{"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/position-encoding","entry":"position_encoding","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":13,"n_papers_ran":12,"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":7,"n_samples_ran":6,"n_samples_fingerprinted":4,"n_places":13,"n_places_pointer_only":4,"by_status":{"ran_honours":3,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":2,"unverified":1},"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":"2607.22136","paper":"/paper/arxiv-2607-22136","title":"Dynamic Commonsense Coordination for Empathetic Response Generation","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"Sahandfer/CEM","path":"src/models/common.py","file_url":"https://github.com/Sahandfer/CEM/blob/HEAD/src/models/common.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e6dc8a206c0c47a8","mcp_get_code":{"code_sha256":"e6dc8a206c0c47a8"}},{"arxiv_id":"2605.27063","paper":"/paper/arxiv-2605-27063","title":"Learning Dynamic Graph Representations through Timespan View Contrasts","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"yimingxu24/CLDG","path":"CLDG/utils.py","file_url":"https://github.com/yimingxu24/CLDG/blob/HEAD/CLDG/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"44a7d49a832f00fd","mcp_get_code":{"code_sha256":"44a7d49a832f00fd"}},{"arxiv_id":"2406.00429","paper":"/paper/towards-generalizable-multi-object-tracking","title":"Towards Generalizable Multi-Object Tracking","date":"2024-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qinzheng2000/generaltrack","path":"core/Point2InstanceRelation.py","file_url":"https://github.com/qinzheng2000/generaltrack/blob/HEAD/core/Point2InstanceRelation.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f5edaf65bc73e518","mcp_get_code":{"code_sha256":"f5edaf65bc73e518"}},{"arxiv_id":"2203.15190","paper":"/paper/3d-shape-reconstruction-from-2d-images-with","title":"3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junshengzhou/3DAttriFlow","path":"utils/model_utils.py","file_url":"https://github.com/junshengzhou/3DAttriFlow/blob/HEAD/utils/model_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1240ac70ff56f0c6","mcp_get_code":{"code_sha256":"1240ac70ff56f0c6"}},{"arxiv_id":"2112.09174","paper":"/paper/learning-bounded-context-free-grammar-via","title":"Learning Bounded Context-Free-Grammar via LSTM and the Transformer:Difference and Explanations","date":"2021-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shihui2010/learn_cfg_with_neural_network","path":"models/transformer.py","file_url":"https://github.com/shihui2010/learn_cfg_with_neural_network/blob/HEAD/models/transformer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6dc8a206c0c47a8","mcp_get_code":{"code_sha256":"e6dc8a206c0c47a8"}},{"arxiv_id":"2010.01454","paper":"/paper/mime-mimicking-emotions-for-empathetic","title":"MIME: MIMicking Emotions for Empathetic Response Generation","date":"2020-10-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"declare-lab/MIME","path":"model/common_layer.py","file_url":"https://github.com/declare-lab/MIME/blob/HEAD/model/common_layer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6dc8a206c0c47a8","mcp_get_code":{"code_sha256":"e6dc8a206c0c47a8"}},{"arxiv_id":"2003.12738","paper":"/paper/variational-transformers-for-diverse-response","title":"Variational Transformers for Diverse Response Generation","date":"2020-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zlinao/Variational-Transformer","path":"model/common_layer.py","file_url":"https://github.com/zlinao/Variational-Transformer/blob/HEAD/model/common_layer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6dc8a206c0c47a8","mcp_get_code":{"code_sha256":"e6dc8a206c0c47a8"}},{"arxiv_id":"1908.07687","paper":"/paper/190807687","title":"MoEL: Mixture of Empathetic Listeners","date":"2019-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HLTCHKUST/MoEL","path":"model/common_layer.py","file_url":"https://github.com/HLTCHKUST/MoEL/blob/HEAD/model/common_layer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6dc8a206c0c47a8","mcp_get_code":{"code_sha256":"e6dc8a206c0c47a8"}},{"arxiv_id":"1905.10033","paper":"/paper/personalizing-dialogue-agents-via-meta","title":"Personalizing Dialogue Agents via Meta-Learning","date":"2019-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HLTCHKUST/PAML","path":"model/common_layer.py","file_url":"https://github.com/HLTCHKUST/PAML/blob/HEAD/model/common_layer.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6dc8a206c0c47a8","mcp_get_code":{"code_sha256":"e6dc8a206c0c47a8"}},{"arxiv_id":"1610.04211","paper":"/paper/gated-end-to-end-memory-networks","title":"Gated End-to-End Memory Networks","date":"2016-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cstghitpku/GateMemN2N","path":"gmemn2n/gmemn2n.py","file_url":"https://github.com/cstghitpku/GateMemN2N/blob/HEAD/gmemn2n/gmemn2n.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8abcd06d5ce60851","mcp_get_code":{"code_sha256":"8abcd06d5ce60851"}},{"arxiv_id":"1603.01417","paper":"/paper/dynamic-memory-networks-for-visual-and","title":"Dynamic Memory Networks for Visual and Textual Question Answering","date":"2016-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dandelin/Dynamic-memory-networks-plus-Pytorch","path":"babi_main.py","file_url":"https://github.com/dandelin/Dynamic-memory-networks-plus-Pytorch/blob/HEAD/babi_main.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9f51aa3fcdd89fcd","mcp_get_code":{"code_sha256":"9f51aa3fcdd89fcd"}},{"arxiv_id":"1503.08895","paper":"/paper/end-to-end-memory-networks","title":"End-To-End Memory Networks","date":"2015-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ishalyminov/memn2n","path":"memn2n/memn2n.py","file_url":"https://github.com/ishalyminov/memn2n/blob/HEAD/memn2n/memn2n.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0ca12d8cc81c8be","mcp_get_code":{"code_sha256":"b0ca12d8cc81c8be"}},{"arxiv_id":"1502.05698","paper":"/paper/towards-ai-complete-question-answering-a-set","title":"Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks","date":"2015-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"domluna/memn2n","path":"memn2n/memn2n.py","file_url":"https://github.com/domluna/memn2n/blob/HEAD/memn2n/memn2n.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8abcd06d5ce60851","mcp_get_code":{"code_sha256":"8abcd06d5ce60851"}}]}