{"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/gather","entry":"gather","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":75,"n_papers_ran":36,"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":46,"n_samples_ran":15,"n_samples_fingerprinted":3,"n_places":75,"n_places_pointer_only":16,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":3,"ran":9,"unverified":31},"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":"2606.22180","paper":"/paper/arxiv-2606-22180","title":"FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"iDC-NEU/NeutronTP","path":"models/tensplit_gcn_large.py","file_url":"https://github.com/iDC-NEU/NeutronTP/blob/HEAD/models/tensplit_gcn_large.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0ea0f96e15ee65f3","mcp_get_code":{"code_sha256":"0ea0f96e15ee65f3"}},{"arxiv_id":"2605.27311","paper":"/paper/arxiv-2605-27311","title":"CHARTOGRAPHER: Counterfactual Chart Generation for Evaluating Vision-Language Models","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"vis-nlp/ChartQA","path":"Models/VL-T5/src/dist_utils.py","file_url":"https://github.com/vis-nlp/ChartQA/blob/HEAD/Models/VL-T5/src/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"2604.27967","paper":"/paper/arxiv-2604-27967","title":"Differentiable latent structure discovery for interpretable forecasting in clinical time series","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"yalavarthivk/GraFITi","path":"grafiti/grafiti_layers.py","file_url":"https://github.com/yalavarthivk/GraFITi/blob/HEAD/grafiti/grafiti_layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c4b979466c3f0695","mcp_get_code":{"code_sha256":"c4b979466c3f0695"}},{"arxiv_id":"2604.11628","paper":"/paper/arxiv-2604-11628","title":"Back to Basics: Let Conversational Agents Remember with Just Retrieval and Generation","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"facebookresearch/contriever","path":"src/dist_utils.py","file_url":"https://github.com/facebookresearch/contriever/blob/HEAD/src/dist_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2506.21215","paper":"/paper/unveiling-causal-reasoning-in-large-language","title":"Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?","date":"2025-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Haoang97/CausalProbe-2024","path":"src/dist_utils.py","file_url":"https://github.com/Haoang97/CausalProbe-2024/blob/HEAD/src/dist_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2506.09625","paper":"/paper/glgenn-a-novel-parameter-light-equivariant","title":"GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras","date":"2025-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"katyafilimoshina/glgenn","path":"engineer/metrics/metrics.py","file_url":"https://github.com/katyafilimoshina/glgenn/blob/HEAD/engineer/metrics/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7e5690a84b931458","mcp_get_code":{"code_sha256":"7e5690a84b931458"}},{"arxiv_id":"2506.05890","paper":"/paper/unleashing-the-potential-of-consistency-1","title":"Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation","date":"2025-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyih/CSCL","path":"code/MultiModal-DeepFake-main/models/METER/dist_utils.py","file_url":"https://github.com/liyih/CSCL/blob/HEAD/code/MultiModal-DeepFake-main/models/METER/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cc8ab2b168b5373d","mcp_get_code":{"code_sha256":"cc8ab2b168b5373d"}},{"arxiv_id":"2502.11190","paper":"/paper/relearn-unlearning-via-learning-for-large","title":"ReLearn: Unlearning via Learning for Large Language Models","date":"2025-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjunlp/unlearn","path":"dataAugument/gather_proc_data.py","file_url":"https://github.com/zjunlp/unlearn/blob/HEAD/dataAugument/gather_proc_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d382772365dcb74d","mcp_get_code":{"code_sha256":"d382772365dcb74d"}},{"arxiv_id":"2502.07972","paper":"/paper/training-sparse-mixture-of-experts-text","title":"Training Sparse Mixture Of Experts Text Embedding Models","date":"2025-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nomic-ai/contrastors","path":"src/contrastors/distributed.py","file_url":"https://github.com/nomic-ai/contrastors/blob/HEAD/src/contrastors/distributed.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":"8cea512acd127521","mcp_get_code":{"code_sha256":"8cea512acd127521"}},{"arxiv_id":"2412.10193","paper":"/paper/simple-guidance-mechanisms-for-discrete","title":"Simple Guidance Mechanisms for Discrete Diffusion Models","date":"2024-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"w86763777/pytorch-image-generation-metrics","path":"pytorch_image_generation_metrics/districuted.py","file_url":"https://github.com/w86763777/pytorch-image-generation-metrics/blob/HEAD/pytorch_image_generation_metrics/districuted.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":"7ab4a7f63870dca5","mcp_get_code":{"code_sha256":"7ab4a7f63870dca5"}},{"arxiv_id":"2410.04842","paper":"/paper/a-simple-image-segmentation-framework-via-in","title":"A Simple Image Segmentation Framework via In-Context Examples","date":"2024-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aim-uofa/SINE","path":"sine/utils/comm.py","file_url":"https://github.com/aim-uofa/SINE/blob/HEAD/sine/utils/comm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"18bde1ccecf78eba","mcp_get_code":{"code_sha256":"18bde1ccecf78eba"}},{"arxiv_id":"2410.03644","paper":"/paper/unlearnable-3d-point-clouds-class-wise","title":"Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need","date":"2024-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CGCL-codes/UnlearnablePC","path":"model_utils/kpconv_util.py","file_url":"https://github.com/CGCL-codes/UnlearnablePC/blob/HEAD/model_utils/kpconv_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06eb9b038422a6ef","mcp_get_code":{"code_sha256":"06eb9b038422a6ef"}},{"arxiv_id":"2407.04363","paper":"/paper/arigraph-learning-knowledge-graph-world","title":"AriGraph: Learning Knowledge Graph World Models with Episodic Memory for LLM Agents","date":"2024-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"airi-institute/arigraph","path":"src/dist_utils.py","file_url":"https://github.com/airi-institute/arigraph/blob/HEAD/src/dist_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2404.19705","paper":"/paper/when-to-retrieve-teaching-llms-to-utilize","title":"When to Retrieve: Teaching LLMs to Utilize Information Retrieval Effectively","date":"2024-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tLabruna/Adapt-LLM","path":"contriever/src/dist_utils.py","file_url":"https://github.com/tLabruna/Adapt-LLM/blob/HEAD/contriever/src/dist_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-4.0","inline_ok":false,"code_sha256_prefix":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2403.12459","paper":"/paper/non-negative-contrastive-learning","title":"Non-negative Contrastive Learning","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pku-ml/non_neg","path":"solo/losses/nnclr.py","file_url":"https://github.com/pku-ml/non_neg/blob/HEAD/solo/losses/nnclr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"19eca70e1694ef1d","mcp_get_code":{"code_sha256":"19eca70e1694ef1d"}},{"arxiv_id":"2403.11193","paper":"/paper/neural-markov-random-field-for-stereo","title":"Neural Markov Random Field for Stereo Matching","date":"2024-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aeolusguan/NMRF","path":"nmrf/utils/dist_utils.py","file_url":"https://github.com/aeolusguan/NMRF/blob/HEAD/nmrf/utils/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7780de4d44f9b787","mcp_get_code":{"code_sha256":"7780de4d44f9b787"}},{"arxiv_id":"2403.06840","paper":"/paper/ra-isf-learning-to-answer-and-understand-from","title":"RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oceanntwt/ra-isf","path":"retrieval_contriever/src/dist_utils.py","file_url":"https://github.com/oceanntwt/ra-isf/blob/HEAD/retrieval_contriever/src/dist_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2402.07867","paper":"/paper/poisonedrag-knowledge-poisoning-attacks-to","title":"PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models","date":"2024-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sleeepeer/poisonedrag","path":"src/contriever_src/dist_utils.py","file_url":"https://github.com/sleeepeer/poisonedrag/blob/HEAD/src/contriever_src/dist_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2402.03216","paper":"/paper/bge-m3-embedding-multi-lingual-multi","title":"BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation","date":"2024-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allen-li1231/treehop-rag","path":"src/dist_utils.py","file_url":"https://github.com/allen-li1231/treehop-rag/blob/HEAD/src/dist_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2402.00838","paper":"/paper/olmo-accelerating-the-science-of-language","title":"OLMo: Accelerating the Science of Language Models","date":"2024-02-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ningyuxu/tip_of_tongue","path":"calf/utils/distributed.py","file_url":"https://github.com/ningyuxu/tip_of_tongue/blob/HEAD/calf/utils/distributed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f273d6bf63dd6913","mcp_get_code":{"code_sha256":"f273d6bf63dd6913"}},{"arxiv_id":"2401.13298","paper":"/paper/towards-explainable-harmful-meme-detection","title":"Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models","date":"2024-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hkbunlp/explainhm-www2024","path":"src/modules/dist_utils.py","file_url":"https://github.com/hkbunlp/explainhm-www2024/blob/HEAD/src/modules/dist_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":"cc8ab2b168b5373d","mcp_get_code":{"code_sha256":"cc8ab2b168b5373d"}},{"arxiv_id":"2401.10474","paper":"/paper/ldreg-local-dimensionality-regularized-self","title":"LDReg: Local Dimensionality Regularized Self-Supervised Learning","date":"2024-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HanxunH/LDReg","path":"losses/ntxent_lid_reg.py","file_url":"https://github.com/HanxunH/LDReg/blob/HEAD/losses/ntxent_lid_reg.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":"cf0782bfe6eddbac","mcp_get_code":{"code_sha256":"cf0782bfe6eddbac"}},{"arxiv_id":"2401.10215","paper":"/paper/gpavatar-generalizable-and-precise-head","title":"GPAvatar: Generalizable and Precise Head Avatar from Image(s)","date":"2024-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xg-chu/gpavatar","path":"core/utils/distributed.py","file_url":"https://github.com/xg-chu/gpavatar/blob/HEAD/core/utils/distributed.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"18bde1ccecf78eba","mcp_get_code":{"code_sha256":"18bde1ccecf78eba"}},{"arxiv_id":"2401.03145","paper":"/paper/self-supervised-feature-adaptation-for-3d","title":"Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection","date":"2024-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuanpengtu/LSFA","path":"Adaptation/NTXentLoss.py","file_url":"https://github.com/yuanpengtu/LSFA/blob/HEAD/Adaptation/NTXentLoss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d42584b747a12bb8","mcp_get_code":{"code_sha256":"d42584b747a12bb8"}},{"arxiv_id":"2401.03082","paper":"/paper/umie-unified-multimodal-information","title":"UMIE: Unified Multimodal Information Extraction with Instruction Tuning","date":"2024-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZUCC-AI/UMIE","path":"src/dist_utils.py","file_url":"https://github.com/ZUCC-AI/UMIE/blob/HEAD/src/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"2312.01697","paper":"/paper/hulk-a-universal-knowledge-translator-for","title":"Hulk: A Universal Knowledge Translator for Human-Centric Tasks","date":"2023-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/hulk","path":"core/comm_.py","file_url":"https://github.com/opengvlab/hulk/blob/HEAD/core/comm_.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"2311.09999","paper":"/paper/transfusion-a-transparency-based-diffusion","title":"TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detection","date":"2023-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maticfuc/eccv_transfusion","path":"model/TransFusion.py","file_url":"https://github.com/maticfuc/eccv_transfusion/blob/HEAD/model/TransFusion.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a77eeeca3b6710fd","mcp_get_code":{"code_sha256":"a77eeeca3b6710fd"}},{"arxiv_id":"2311.07052","paper":"/paper/towards-the-law-of-capacity-gap-in-distilling","title":"Towards the Law of Capacity Gap in Distilling Language Models","date":"2023-11-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"genezc/minima","path":"minima/run_distillation_llama_ds.py","file_url":"https://github.com/genezc/minima/blob/HEAD/minima/run_distillation_llama_ds.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":"3d58cb57472675a1","mcp_get_code":{"code_sha256":"3d58cb57472675a1"}},{"arxiv_id":"2310.18555","paper":null,"title":"arXiv:2310.18555","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":null,"repo":"tsirif/uLA","path":"ula/utils/misc.py","file_url":"https://github.com/tsirif/uLA/blob/HEAD/ula/utils/misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a448d12e56c7a36f","mcp_get_code":{"code_sha256":"a448d12e56c7a36f"}},{"arxiv_id":"2310.10196","paper":"/paper/large-models-for-time-series-and-spatio","title":"Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenhaomin/DiffSTG","path":"utils/common_utils.py","file_url":"https://github.com/wenhaomin/DiffSTG/blob/HEAD/utils/common_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2b9fe6fe2c375675","mcp_get_code":{"code_sha256":"2b9fe6fe2c375675"}},{"arxiv_id":"2309.16298","paper":"/paper/at-which-training-stage-does-cocde-data-help","title":"At Which Training Stage Does Code Data Help LLMs Reasoning?","date":"2023-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yingweima2022/codellm","path":"src/generate.py","file_url":"https://github.com/yingweima2022/codellm/blob/HEAD/src/generate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a43454e73b20e26c","mcp_get_code":{"code_sha256":"a43454e73b20e26c"}},{"arxiv_id":"2308.05525","paper":"/paper/critical-points-an-agile-point-cloud","title":"Robustifying Point Cloud Networks by Refocusing","date":"2023-08-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yossilevii100/critical_points2","path":"shape_invariant_attack/model_utils/kpconv_util.py","file_url":"https://github.com/yossilevii100/critical_points2/blob/HEAD/shape_invariant_attack/model_utils/kpconv_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"06eb9b038422a6ef","mcp_get_code":{"code_sha256":"06eb9b038422a6ef"}},{"arxiv_id":"2308.04782","paper":"/paper/pointmbf-a-multi-scale-bidirectional-fusion","title":"PointMBF: A Multi-scale Bidirectional Fusion Network for Unsupervised RGB-D Point Cloud Registration","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"phdymz/pointmbf","path":"models/block.py","file_url":"https://github.com/phdymz/pointmbf/blob/HEAD/models/block.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"06eb9b038422a6ef","mcp_get_code":{"code_sha256":"06eb9b038422a6ef"}},{"arxiv_id":"2306.16713","paper":"/paper/answer-mining-from-a-pool-of-images-towards","title":"Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering","date":"2023-06-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Abhiram4572/mi_bart","path":"mi_bart/src/dist_utils.py","file_url":"https://github.com/Abhiram4572/mi_bart/blob/HEAD/mi_bart/src/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"2306.11134","paper":"/paper/openp5-benchmarking-foundation-models-for","title":"OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems","date":"2023-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeykigung/P5","path":"src/dist_utils.py","file_url":"https://github.com/jeykigung/P5/blob/HEAD/src/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"2306.04362","paper":"/paper/youku-mplug-a-10-million-large-scale-chinese","title":"Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks","date":"2023-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"x-plug/youku-mplug","path":"models/distributed_utils.py","file_url":"https://github.com/x-plug/youku-mplug/blob/HEAD/models/distributed_utils.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":"e62848b63e5fdfa1","mcp_get_code":{"code_sha256":"e62848b63e5fdfa1"}},{"arxiv_id":"2305.14302","paper":"/paper/vip5-towards-multimodal-foundation-models-for","title":"VIP5: Towards Multimodal Foundation Models for Recommendation","date":"2023-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeykigung/vip5","path":"src/dist_utils.py","file_url":"https://github.com/jeykigung/vip5/blob/HEAD/src/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"2305.12223","paper":"/paper/what-makes-for-good-visual-tokenizers-for","title":"What Makes for Good Visual Tokenizers for Large Language Models?","date":"2023-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tencentarc/gvt","path":"gvt/gvt/modules/dist_utils.py","file_url":"https://github.com/tencentarc/gvt/blob/HEAD/gvt/gvt/modules/dist_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":"cc8ab2b168b5373d","mcp_get_code":{"code_sha256":"cc8ab2b168b5373d"}},{"arxiv_id":"2304.11582","paper":"/paper/difftraj-generating-gps-trajectory-with-1","title":"DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model","date":"2023-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Yasoz/DiffTraj","path":"utils/utils.py","file_url":"https://github.com/Yasoz/DiffTraj/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c6e806d22e50ae25","mcp_get_code":{"code_sha256":"c6e806d22e50ae25"}},{"arxiv_id":"2303.16406","paper":"/paper/hierarchical-video-moment-retrieval-and-step","title":"Hierarchical Video-Moment Retrieval and Step-Captioning","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"j-min/HiREST","path":"dist_utils.py","file_url":"https://github.com/j-min/HiREST/blob/HEAD/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"2303.15062","paper":"/paper/the-devil-is-in-the-points-weakly-semi","title":"The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/PointWSSIS","path":"MaskRefineNet/utils/distributed.py","file_url":"https://github.com/clovaai/PointWSSIS/blob/HEAD/MaskRefineNet/utils/distributed.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":"88d3340343b336ff","mcp_get_code":{"code_sha256":"88d3340343b336ff"}},{"arxiv_id":"2303.02387","paper":"/paper/towards-a-unified-theoretical-understanding","title":"Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism","date":"2023-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pku-ml/rank-differential-mechanism","path":"imagenet/main_simsiam.py","file_url":"https://github.com/pku-ml/rank-differential-mechanism/blob/HEAD/imagenet/main_simsiam.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e9e8d99a9c25e301","mcp_get_code":{"code_sha256":"e9e8d99a9c25e301"}},{"arxiv_id":"2301.10222","paper":"/paper/rangevit-towards-vision-transformers-for-3d","title":"RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving","date":"2023-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeoai/rangevit","path":"models/rangevit.py","file_url":"https://github.com/valeoai/rangevit/blob/HEAD/models/rangevit.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"06eb9b038422a6ef","mcp_get_code":{"code_sha256":"06eb9b038422a6ef"}},{"arxiv_id":"2210.16870","paper":"/paper/a-simple-efficient-and-scalable-contrastive","title":"A simple, efficient and scalable contrastive masked autoencoder for learning visual representations","date":"2022-10-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bwconrad/can","path":"src/loss.py","file_url":"https://github.com/bwconrad/can/blob/HEAD/src/loss.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a448d12e56c7a36f","mcp_get_code":{"code_sha256":"a448d12e56c7a36f"}},{"arxiv_id":"2210.01571","paper":"/paper/vicregl-self-supervised-learning-of-local","title":"VICRegL: Self-Supervised Learning of Local Visual Features","date":"2022-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lightly-ai/lightly","path":"lightly/loss/vicregl_loss.py","file_url":"https://github.com/lightly-ai/lightly/blob/HEAD/lightly/loss/vicregl_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f60f882797a1d561","mcp_get_code":{"code_sha256":"f60f882797a1d561"}},{"arxiv_id":"2208.02817","paper":"/paper/occupancy-planes-for-single-view-rgb-d-human","title":"Occupancy Planes for Single-view RGB-D Human Reconstruction","date":"2022-08-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaoming-zhao/oplanes","path":"oplanes/utils/comm.py","file_url":"https://github.com/xiaoming-zhao/oplanes/blob/HEAD/oplanes/utils/comm.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":"18bde1ccecf78eba","mcp_get_code":{"code_sha256":"18bde1ccecf78eba"}},{"arxiv_id":"2205.12454","paper":"/paper/recipe-for-a-general-powerful-scalable-graph","title":"Recipe for a General, Powerful, Scalable Graph Transformer","date":"2022-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"graphcore/ogb-lsc-pcqm4mv2","path":"model/hybrid/layers.py","file_url":"https://github.com/graphcore/ogb-lsc-pcqm4mv2/blob/HEAD/model/hybrid/layers.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df3a80c6ac89079c","mcp_get_code":{"code_sha256":"df3a80c6ac89079c"}},{"arxiv_id":"2203.15143","paper":"/paper/towards-end-to-end-unified-scene-text","title":"Towards End-to-End Unified Scene Text Detection and Layout Analysis","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research-datasets/hiertext","path":"evaluator/np_box_list_ops.py","file_url":"https://github.com/google-research-datasets/hiertext/blob/HEAD/evaluator/np_box_list_ops.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-SA-4.0","inline_ok":false,"code_sha256_prefix":"3cbdd09583615484","mcp_get_code":{"code_sha256":"3cbdd09583615484"}},{"arxiv_id":"2203.14486","paper":"/paper/equivariant-point-cloud-analysis-via-learning","title":"Equivariant Point Cloud Analysis via Learning Orientations for Message Passing","date":"2022-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luost26/equivariant-orientedmp","path":"models/cls/oriented_dgcnn.py","file_url":"https://github.com/luost26/equivariant-orientedmp/blob/HEAD/models/cls/oriented_dgcnn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e1b540b7dbb4fd7","mcp_get_code":{"code_sha256":"8e1b540b7dbb4fd7"}},{"arxiv_id":"2203.04041","paper":"/paper/shape-invariant-3d-adversarial-point-clouds","title":"Shape-invariant 3D Adversarial Point Clouds","date":"2022-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shikiw/SI-Adv","path":"model_utils/kpconv_util.py","file_url":"https://github.com/shikiw/SI-Adv/blob/HEAD/model_utils/kpconv_util.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06eb9b038422a6ef","mcp_get_code":{"code_sha256":"06eb9b038422a6ef"}},{"arxiv_id":"2111.12591","paper":"/paper/lepard-learning-partial-point-cloud-matching","title":"Lepard: Learning partial point cloud matching in rigid and deformable scenes","date":"2021-11-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rabbityl/lepard","path":"models/blocks.py","file_url":"https://github.com/rabbityl/lepard/blob/HEAD/models/blocks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06eb9b038422a6ef","mcp_get_code":{"code_sha256":"06eb9b038422a6ef"}},{"arxiv_id":"2109.04912","paper":"/paper/reasonbert-pre-trained-to-reason-with-distant","title":"ReasonBERT: Pre-trained to Reason with Distant Supervision","date":"2021-09-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunlab-osu/reasonbert","path":"data_loader/tapas_modelling.py","file_url":"https://github.com/sunlab-osu/reasonbert/blob/HEAD/data_loader/tapas_modelling.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":"ead963170369d948","mcp_get_code":{"code_sha256":"ead963170369d948"}},{"arxiv_id":"2108.02833","paper":"/paper/elaborative-rehearsal-for-zero-shot-action","title":"Elaborative Rehearsal for Zero-shot Action Recognition","date":"2021-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeLightCMU/ElaborativeRehearsal","path":"framework/dist_helper.py","file_url":"https://github.com/DeLightCMU/ElaborativeRehearsal/blob/HEAD/framework/dist_helper.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"842ad4fd53da0aec","mcp_get_code":{"code_sha256":"842ad4fd53da0aec"}},{"arxiv_id":"2106.06162","paper":"/paper/hybrid-generative-contrastive-representation","title":"Hybrid Generative-Contrastive Representation Learning","date":"2021-06-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kakaobrain/scrl","path":"distributed/comm.py","file_url":"https://github.com/kakaobrain/scrl/blob/HEAD/distributed/comm.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":"0ae40a93ce2b44c0","mcp_get_code":{"code_sha256":"0ae40a93ce2b44c0"}},{"arxiv_id":"2106.01425","paper":"/paper/gradient-assisted-learning","title":"GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"diaoenmao/GAL-Gradient-Assisted-Learning-for-Decentralized-Multi-Organization-Collaborations","path":"src/train_model_al.py","file_url":"https://github.com/diaoenmao/GAL-Gradient-Assisted-Learning-for-Decentralized-Multi-Organization-Collaborations/blob/HEAD/src/train_model_al.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b70c15bcf6a12a08","mcp_get_code":{"code_sha256":"b70c15bcf6a12a08"}},{"arxiv_id":"2105.04906","paper":"/paper/vicreg-variance-invariance-covariance","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","date":"2021-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lightly-ai/lightly","path":"lightly/loss/vicreg_loss.py","file_url":"https://github.com/lightly-ai/lightly/blob/HEAD/lightly/loss/vicreg_loss.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f4b990248d9c5170","mcp_get_code":{"code_sha256":"f4b990248d9c5170"}},{"arxiv_id":"2104.08984","paper":"/paper/contrastive-learning-improves-model","title":"Contrastive Learning Improves Model Robustness Under Label Noise","date":"2021-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arghosh/noisy_label_pretrain","path":"models/losses.py","file_url":"https://github.com/arghosh/noisy_label_pretrain/blob/HEAD/models/losses.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d5e11c2280cdb46","mcp_get_code":{"code_sha256":"3d5e11c2280cdb46"}},{"arxiv_id":"2010.02502","paper":"/paper/denoising-diffusion-implicit-models-1","title":"Denoising Diffusion Implicit Models","date":"2020-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjfghk5697/Pytorch-Research-Paper-Implementations","path":"Diffusion/DDIM/utils.py","file_url":"https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/blob/HEAD/Diffusion/DDIM/utils.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":"ea4eae1fc415c0a6","mcp_get_code":{"code_sha256":"ea4eae1fc415c0a6"}},{"arxiv_id":"2007.12668","paper":"/paper/kprnet-improving-projection-based-lidar","title":"KPRNet: Improving projection-based LiDAR semantic segmentation","date":"2020-07-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeyvidKochanov-TomTom/kprnet","path":"models/kpconv/blocks.py","file_url":"https://github.com/DeyvidKochanov-TomTom/kprnet/blob/HEAD/models/kpconv/blocks.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06eb9b038422a6ef","mcp_get_code":{"code_sha256":"06eb9b038422a6ef"}},{"arxiv_id":"2004.12989","paper":"/paper/corenet-coherent-3d-scene-reconstruction-from","title":"CoReNet: Coherent 3D scene reconstruction from a single RGB image","date":"2020-04-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/corenet","path":"src/corenet/distributed.py","file_url":"https://github.com/google-research/corenet/blob/HEAD/src/corenet/distributed.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":"6d011553084806dd","mcp_get_code":{"code_sha256":"6d011553084806dd"}},{"arxiv_id":"2004.02349","paper":"/paper/tapas-weakly-supervised-table-parsing-via-pre","title":"TAPAS: Weakly Supervised Table Parsing via Pre-training","date":"2020-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kamalkraj/TAPAS-TF2","path":"tapas/models/segmented_tensor.py","file_url":"https://github.com/kamalkraj/TAPAS-TF2/blob/HEAD/tapas/models/segmented_tensor.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":"5627e0c233b85bf2","mcp_get_code":{"code_sha256":"5627e0c233b85bf2"}},{"arxiv_id":"2003.09163","paper":"/paper/detection-in-crowded-scenes-one-proposal","title":"Detection in Crowded Scenes: One Proposal, Multiple Predictions","date":"2020-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-model/CrowdDetection","path":"evaluate/compute_JI.py","file_url":"https://github.com/megvii-model/CrowdDetection/blob/HEAD/evaluate/compute_JI.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":"fabe07d7d0c3a52c","mcp_get_code":{"code_sha256":"fabe07d7d0c3a52c"}},{"arxiv_id":"1904.08889","paper":"/paper/kpconv-flexible-and-deformable-convolution","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","date":"2019-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"isl-org/Open3D-ML","path":"ml3d/torch/models/kpconv.py","file_url":"https://github.com/isl-org/Open3D-ML/blob/HEAD/ml3d/torch/models/kpconv.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"9a89c500824a1c5c","mcp_get_code":{"code_sha256":"9a89c500824a1c5c"}},{"arxiv_id":"1904.07850","paper":"/paper/objects-as-points","title":"Objects as Points","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tensorboy/centerpose","path":"demo/convert2onnx.py","file_url":"https://github.com/tensorboy/centerpose/blob/HEAD/demo/convert2onnx.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4b7b909c66e73f75","mcp_get_code":{"code_sha256":"4b7b909c66e73f75"}},{"arxiv_id":"1811.06965","paper":"/paper/gpipe-efficient-training-of-giant-neural","title":"GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism","date":"2018-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KakaoBrain/torchgpipe","path":"torchgpipe/microbatch.py","file_url":"https://github.com/KakaoBrain/torchgpipe/blob/HEAD/torchgpipe/microbatch.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":"9463c0f5ec4c4938","mcp_get_code":{"code_sha256":"9463c0f5ec4c4938"}},{"arxiv_id":"1806.01261","paper":"/paper/relational-inductive-biases-deep-learning-and","title":"Relational inductive biases, deep learning, and graph networks","date":"2018-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mylonasc/tf-gnns","path":"tf_gnns/backend_ops.py","file_url":"https://github.com/mylonasc/tf-gnns/blob/HEAD/tf_gnns/backend_ops.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":"0064a27d6d3cb322","mcp_get_code":{"code_sha256":"0064a27d6d3cb322"}},{"arxiv_id":"aaai_25158","paper":null,"title":"arXiv:aaai_25158","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"transvcl/TransVCL","path":"transvcl/utils/dist.py","file_url":"https://github.com/transvcl/TransVCL/blob/HEAD/transvcl/utils/dist.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d171a9b9dac98af4","mcp_get_code":{"code_sha256":"d171a9b9dac98af4"}},{"arxiv_id":"aaai_21320","paper":null,"title":"arXiv:aaai_21320","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"siat-nlp/GALAXY","path":"galaxy/models/generator.py","file_url":"https://github.com/siat-nlp/GALAXY/blob/HEAD/galaxy/models/generator.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":"4258fc4d59588f88","mcp_get_code":{"code_sha256":"4258fc4d59588f88"}},{"arxiv_id":"Zhang_VQACL_A_Novel_Visual_Question_Answering_Continual_Learning_Setting_CVPR_2023_paper","paper":null,"title":"arXiv:Zhang_VQACL_A_Novel_Visual_Question_Answering_Continual_Learning_Setting_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zhangxi1997/VQACL","path":"VL-T5/src/dist_utils.py","file_url":"https://github.com/zhangxi1997/VQACL/blob/HEAD/VL-T5/src/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper","paper":null,"title":"arXiv:Ao_BUFFER_Balancing_Accuracy_Efficiency_and_Generalizability_in_Point_Cloud_Registration_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"aosheng1996/BUFFER","path":"models/point_learner.py","file_url":"https://github.com/aosheng1996/BUFFER/blob/HEAD/models/point_learner.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06eb9b038422a6ef","mcp_get_code":{"code_sha256":"06eb9b038422a6ef"}},{"arxiv_id":"2025.findings-acl.1365","paper":null,"title":"arXiv:2025.findings-acl.1365","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ldilab/ECoRAG","path":"compressor/src/dist_utils.py","file_url":"https://github.com/ldilab/ECoRAG/blob/HEAD/compressor/src/dist_utils.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":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2025.emnlp-main.192","paper":null,"title":"arXiv:2025.emnlp-main.192","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"FreedomIntelligence/RAG-Instruct","path":"retrieval_lm/src/dist_utils.py","file_url":"https://github.com/FreedomIntelligence/RAG-Instruct/blob/HEAD/retrieval_lm/src/dist_utils.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":"9fb6e57668661c38","mcp_get_code":{"code_sha256":"9fb6e57668661c38"}},{"arxiv_id":"2023.findings-emnlp.644","paper":null,"title":"arXiv:2023.findings-emnlp.644","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jeykigung/VIP5","path":"src/dist_utils.py","file_url":"https://github.com/jeykigung/VIP5/blob/HEAD/src/dist_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"00a9f7918ac39329","mcp_get_code":{"code_sha256":"00a9f7918ac39329"}},{"arxiv_id":"136730727","paper":null,"title":"arXiv:136730727","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"louisYen/S3R","path":"anomaly/apis/comm.py","file_url":"https://github.com/louisYen/S3R/blob/HEAD/anomaly/apis/comm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f14194e77340fc7","mcp_get_code":{"code_sha256":"7f14194e77340fc7"}},{"arxiv_id":"05117","paper":null,"title":"arXiv:05117","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"MaticFuc/ECCV_TransFusion","path":"model/TransFusion.py","file_url":"https://github.com/MaticFuc/ECCV_TransFusion/blob/HEAD/model/TransFusion.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a77eeeca3b6710fd","mcp_get_code":{"code_sha256":"a77eeeca3b6710fd"}}]}