_url":"https://github.com/shawnricecake/quart-depth/blob/HEAD/metric3d/mono/model/decode_heads/RAFTDepthNormalDPTDecoder5_quant.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"01dbab41e1e09abf","mcp_get_code":{"code_sha256":"01dbab41e1e09abf"}},{"arxiv_id":"2501.09898","paper":"/paper/foundationstereo-zero-shot-stereo-matching","title":"FoundationStereo: Zero-Shot Stereo Matching","date":"2025-01-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NVlabs/FoundationStereo","path":"core/foundation_stereo.py","file_url":"https://github.com/NVlabs/FoundationStereo/blob/HEAD/core/foundation_stereo.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"08bedbc0c0447f5d","mcp_get_code":{"code_sha256":"08bedbc0c0447f5d"}},{"arxiv_id":"2501.09466","paper":"/paper/defom-stereo-depth-foundation-model-based","title":"DEFOM-Stereo: Depth Foundation Model Based Stereo Matching","date":"2025-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"insta360-research-team/defom-stereo","path":"core/update.py","file_url":"https://github.com/insta360-research-team/defom-stereo/blob/HEAD/core/update.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08bedbc0c0447f5d","mcp_get_code":{"code_sha256":"08bedbc0c0447f5d"}},{"arxiv_id":"2412.04472","paper":"/paper/stereo-anywhere-robust-zero-shot-deep-stereo","title":"Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail","date":"2024-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bartn8/stereoanywhere","path":"models/stereoanywhere/update.py","file_url":"https://github.com/bartn8/stereoanywhere/blob/HEAD/models/stereoanywhere/update.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"08bedbc0c0447f5d","mcp_get_code":{"code_sha256":"08bedbc0c0447f5d"}},{"arxiv_id":"2410.04534","paper":"/paper/unimumo-unified-text-music-and-motion","title":"UniMuMo: Unified Text, Music and Motion Generation","date":"2024-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanyangclarence/UniMuMo","path":"unimumo/alignment/interpolation.py","file_url":"https://github.com/hanyangclarence/UniMuMo/blob/HEAD/unimumo/alignment/interpolation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"158ff9eaa14b362d","mcp_get_code":{"code_sha256":"158ff9eaa14b362d"}},{"arxiv_id":"2408.14690","paper":"/paper/training-free-activation-sparsity-in-large","title":"Training-Free Activation Sparsity in Large Language Models","date":"2024-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fasterdecoding/teal","path":"teal/model.py","file_url":"https://github.com/fasterdecoding/teal/blob/HEAD/teal/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e141b830083564c0","mcp_get_code":{"code_sha256":"e141b830083564c0"}},{"arxiv_id":"2407.15420","paper":"/paper/local-all-pair-correspondence-for-point","title":"Local All-Pair Correspondence for Point Tracking","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvlab-kaist/locotrack","path":"locotrack/utils/model_utils.py","file_url":"https://github.com/cvlab-kaist/locotrack/blob/HEAD/locotrack/utils/model_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"39fcbdf93dcb4313","mcp_get_code":{"code_sha256":"39fcbdf93dcb4313"}},{"arxiv_id":"2407.11950","paper":"/paper/temporally-consistent-stereo-matching","title":"Temporally Consistent Stereo Matching","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaxiZeng/Temporally-Consistent-Stereo-Matching","path":"core/update.py","file_url":"https://github.com/jiaxiZeng/Temporally-Consistent-Stereo-Matching/blob/HEAD/core/update.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08bedbc0c0447f5d","mcp_get_code":{"code_sha256":"08bedbc0c0447f5d"}},{"arxiv_id":"2405.20313","paper":"/paper/sequence-augmented-se-3-flow-matching-for","title":"Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dreamfold/foldflow","path":"FoldFlow/so3/igso3.py","file_url":"https://github.com/dreamfold/foldflow/blob/HEAD/FoldFlow/so3/igso3.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a372d83ad8009640","mcp_get_code":{"code_sha256":"a372d83ad8009640"}},{"arxiv_id":"2404.06842","paper":"/paper/mocha-stereo-motif-channel-attention-network","title":"MoCha-Stereo: Motif Channel Attention Network for Stereo Matching","date":"2024-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyangchen/mocha-stereo","path":"MoCha-Stereo/core/update.py","file_url":"https://github.com/zyangchen/mocha-stereo/blob/HEAD/MoCha-Stereo/core/update.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08bedbc0c0447f5d","mcp_get_code":{"code_sha256":"08bedbc0c0447f5d"}},{"arxiv_id":"2403.00486","paper":"/paper/selective-stereo-adaptive-frequency","title":"Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"windsrain/selective-stereo","path":"Selective-IGEV/core/update.py","file_url":"https://github.com/windsrain/selective-stereo/blob/HEAD/Selective-IGEV/core/update.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08bedbc0c0447f5d","mcp_get_code":{"code_sha256":"08bedbc0c0447f5d"}},{"arxiv_id":"2401.04071","paper":"/paper/fun-with-flags-robust-principal-directions","title":"Fun with Flags: Robust Principal Directions via Flag Manifolds","date":"2024-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nmank/funwithflags","path":"Hands/my_interpolate.py","file_url":"https://github.com/nmank/funwithflags/blob/HEAD/Hands/my_interpolate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"adab89d94f21c30f","mcp_get_code":{"code_sha256":"adab89d94f21c30f"}},{"arxiv_id":"2312.02155","paper":"/paper/gps-gaussian-generalizable-pixel-wise-3d","title":"GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aipixel/gps-gaussian","path":"core/update.py","file_url":"https://github.com/aipixel/gps-gaussian/blob/HEAD/core/update.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08bedbc0c0447f5d","mcp_get_code":{"code_sha256":"08bedbc0c0447f5d"}},{"arxiv_id":"2307.01197","paper":"/paper/segment-anything-meets-point-tracking","title":"Segment Anything Meets Point Tracking","date":"2023-07-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"syscv/sam-pt","path":"sam_pt/point_tracker/tapnet/tapnet_model.py","file_url":"https://github.com/syscv/sam-pt/blob/HEAD/sam_pt/point_tracker/tapnet/tapnet_model.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":"e5e6908904107ff9","mcp_get_code":{"code_sha256":"e5e6908904107ff9"}},{"arxiv_id":"2212.09730","paper":"/paper/speaking-style-conversion-with-discrete-self","title":"Speaking Style Conversion in the Waveform Domain Using Discrete Self-Supervised Units","date":"2022-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gallilmaimon/DISSC","path":"utils.py","file_url":"https://github.com/gallilmaimon/DISSC/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"446926f352958627","mcp_get_code":{"code_sha256":"446926f352958627"}},{"arxiv_id":"2112.09130","paper":"/paper/ensembling-off-the-shelf-models-for-gan","title":"Ensembling Off-the-shelf Models for GAN Training","date":"2021-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nupurkmr9/vision-aided-gan","path":"vision_aided_loss/face_parsing.py","file_url":"https://github.com/nupurkmr9/vision-aided-gan/blob/HEAD/vision_aided_loss/face_parsing.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3e87c5d93ee4df83","mcp_get_code":{"code_sha256":"3e87c5d93ee4df83"}},{"arxiv_id":"2112.05142","paper":"/paper/hairclip-design-your-hair-by-text-and","title":"HairCLIP: Design Your Hair by Text and Reference Image","date":"2021-12-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wty-ustc/hairclip","path":"criteria/parse_related_loss/model_utils.py","file_url":"https://github.com/wty-ustc/hairclip/blob/HEAD/criteria/parse_related_loss/model_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"LGPL-2.1","inline_ok":false,"code_sha256_prefix":"3e87c5d93ee4df83","mcp_get_code":{"code_sha256":"3e87c5d93ee4df83"}},{"arxiv_id":"2106.14917","paper":"/paper/striking-the-right-balance-recall-loss-for","title":"Striking the Right Balance: Recall Loss for Semantic Segmentation","date":"2021-06-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PotatoTian/recall-semseg","path":"ptsemseg/models/utils.py","file_url":"https://github.com/PotatoTian/recall-semseg/blob/HEAD/ptsemseg/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aed5a907c8821d6d","mcp_get_code":{"code_sha256":"aed5a907c8821d6d"}},{"arxiv_id":"2006.00176","paper":"/paper/when2com-multi-agent-perception-via","title":"When2com: Multi-Agent Perception via Communication Graph Grouping","date":"2020-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GT-RIPL/MultiAgentPerception","path":"ptsemseg/models/utils.py","file_url":"https://github.com/GT-RIPL/MultiAgentPerception/blob/HEAD/ptsemseg/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aed5a907c8821d6d","mcp_get_code":{"code_sha256":"aed5a907c8821d6d"}},{"arxiv_id":"1912.03618","paper":"/paper/efficient-black-box-assessment-of-autonomous","title":"Efficient Black-box Assessment of Autonomous Vehicle Safety","date":"2019-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Trustworthy-AI/testpilot","path":"testpilot/testpilot0.5/common/numpy_fast.py","file_url":"https://github.com/Trustworthy-AI/testpilot/blob/HEAD/testpilot/testpilot0.5/common/numpy_fast.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"75cbbd8d6697e8ed","mcp_get_code":{"code_sha256":"75cbbd8d6697e8ed"}},{"arxiv_id":"1811.10597","paper":"/paper/gan-dissection-visualizing-and-understanding","title":"GAN Dissection: Visualizing and Understanding Generative Adversarial Networks","date":"2018-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexandonian/ganocracy","path":"gan_training/ganocracy/utils/visualizer.py","file_url":"https://github.com/alexandonian/ganocracy/blob/HEAD/gan_training/ganocracy/utils/visualizer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4672b1fe29d691a","mcp_get_code":{"code_sha256":"a4672b1fe29d691a"}},{"arxiv_id":"Zeng_Parameterized_Cost_Volume_for_Stereo_Matching_ICCV_2023_paper","paper":null,"title":"arXiv:Zeng_Parameterized_Cost_Volume_for_Stereo_Matching_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jiaxiZeng/Parameterized-Cost-Volume-for-Stereo-Matching","path":"core/update.py","file_url":"https://github.com/jiaxiZeng/Parameterized-Cost-Volume-for-Stereo-Matching/blob/HEAD/core/update.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08bedbc0c0447f5d","mcp_get_code":{"code_sha256":"08bedbc0c0447f5d"}}]}