{"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/edge2mat","entry":"edge2mat","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":8,"n_papers_ran":8,"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":2,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":0},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2604.23264","paper":"/paper/arxiv-2604-23264","title":"MotionHiFlow: Text-to-Motion via Hierarchical Flow Matching","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"ai-lh/MotionHiFlow","path":"src/utils/graph_tools.py","file_url":"https://github.com/ai-lh/MotionHiFlow/blob/HEAD/src/utils/graph_tools.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":"5f5ae82020ac7ec2","mcp_get_code":{"code_sha256":"5f5ae82020ac7ec2"}},{"arxiv_id":"2310.16288","paper":"/paper/motionagformer-enhancing-3d-human-pose","title":"MotionAGFormer: Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network","date":"2023-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taatiteam/motionagformer","path":"model/modules/ctrgc.py","file_url":"https://github.com/taatiteam/motionagformer/blob/HEAD/model/modules/ctrgc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5f5ae82020ac7ec2","mcp_get_code":{"code_sha256":"5f5ae82020ac7ec2"}},{"arxiv_id":"2310.16035","paper":"/paper/what-s-left-concept-grounding-with-logic","title":"What's Left? Concept Grounding with Logic-Enhanced Foundation Models","date":"2023-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joyhsu0504/left","path":"left/nn/agcn/agcn_graph.py","file_url":"https://github.com/joyhsu0504/left/blob/HEAD/left/nn/agcn/agcn_graph.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"02f326c9c80bd20a","mcp_get_code":{"code_sha256":"02f326c9c80bd20a"}},{"arxiv_id":"2305.11468","paper":"/paper/overcoming-topology-agnosticism-enhancing","title":"Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology Awareness","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhouyuxuanyx/blockgcn","path":"graph/tools.py","file_url":"https://github.com/zhouyuxuanyx/blockgcn/blob/HEAD/graph/tools.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5f5ae82020ac7ec2","mcp_get_code":{"code_sha256":"5f5ae82020ac7ec2"}},{"arxiv_id":"2302.01825","paper":"/paper/hdformer-high-order-directed-transformer-for","title":"HDFormer: High-order Directed Transformer for 3D Human Pose Estimation","date":"2023-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hyer/HDFormer","path":"models/hd_former.py","file_url":"https://github.com/hyer/HDFormer/blob/HEAD/models/hd_former.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":"5f5ae82020ac7ec2","mcp_get_code":{"code_sha256":"5f5ae82020ac7ec2"}},{"arxiv_id":"2211.09590","paper":"/paper/hypergraph-transformer-for-skeleton-based","title":"Hypergraph Transformer for Skeleton-based Action Recognition","date":"2022-11-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZhouYuxuanYX/Hypergraph-Transformer-for-Skeleton-based-Action-Recognition","path":"graph/tools.py","file_url":"https://github.com/ZhouYuxuanYX/Hypergraph-Transformer-for-Skeleton-based-Action-Recognition/blob/HEAD/graph/tools.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5f5ae82020ac7ec2","mcp_get_code":{"code_sha256":"5f5ae82020ac7ec2"}},{"arxiv_id":"2208.10741","paper":"/paper/hierarchically-decomposed-graph-convolutional","title":"Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action Recognition","date":"2022-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jho-Yonsei/HD-GCN","path":"graph/tools.py","file_url":"https://github.com/Jho-Yonsei/HD-GCN/blob/HEAD/graph/tools.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":"5f5ae82020ac7ec2","mcp_get_code":{"code_sha256":"5f5ae82020ac7ec2"}},{"arxiv_id":"03697","paper":null,"title":"arXiv:03697","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"jerry-wjh/UbH-GCN","path":"graph/tools.py","file_url":"https://github.com/jerry-wjh/UbH-GCN/blob/HEAD/graph/tools.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":"5f5ae82020ac7ec2","mcp_get_code":{"code_sha256":"5f5ae82020ac7ec2"}}]}