{"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/evaluation","entry":"evaluation","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":57,"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":59,"n_samples_ran":16,"n_samples_fingerprinted":1,"n_places":65,"n_places_pointer_only":23,"by_status":{"ran_honours":2,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":3,"ran":7,"unverified":43},"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":"2606.19374","paper":"/paper/arxiv-2606-19374","title":"Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"mohamedmohamed2021/SSProNet","path":"run_ProNet.py","file_url":"https://github.com/mohamedmohamed2021/SSProNet/blob/HEAD/run_ProNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"721646cdc27b9f30","mcp_get_code":{"code_sha256":"721646cdc27b9f30"}},{"arxiv_id":"2604.22661","paper":"/paper/arxiv-2604-22661","title":"Can QPP Choose the Right Query variant? Evaluating Query Variant Selection for RAG Pipelines","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"Narabzad/QPP-4-RAG","path":"QPP4CS/evaluation_QPP.py","file_url":"https://github.com/Narabzad/QPP-4-RAG/blob/HEAD/QPP4CS/evaluation_QPP.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0b535ecda28038b9","mcp_get_code":{"code_sha256":"0b535ecda28038b9"}},{"arxiv_id":"2601.15316","paper":"/paper/arxiv-2601-15316","title":"The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"majingCUHK/Rumor_RvNN","path":"model/evaluate.py","file_url":"https://github.com/majingCUHK/Rumor_RvNN/blob/HEAD/model/evaluate.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a3772b851907a3b6","mcp_get_code":{"code_sha256":"a3772b851907a3b6"}},{"arxiv_id":"2510.18360","paper":"/paper/arxiv-2510-18360","title":"Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"y0ngjaenius/CVPR2024_FLOWERFormer","path":"utils.py","file_url":"https://github.com/y0ngjaenius/CVPR2024_FLOWERFormer/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fa3c832aaf87cffc","mcp_get_code":{"code_sha256":"fa3c832aaf87cffc"}},{"arxiv_id":"2411.02988","paper":"/paper/confidence-calibration-of-classifiers-with","title":"Confidence Calibration of Classifiers with Many Classes","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lifan-yuan/PLMCalibration","path":"prompt-ood.py","file_url":"https://github.com/lifan-yuan/PLMCalibration/blob/HEAD/prompt-ood.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5439c7c6310eaacf","mcp_get_code":{"code_sha256":"5439c7c6310eaacf"}},{"arxiv_id":"2411.02988","paper":"/paper/confidence-calibration-of-classifiers-with","title":"Confidence Calibration of Classifiers with Many Classes","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lifan-yuan/PLMCalibration","path":"prompt-emergent.py","file_url":"https://github.com/lifan-yuan/PLMCalibration/blob/HEAD/prompt-emergent.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"85faff7a2af04107","mcp_get_code":{"code_sha256":"85faff7a2af04107"}},{"arxiv_id":"2410.16130","paper":"/paper/can-large-audio-language-models-truly-hear","title":"Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning","date":"2024-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kuan2jiu99/audio-hallucination","path":"icassp2025/evaluation.py","file_url":"https://github.com/kuan2jiu99/audio-hallucination/blob/HEAD/icassp2025/evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ccc0c3e69385b7eb","mcp_get_code":{"code_sha256":"ccc0c3e69385b7eb"}},{"arxiv_id":"2410.16130","paper":"/paper/can-large-audio-language-models-truly-hear","title":"Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning","date":"2024-10-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kuan2jiu99/audio-hallucination","path":"interspeech2024/evaluation.py","file_url":"https://github.com/kuan2jiu99/audio-hallucination/blob/HEAD/interspeech2024/evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8d1a616242359eee","mcp_get_code":{"code_sha256":"8d1a616242359eee"}},{"arxiv_id":"2409.19886","paper":"/paper/routerdc-query-based-router-by-dual","title":"RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models","date":"2024-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shuhao02/RouterDC","path":"train_router_mdeberta.py","file_url":"https://github.com/shuhao02/RouterDC/blob/HEAD/train_router_mdeberta.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f2bce34f97bf32be","mcp_get_code":{"code_sha256":"f2bce34f97bf32be"}},{"arxiv_id":"2409.19886","paper":"/paper/routerdc-query-based-router-by-dual","title":"RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models","date":"2024-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shuhao02/RouterDC","path":"train_router_mdeberta_routerbench.py","file_url":"https://github.com/shuhao02/RouterDC/blob/HEAD/train_router_mdeberta_routerbench.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"686862d6e6432442","mcp_get_code":{"code_sha256":"686862d6e6432442"}},{"arxiv_id":"2404.18185","paper":"/paper/ranked-list-truncation-for-large-language","title":"Ranked List Truncation for Large Language Model-based Re-Ranking","date":"2024-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chuanmeng/rlt4reranking","path":"rlt/evaluation.py","file_url":"https://github.com/chuanmeng/rlt4reranking/blob/HEAD/rlt/evaluation.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"62643434cfae56cf","mcp_get_code":{"code_sha256":"62643434cfae56cf"}},{"arxiv_id":"2404.12726","paper":"/paper/evaluating-character-understanding-of-large","title":"Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works","date":"2024-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joanna0123/character_profiling","path":"code/evaluation_score.py","file_url":"https://github.com/joanna0123/character_profiling/blob/HEAD/code/evaluation_score.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"72ce9df219e77438","mcp_get_code":{"code_sha256":"72ce9df219e77438"}},{"arxiv_id":"2404.01012","paper":"/paper/query-performance-prediction-using-relevance","title":"Query Performance Prediction using Relevance Judgments Generated by Large Language Models","date":"2024-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chuanmeng/qpp-genre","path":"evaluate_qpp.py","file_url":"https://github.com/chuanmeng/qpp-genre/blob/HEAD/evaluate_qpp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cff293c53a348a4b","mcp_get_code":{"code_sha256":"cff293c53a348a4b"}},{"arxiv_id":"2403.12821","paper":"/paper/flowerformer-empowering-neural-architecture","title":"FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph Transformer","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"y0ngjaenius/cvpr2024_flowerformer","path":"utils.py","file_url":"https://github.com/y0ngjaenius/cvpr2024_flowerformer/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fa3c832aaf87cffc","mcp_get_code":{"code_sha256":"fa3c832aaf87cffc"}},{"arxiv_id":"2403.01400","paper":"/paper/decoupling-weighing-and-selecting-for","title":"Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TianyuFan0504/WAS","path":"node-level/src/utils.py","file_url":"https://github.com/TianyuFan0504/WAS/blob/HEAD/node-level/src/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d741c615263a94db","mcp_get_code":{"code_sha256":"d741c615263a94db"}},{"arxiv_id":"2402.09623","paper":"/paper/conformalized-adaptive-forecasting-of","title":"Conformalized Adaptive Forecasting of Heterogeneous Trajectories","date":"2024-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fionaz3696/cafht","path":"ConformalizedTS/evals.py","file_url":"https://github.com/fionaz3696/cafht/blob/HEAD/ConformalizedTS/evals.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"af0a444dd2175e9a","mcp_get_code":{"code_sha256":"af0a444dd2175e9a"}},{"arxiv_id":"2401.00825","paper":"/paper/sharp-nerf-grid-based-fast-deblurring-neural","title":"Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Fields Using Sharpness Prior","date":"2024-01-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"benhenryl/sharpnerf","path":"renderer.py","file_url":"https://github.com/benhenryl/sharpnerf/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e11cbdeef08d1b1","mcp_get_code":{"code_sha256":"8e11cbdeef08d1b1"}},{"arxiv_id":"2312.10034","paper":"/paper/slimmerf-slimmable-radiance-fields","title":"SlimmeRF: Slimmable Radiance Fields","date":"2023-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shiran-yuan/slimmerf","path":"renderer.py","file_url":"https://github.com/shiran-yuan/slimmerf/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa41cb74893ae745","mcp_get_code":{"code_sha256":"aa41cb74893ae745"}},{"arxiv_id":"2312.06855","paper":"/paper/multimodal-pretraining-of-medical-time-series","title":"Multimodal Pretraining of Medical Time Series and Notes","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kingrc15/multimodal-clinical-pretraining","path":"experiments/measurement_notes/measurement_notes_pretraining.py","file_url":"https://github.com/kingrc15/multimodal-clinical-pretraining/blob/HEAD/experiments/measurement_notes/measurement_notes_pretraining.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"be795e4ecd30e0a9","mcp_get_code":{"code_sha256":"be795e4ecd30e0a9"}},{"arxiv_id":"2312.03391","paper":"/paper/action-scene-graphs-for-long-form","title":"Action Scene Graphs for Long-Form Understanding of Egocentric Videos","date":"2023-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fpv-iplab/easg","path":"easg-generation/run_easg.py","file_url":"https://github.com/fpv-iplab/easg/blob/HEAD/easg-generation/run_easg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00bdfbb10c91c2eb","mcp_get_code":{"code_sha256":"00bdfbb10c91c2eb"}},{"arxiv_id":"2312.03203","paper":"/paper/feature-3dgs-supercharging-3d-gaussian","title":"Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields","date":"2023-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"keloee/maskfield","path":"renderer.py","file_url":"https://github.com/keloee/maskfield/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0b4c49066bad50f8","mcp_get_code":{"code_sha256":"0b4c49066bad50f8"}},{"arxiv_id":"2311.11227","paper":"/paper/fedra-a-random-allocation-strategy-for","title":"FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients","date":"2023-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leondada/fedra","path":"utils.py","file_url":"https://github.com/leondada/fedra/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"97ca3c78079ab836","mcp_get_code":{"code_sha256":"97ca3c78079ab836"}},{"arxiv_id":"2311.04939","paper":"/paper/loogle-can-long-context-language-models","title":"LooGLE: Can Long-Context Language Models Understand Long Contexts?","date":"2023-11-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bigai-nlco/loogle","path":"Evaluation/automatic_eval.py","file_url":"https://github.com/bigai-nlco/loogle/blob/HEAD/Evaluation/automatic_eval.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ddadc41b94b41c9e","mcp_get_code":{"code_sha256":"ddadc41b94b41c9e"}},{"arxiv_id":"2310.01821","paper":"/paper/mimo-nerf-fast-neural-rendering-with-multi-1","title":"MIMO-NeRF: Fast Neural Rendering with Multi-input Multi-output Neural Radiance Fields","date":"2023-10-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apchenstu/TensoRF","path":"renderer.py","file_url":"https://github.com/apchenstu/TensoRF/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aa41cb74893ae745","mcp_get_code":{"code_sha256":"aa41cb74893ae745"}},{"arxiv_id":"2310.01180","paper":"/paper/evolutionary-neural-architecture-search-for-1","title":"Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DevilYangS/ENAS-KT","path":"EvoTransformer.py","file_url":"https://github.com/DevilYangS/ENAS-KT/blob/HEAD/EvoTransformer.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"991bdeb73a94fdd8","mcp_get_code":{"code_sha256":"991bdeb73a94fdd8"}},{"arxiv_id":"2310.12987","paper":"/paper/spec-nerf-multi-spectral-neural-radiance","title":"Spec-NeRF: Multi-spectral Neural Radiance Fields","date":"2023-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cpregroup/specnerf-v2","path":"renderer.py","file_url":"https://github.com/cpregroup/specnerf-v2/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"aa41cb74893ae745","mcp_get_code":{"code_sha256":"aa41cb74893ae745"}},{"arxiv_id":"2308.13234","paper":"/paper/decoding-natural-images-from-eeg-for-object","title":"Decoding Natural Images from EEG for Object Recognition","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaozhangyes/cognitioncapturer","path":"src/models/components/utils.py","file_url":"https://github.com/xiaozhangyes/cognitioncapturer/blob/HEAD/src/models/components/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e2c3c0b6fb6fe418","mcp_get_code":{"code_sha256":"e2c3c0b6fb6fe418"}},{"arxiv_id":"2308.05309","paper":"/paper/homophily-enhanced-structure-learning-for","title":"Homophily-enhanced Structure Learning for Graph Clustering","date":"2023-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"galogm/hole","path":"utils/evaluation.py","file_url":"https://github.com/galogm/hole/blob/HEAD/utils/evaluation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8a940b576d09933c","mcp_get_code":{"code_sha256":"8a940b576d09933c"}},{"arxiv_id":"2307.02469","paper":"/paper/what-matters-in-training-a-gpt4-style","title":"What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?","date":"2023-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/lynx-llm","path":"generate.py","file_url":"https://github.com/bytedance/lynx-llm/blob/HEAD/generate.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cd56d16a521c2a10","mcp_get_code":{"code_sha256":"cd56d16a521c2a10"}},{"arxiv_id":"2305.17455","paper":"/paper/crossget-cross-guided-ensemble-of-tokens-for","title":"CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers","date":"2023-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sdc17/CrossGET","path":"CLIP/train_retrieval_clip.py","file_url":"https://github.com/sdc17/CrossGET/blob/HEAD/CLIP/train_retrieval_clip.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"d1e13682bf9d66ff","mcp_get_code":{"code_sha256":"d1e13682bf9d66ff"}},{"arxiv_id":"2305.14093","paper":"/paper/weakly-supervised-3d-open-vocabulary-1","title":"Weakly Supervised 3D Open-vocabulary Segmentation","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kunhao-Liu/3D-OVS","path":"renderer.py","file_url":"https://github.com/Kunhao-Liu/3D-OVS/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9a9881451ca96ede","mcp_get_code":{"code_sha256":"9a9881451ca96ede"}},{"arxiv_id":"2305.11588","paper":"/paper/text2nerf-text-driven-3d-scene-generation","title":"Text2NeRF: Text-Driven 3D Scene Generation with Neural Radiance Fields","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eckertzhang/text2nerf","path":"renderer.py","file_url":"https://github.com/eckertzhang/text2nerf/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7495b668be4d84cf","mcp_get_code":{"code_sha256":"7495b668be4d84cf"}},{"arxiv_id":"2303.03808","paper":"/paper/multiscale-tensor-decomposition-and-rendering","title":"Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis","date":"2023-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"imkanghan/nrff","path":"renderer.py","file_url":"https://github.com/imkanghan/nrff/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"31ef772e5aa195a6","mcp_get_code":{"code_sha256":"31ef772e5aa195a6"}},{"arxiv_id":"2302.01226","paper":"/paper/factor-fields-a-unified-framework-for-neural","title":"Factor Fields: A Unified Framework for Neural Fields and Beyond","date":"2023-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"autonomousvision/factor-fields","path":"renderer.py","file_url":"https://github.com/autonomousvision/factor-fields/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"327ff36daa2346f2","mcp_get_code":{"code_sha256":"327ff36daa2346f2"}},{"arxiv_id":"2211.00151","paper":"/paper/a-close-look-into-the-calibration-of-pre","title":"A Close Look into the Calibration of Pre-trained Language Models","date":"2022-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lifan-yuan/plmcalibration","path":"prompt-ood.py","file_url":"https://github.com/lifan-yuan/plmcalibration/blob/HEAD/prompt-ood.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5439c7c6310eaacf","mcp_get_code":{"code_sha256":"5439c7c6310eaacf"}},{"arxiv_id":"2209.15266","paper":"/paper/data-poisoning-attacks-against-multimodal","title":"Data Poisoning Attacks Against Multimodal Encoders","date":"2022-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zqypku/mm_poison","path":"retrieval_by_CLIP.py","file_url":"https://github.com/zqypku/mm_poison/blob/HEAD/retrieval_by_CLIP.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4a5d89815e14d530","mcp_get_code":{"code_sha256":"4a5d89815e14d530"}},{"arxiv_id":"2207.10667","paper":"/paper/online-domain-adaptation-for-semantic","title":"Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions","date":"2022-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"theo2021/OnDA","path":"framework/domain_adaptation/methods/prototypes.py","file_url":"https://github.com/theo2021/OnDA/blob/HEAD/framework/domain_adaptation/methods/prototypes.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-2.0","inline_ok":false,"code_sha256_prefix":"cf0328586e1295aa","mcp_get_code":{"code_sha256":"cf0328586e1295aa"}},{"arxiv_id":"2206.06640","paper":"/paper/confidence-score-for-source-free-unsupervised","title":"Confidence Score for Source-Free Unsupervised Domain Adaptation","date":"2022-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhyun17/cowa-jmds","path":"image_target_CoWA.py","file_url":"https://github.com/jhyun17/cowa-jmds/blob/HEAD/image_target_CoWA.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c0f8665d6d8818eb","mcp_get_code":{"code_sha256":"c0f8665d6d8818eb"}},{"arxiv_id":"2205.14870","paper":"/paper/compressible-composable-nerf-via-rank","title":"Compressible-composable NeRF via Rank-residual Decomposition","date":"2022-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashawkey/CCNeRF","path":"renderer.py","file_url":"https://github.com/ashawkey/CCNeRF/blob/HEAD/renderer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e0fb44e80a373df5","mcp_get_code":{"code_sha256":"e0fb44e80a373df5"}},{"arxiv_id":"2205.03860","paper":"/paper/zero-and-r2d2-a-large-scale-chinese-cross","title":"CCMB: A Large-scale Chinese Cross-modal Benchmark","date":"2022-05-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuxie11/R2D2","path":"r2d2_inference_demo.py","file_url":"https://github.com/yuxie11/R2D2/blob/HEAD/r2d2_inference_demo.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":"be3c5540222af5be","mcp_get_code":{"code_sha256":"be3c5540222af5be"}},{"arxiv_id":"2201.11957","paper":"/paper/global-reasoned-multi-task-learning-model-for","title":"Global-Reasoned Multi-Task Learning Model for Surgical Scene Understanding","date":"2022-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"89a1d6919a0782a9","mcp_get_code":{"code_sha256":"89a1d6919a0782a9"}},{"arxiv_id":"2110.06831","paper":"/paper/safe-driving-via-expert-guided-policy","title":"Safe Driving via Expert Guided Policy Optimization","date":"2021-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"decisionforce/EGPO","path":"egpo_utils/dagger/utils.py","file_url":"https://github.com/decisionforce/EGPO/blob/HEAD/egpo_utils/dagger/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"af575f58b460350e","mcp_get_code":{"code_sha256":"af575f58b460350e"}},{"arxiv_id":"2106.10197","paper":"/paper/a-dynamic-spatial-temporal-attention-network","title":"A Dynamic Spatial-temporal Attention Network for Early Anticipation of Traffic Accidents","date":"2021-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"monjurulkarim/DSTA","path":"src/eval_tools.py","file_url":"https://github.com/monjurulkarim/DSTA/blob/HEAD/src/eval_tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b54236206b80b795","mcp_get_code":{"code_sha256":"b54236206b80b795"}},{"arxiv_id":"2101.10774","paper":"/paper/lightweight-multi-branch-network-for-person","title":"Lightweight Multi-Branch Network for Person Re-Identification","date":"2021-01-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jixunbo/LightMBN","path":"utils/functions.py","file_url":"https://github.com/jixunbo/LightMBN/blob/HEAD/utils/functions.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"08c4c287ecfd8475","mcp_get_code":{"code_sha256":"08c4c287ecfd8475"}},{"arxiv_id":"2010.16056","paper":"/paper/generating-radiology-reports-via-memory","title":"Generating Radiology Reports via Memory-driven Transformer","date":"2020-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jbdel/vilmedic","path":"vilmedic/models/rrg/RRG.py","file_url":"https://github.com/jbdel/vilmedic/blob/HEAD/vilmedic/models/rrg/RRG.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90a7d4b65fda9753","mcp_get_code":{"code_sha256":"90a7d4b65fda9753"}},{"arxiv_id":"2008.00334","paper":"/paper/uncertainty-based-traffic-accident","title":"Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning","date":"2020-08-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Cogito2012/UString","path":"src/eval_tools.py","file_url":"https://github.com/Cogito2012/UString/blob/HEAD/src/eval_tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"491e614341f4f94d","mcp_get_code":{"code_sha256":"491e614341f4f94d"}},{"arxiv_id":"2007.08854","paper":"/paper/dvi-depth-guided-video-inpainting-for","title":"DVI: Depth Guided Video Inpainting for Autonomous Driving","date":"2020-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ApolloScapeAuto/dataset-api","path":"trajectory_prediction/evaluation.py","file_url":"https://github.com/ApolloScapeAuto/dataset-api/blob/HEAD/trajectory_prediction/evaluation.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":"45585870c1fd0413","mcp_get_code":{"code_sha256":"45585870c1fd0413"}},{"arxiv_id":"2007.03357","paper":"/paper/learning-and-reasoning-with-the-graph","title":"Learning and Reasoning with the Graph Structure Representation in Robotic Surgery","date":"2020-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mobarakol/Surgical_SceneGraph_Generation","path":"evaluation_metrics.py","file_url":"https://github.com/mobarakol/Surgical_SceneGraph_Generation/blob/HEAD/evaluation_metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"89a1d6919a0782a9","mcp_get_code":{"code_sha256":"89a1d6919a0782a9"}},{"arxiv_id":"1909.06654","paper":"/paper/musicnn-pre-trained-convolutional-neural","title":"musicnn: Pre-trained convolutional neural networks for music audio tagging","date":"2019-09-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jordipons/musicnn-training","path":"src/evaluate.py","file_url":"https://github.com/jordipons/musicnn-training/blob/HEAD/src/evaluate.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"ISC","inline_ok":true,"code_sha256_prefix":"778f3293c69fd511","mcp_get_code":{"code_sha256":"778f3293c69fd511"}},{"arxiv_id":"1909.05235","paper":"/paper/softtriple-loss-deep-metric-learning-without","title":"SoftTriple Loss: Deep Metric Learning Without Triplet Sampling","date":"2019-09-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"idstcv/SoftTriple","path":"evaluation.py","file_url":"https://github.com/idstcv/SoftTriple/blob/HEAD/evaluation.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":"d120bf5b3dee8b75","mcp_get_code":{"code_sha256":"d120bf5b3dee8b75"}},{"arxiv_id":"1901.08149","paper":"/paper/transfertransfo-a-transfer-learning-approach","title":"TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents","date":"2019-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"e46d6b86df789052","mcp_get_code":{"code_sha256":"e46d6b86df789052"}},{"arxiv_id":"1805.10627","paper":"/paper/reliability-and-learnability-of-human-bandit","title":"Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning","date":"2018-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juliakreutzer/bandit-neuralmonkey","path":"neuralmonkey/learning_utils.py","file_url":"https://github.com/juliakreutzer/bandit-neuralmonkey/blob/HEAD/neuralmonkey/learning_utils.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":"4702de42b8eb5525","mcp_get_code":{"code_sha256":"4702de42b8eb5525"}},{"arxiv_id":"1803.03467","paper":"/paper/ripplenet-propagating-user-preferences-on-the","title":"RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems","date":"2018-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Hank-Kuo/RippleNet","path":"evaluate.py","file_url":"https://github.com/Hank-Kuo/RippleNet/blob/HEAD/evaluate.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a863327019290a51","mcp_get_code":{"code_sha256":"a863327019290a51"}},{"arxiv_id":"1801.07243","paper":"/paper/personalizing-dialogue-agents-i-have-a-dog-do","title":"Personalizing Dialogue Agents: I have a dog, do you have pets too?","date":"2018-01-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samsonleegh/convai_smile","path":"model_pipeline/model_train.py","file_url":"https://github.com/samsonleegh/convai_smile/blob/HEAD/model_pipeline/model_train.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e46d6b86df789052","mcp_get_code":{"code_sha256":"e46d6b86df789052"}},{"arxiv_id":"1702.03814","paper":"/paper/bilateral-multi-perspective-matching-for","title":"Bilateral Multi-Perspective Matching for Natural Language Sentences","date":"2017-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhiguowang/BiMPM","path":"src/SentenceMatchTrainer.py","file_url":"https://github.com/zhiguowang/BiMPM/blob/HEAD/src/SentenceMatchTrainer.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":"9a2fbfc157264b95","mcp_get_code":{"code_sha256":"9a2fbfc157264b95"}},{"arxiv_id":"1612.07695","paper":"/paper/multinet-real-time-joint-semantic-reasoning","title":"MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving","date":"2016-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MarvinTeichmann/KittiSeg","path":"decoder/kitti_multiloss.py","file_url":"https://github.com/MarvinTeichmann/KittiSeg/blob/HEAD/decoder/kitti_multiloss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9897841211ca8e31","mcp_get_code":{"code_sha256":"9897841211ca8e31"}},{"arxiv_id":"1612.07695","paper":"/paper/multinet-real-time-joint-semantic-reasoning","title":"MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving","date":"2016-12-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ziyuan400/video_segmentation","path":"decoder/kitti_multiloss.py","file_url":"https://github.com/ziyuan400/video_segmentation/blob/HEAD/decoder/kitti_multiloss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"64d216d65449fe96","mcp_get_code":{"code_sha256":"64d216d65449fe96"}},{"arxiv_id":"aaai_25588","paper":null,"title":"arXiv:aaai_25588","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wdimmy/Var2Vec","path":"kbc/metrics.py","file_url":"https://github.com/wdimmy/Var2Vec/blob/HEAD/kbc/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"94299beed7c12adb","mcp_get_code":{"code_sha256":"94299beed7c12adb"}},{"arxiv_id":"aaai_20057","paper":null,"title":"arXiv:aaai_20057","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jackie840129/FedFR","path":"local_1n.py","file_url":"https://github.com/jackie840129/FedFR/blob/HEAD/local_1n.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"af0a3d68f2ad21de","mcp_get_code":{"code_sha256":"af0a3d68f2ad21de"}},{"arxiv_id":"2023.findings-emnlp.672","paper":null,"title":"arXiv:2023.findings-emnlp.672","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"guihuzhang/FactSpotter","path":"fact_spotter_training/train_classifier_dart.py","file_url":"https://github.com/guihuzhang/FactSpotter/blob/HEAD/fact_spotter_training/train_classifier_dart.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5643f5f42fea6fd2","mcp_get_code":{"code_sha256":"5643f5f42fea6fd2"}},{"arxiv_id":"2023.findings-emnlp.672","paper":null,"title":"arXiv:2023.findings-emnlp.672","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"guihuzhang/FactSpotter","path":"fact_spotter_training/train_classifier_grail.py","file_url":"https://github.com/guihuzhang/FactSpotter/blob/HEAD/fact_spotter_training/train_classifier_grail.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"daa28a651a2a68dd","mcp_get_code":{"code_sha256":"daa28a651a2a68dd"}},{"arxiv_id":"2023.findings-emnlp.672","paper":null,"title":"arXiv:2023.findings-emnlp.672","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"guihuzhang/FactSpotter","path":"fact_spotter_training/train_classifier_spq.py","file_url":"https://github.com/guihuzhang/FactSpotter/blob/HEAD/fact_spotter_training/train_classifier_spq.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e8d700e822c0b074","mcp_get_code":{"code_sha256":"e8d700e822c0b074"}},{"arxiv_id":"136890323","paper":null,"title":"arXiv:136890323","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KAIST-vilab/AEFT","path":"evaluation_aff.py","file_url":"https://github.com/KAIST-vilab/AEFT/blob/HEAD/evaluation_aff.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2aa283301c8b9620","mcp_get_code":{"code_sha256":"2aa283301c8b9620"}},{"arxiv_id":"136890323","paper":null,"title":"arXiv:136890323","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KAIST-vilab/AEFT","path":"infer_aff.py","file_url":"https://github.com/KAIST-vilab/AEFT/blob/HEAD/infer_aff.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13c8d9f29398c91b","mcp_get_code":{"code_sha256":"13c8d9f29398c91b"}},{"arxiv_id":"136890323","paper":null,"title":"arXiv:136890323","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"KAIST-vilab/AEFT","path":"train_aff.py","file_url":"https://github.com/KAIST-vilab/AEFT/blob/HEAD/train_aff.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fb6d296eac8fcef","mcp_get_code":{"code_sha256":"2fb6d296eac8fcef"}}]}