{"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/invert-dict","entry":"invert_dict","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":9,"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":8,"n_samples_ran":4,"n_samples_fingerprinted":0,"n_places":13,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":2,"unverified":4},"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":"2506.22427","paper":null,"title":"arXiv:2506.22427","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"Nokia-Bell-Labs/Loss-Vector-based-Clustered-Federated-Learning","path":"utils/data_utils.py","file_url":"https://github.com/Nokia-Bell-Labs/Loss-Vector-based-Clustered-Federated-Learning/blob/HEAD/utils/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause-Clear","inline_ok":false,"code_sha256_prefix":"c9f3b795d7b45aaf","mcp_get_code":{"code_sha256":"c9f3b795d7b45aaf"}},{"arxiv_id":"2411.06500","paper":"/paper/towards-graph-neural-network-surrogates","title":"Graph Neural Network Surrogates to leverage Mechanistic Expert Knowledge towards Reliable and Immediate Pandemic Response","date":"2024-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scicompmod/memilio","path":"pycode/memilio-epidata/memilio/epidata/defaultDict.py","file_url":"https://github.com/scicompmod/memilio/blob/HEAD/pycode/memilio-epidata/memilio/epidata/defaultDict.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":"fda9fd30ff484cae","mcp_get_code":{"code_sha256":"fda9fd30ff484cae"}},{"arxiv_id":"2405.06708","paper":"/paper/langcell-language-cell-pre-training-for-cell","title":"LangCell: Language-Cell Pre-training for Cell Identity Understanding","date":"2024-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PharMolix/LangCell","path":"geneformer_001/geneformer/in_silico_perturber_stats.py","file_url":"https://github.com/PharMolix/LangCell/blob/HEAD/geneformer_001/geneformer/in_silico_perturber_stats.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9283a2d8498ee85b","mcp_get_code":{"code_sha256":"9283a2d8498ee85b"}},{"arxiv_id":"2402.14744","paper":"/paper/large-language-models-as-urban-residents-an","title":"Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangjw6/llmob","path":"evaluate.py","file_url":"https://github.com/wangjw6/llmob/blob/HEAD/evaluate.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b79ee975818fce6c","mcp_get_code":{"code_sha256":"b79ee975818fce6c"}},{"arxiv_id":"2310.17914","paper":"/paper/3d-aware-visual-question-answering-about-1","title":"3D-Aware Visual Question Answering about Parts, Poses and Occlusions","date":"2023-10-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xingruiwang/3d-aware-vqa","path":"PO3D-VQA/attr_net/utils.py","file_url":"https://github.com/xingruiwang/3d-aware-vqa/blob/HEAD/PO3D-VQA/attr_net/utils.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":"e6dbe2d3b6f0cc87","mcp_get_code":{"code_sha256":"e6dbe2d3b6f0cc87"}},{"arxiv_id":"2307.16210","paper":"/paper/rethinking-uncertainly-missing-and-ambiguous","title":"Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment","date":"2023-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjukg/umaea","path":"UMAEA/torchlight/utils.py","file_url":"https://github.com/zjukg/umaea/blob/HEAD/UMAEA/torchlight/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6dbe2d3b6f0cc87","mcp_get_code":{"code_sha256":"e6dbe2d3b6f0cc87"}},{"arxiv_id":"2307.11772","paper":"/paper/autoalign-fully-automatic-and-effective","title":"AutoAlign: Fully Automatic and Effective Knowledge Graph Alignment enabled by Large Language Models","date":"2023-07-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ruizhang-ai/autoalign","path":"code/AutoAlign.py","file_url":"https://github.com/ruizhang-ai/autoalign/blob/HEAD/code/AutoAlign.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":"d5fd1ea81b01c47e","mcp_get_code":{"code_sha256":"d5fd1ea81b01c47e"}},{"arxiv_id":"2305.02519","paper":"/paper/anetqa-a-large-scale-benchmark-for-fine","title":"ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos","date":"2023-05-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MILVLG/anetqa-code","path":"hcrn/DataLoader.py","file_url":"https://github.com/MILVLG/anetqa-code/blob/HEAD/hcrn/DataLoader.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":"e6dbe2d3b6f0cc87","mcp_get_code":{"code_sha256":"e6dbe2d3b6f0cc87"}},{"arxiv_id":"2212.14454","paper":"/paper/meaformer-multi-modal-entity-alignment","title":"MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid","date":"2022-12-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zjukg/MEAformer","path":"torchlight/utils.py","file_url":"https://github.com/zjukg/MEAformer/blob/HEAD/torchlight/utils.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6dbe2d3b6f0cc87","mcp_get_code":{"code_sha256":"e6dbe2d3b6f0cc87"}},{"arxiv_id":"2211.03779","paper":"/paper/cripp-vqa-counterfactual-reasoning-about","title":"CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering","date":"2022-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thaolmk54/hcrn-videoqa","path":"DataLoader.py","file_url":"https://github.com/thaolmk54/hcrn-videoqa/blob/HEAD/DataLoader.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":"e6dbe2d3b6f0cc87","mcp_get_code":{"code_sha256":"e6dbe2d3b6f0cc87"}},{"arxiv_id":"1812.01855","paper":"/paper/explainable-and-explicit-visual-reasoning","title":"Explainable and Explicit Visual Reasoning over Scene Graphs","date":"2018-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shijx12/XNM-Net","path":"utils/misc.py","file_url":"https://github.com/shijx12/XNM-Net/blob/HEAD/utils/misc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e6dbe2d3b6f0cc87","mcp_get_code":{"code_sha256":"e6dbe2d3b6f0cc87"}},{"arxiv_id":"1803.05268","paper":"/paper/transparency-by-design-closing-the-gap","title":"Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning","date":"2018-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davidmascharka/tbd-nets","path":"utils/clevr.py","file_url":"https://github.com/davidmascharka/tbd-nets/blob/HEAD/utils/clevr.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8fb8b8481eaa4c9e","mcp_get_code":{"code_sha256":"8fb8b8481eaa4c9e"}},{"arxiv_id":"1710.07300","paper":"/paper/figureqa-an-annotated-figure-dataset-for","title":"FigureQA: An Annotated Figure Dataset for Visual Reasoning","date":"2017-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vmichals/FigureQA-baseline","path":"util/text_tools.py","file_url":"https://github.com/vmichals/FigureQA-baseline/blob/HEAD/util/text_tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"089fe07a4abbde8f","mcp_get_code":{"code_sha256":"089fe07a4abbde8f"}}]}