{"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/pairwise","entry":"pairwise","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":27,"n_papers_ran":18,"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":14,"n_samples_ran":10,"n_samples_fingerprinted":0,"n_places":27,"n_places_pointer_only":9,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":4,"ran_fixture":1,"ran":5,"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":"2606.00253","paper":"/paper/arxiv-2606-00253","title":"Per-Group Error, Not Total MSE: Fine-Tuning Vision-Language-Action Models for 11-DoF Mobile Manipulation","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"paumontagut/per-group-mse-vla","path":"figures/robot_trials.py","file_url":"https://github.com/paumontagut/per-group-mse-vla/blob/HEAD/figures/robot_trials.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ceef46d001a7c64e","mcp_get_code":{"code_sha256":"ceef46d001a7c64e"}},{"arxiv_id":"2605.03723","paper":"/paper/arxiv-2605-03723","title":"Segmenting Human-LLM Co-authored Text via Change Point Detection","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"Mamba413/DetectLLMSegmentation","path":"ruptures/src/ruptures/detection/binseg.py","file_url":"https://github.com/Mamba413/DetectLLMSegmentation/blob/HEAD/ruptures/src/ruptures/detection/binseg.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e321c308839f1028","mcp_get_code":{"code_sha256":"e321c308839f1028"}},{"arxiv_id":"2504.12463","paper":"/paper/dense-backpropagation-improves-training-for","title":"Dense Backpropagation Improves Training for Sparse Mixture-of-Experts","date":"2025-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vatsal0/default-moe","path":"configs/gen_docs.py","file_url":"https://github.com/vatsal0/default-moe/blob/HEAD/configs/gen_docs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"50c9c165cf801f59","mcp_get_code":{"code_sha256":"50c9c165cf801f59"}},{"arxiv_id":"2501.12012","paper":"/paper/tabularargn-a-flexible-and-efficient-auto-1","title":"TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data","date":"2025-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mostly-ai/mostlyai","path":"mostlyai/sdk/_data/base.py","file_url":"https://github.com/mostly-ai/mostlyai/blob/HEAD/mostlyai/sdk/_data/base.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":"363d20718966e6b6","mcp_get_code":{"code_sha256":"363d20718966e6b6"}},{"arxiv_id":"2411.12992","paper":"/paper/memoryformer-minimize-transformer-computation","title":"MemoryFormer: Minimize Transformer Computation by Removing Fully-Connected Layers","date":"2024-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ningding-o/MemoryFormer","path":"configs/gen_docs.py","file_url":"https://github.com/ningding-o/MemoryFormer/blob/HEAD/configs/gen_docs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"50c9c165cf801f59","mcp_get_code":{"code_sha256":"50c9c165cf801f59"}},{"arxiv_id":"2410.04798","paper":"/paper/dape-v2-process-attention-score-as-feature","title":"DAPE V2: Process Attention Score as Feature Map for Length Extrapolation","date":"2024-10-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chuanyang-zheng/cape","path":"configs/gen_docs.py","file_url":"https://github.com/chuanyang-zheng/cape/blob/HEAD/configs/gen_docs.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":"50c9c165cf801f59","mcp_get_code":{"code_sha256":"50c9c165cf801f59"}},{"arxiv_id":"2406.12428","paper":"/paper/pslm-parallel-generation-of-text-and-speech","title":"PSLM: Parallel Generation of Text and Speech with LLMs for Low-Latency Spoken Dialogue Systems","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eleutherai/gpt-neox","path":"configs/gen_docs.py","file_url":"https://github.com/eleutherai/gpt-neox/blob/HEAD/configs/gen_docs.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":"50c9c165cf801f59","mcp_get_code":{"code_sha256":"50c9c165cf801f59"}},{"arxiv_id":"2403.12033","paper":"/paper/hiker-sgg-hierarchical-knowledge-enhanced","title":"HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation","date":"2024-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangce01/HiKER-SGG","path":"lib/pytorch_misc.py","file_url":"https://github.com/zhangce01/HiKER-SGG/blob/HEAD/lib/pytorch_misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fef49730c9e9a2b8","mcp_get_code":{"code_sha256":"fef49730c9e9a2b8"}},{"arxiv_id":"2312.09997","paper":"/paper/one-self-configurable-model-to-solve-many","title":"One Self-Configurable Model to Solve Many Abstract Visual Reasoning Problems","date":"2023-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mikomel/sal","path":"avr/config.py","file_url":"https://github.com/mikomel/sal/blob/HEAD/avr/config.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5a016f69a93b885a","mcp_get_code":{"code_sha256":"5a016f69a93b885a"}},{"arxiv_id":"2312.02406","paper":"/paper/efficient-online-data-mixing-for-language","title":"Efficient Online Data Mixing For Language Model Pre-Training","date":"2023-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alon-albalak/online-data-mixing","path":"configs/gen_docs.py","file_url":"https://github.com/alon-albalak/online-data-mixing/blob/HEAD/configs/gen_docs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"50c9c165cf801f59","mcp_get_code":{"code_sha256":"50c9c165cf801f59"}},{"arxiv_id":"2311.16098","paper":"/paper/on-bringing-robots-home","title":"On Bringing Robots Home","date":"2023-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"notmahi/dobb-e","path":"stick-data-collection/export_vids_ffmpeg.py","file_url":"https://github.com/notmahi/dobb-e/blob/HEAD/stick-data-collection/export_vids_ffmpeg.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"21bacb95de249600","mcp_get_code":{"code_sha256":"21bacb95de249600"}},{"arxiv_id":"2310.20159","paper":"/paper/language-guided-visual-question-answering","title":"Language Guided Visual Question Answering: Elevate Your Multimodal Language Model Using Knowledge-Enriched Prompts","date":"2023-10-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"declare-lab/lg-vqa","path":"guidance/RelTR/lib/pytorch_misc.py","file_url":"https://github.com/declare-lab/lg-vqa/blob/HEAD/guidance/RelTR/lib/pytorch_misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fef49730c9e9a2b8","mcp_get_code":{"code_sha256":"fef49730c9e9a2b8"}},{"arxiv_id":"2307.06422","paper":null,"title":"arXiv:2307.06422","date":null,"month_inferred_from_arxiv_id":"2023-07","title_source":null,"repo":"thupchnsky/dp-gnn","path":"core/utils.py","file_url":"https://github.com/thupchnsky/dp-gnn/blob/HEAD/core/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e2521bda2312168c","mcp_get_code":{"code_sha256":"e2521bda2312168c"}},{"arxiv_id":"2305.11475","paper":"/paper/curve-your-enthusiasm-concurvity","title":"Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models","date":"2023-05-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"merantix-momentum/concurvity-regularization","path":"main/concurvity.py","file_url":"https://github.com/merantix-momentum/concurvity-regularization/blob/HEAD/main/concurvity.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"98a4acbd35bc6550","mcp_get_code":{"code_sha256":"98a4acbd35bc6550"}},{"arxiv_id":"2207.12598","paper":"/paper/classifier-free-diffusion-guidance","title":"Classifier-Free Diffusion Guidance","date":"2022-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kjsman/stable-diffusion-pytorch","path":"stable_diffusion_pytorch/pipeline.py","file_url":"https://github.com/kjsman/stable-diffusion-pytorch/blob/HEAD/stable_diffusion_pytorch/pipeline.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df31b88dfd9608ff","mcp_get_code":{"code_sha256":"df31b88dfd9608ff"}},{"arxiv_id":"2205.09921","paper":"/paper/kerple-kernelized-relative-positional","title":"KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation","date":"2022-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chijames/KERPLE","path":"configs/gen_docs.py","file_url":"https://github.com/chijames/KERPLE/blob/HEAD/configs/gen_docs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"50c9c165cf801f59","mcp_get_code":{"code_sha256":"50c9c165cf801f59"}},{"arxiv_id":"2203.00949","paper":"/paper/gap-differentially-private-graph-neural","title":"GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation","date":"2022-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sisaman/gap","path":"core/utils.py","file_url":"https://github.com/sisaman/gap/blob/HEAD/core/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"code_sha256_prefix":"e2521bda2312168c","mcp_get_code":{"code_sha256":"e2521bda2312168c"}},{"arxiv_id":"2107.12309","paper":"/paper/spatial-temporal-transformer-for-dynamic","title":"Spatial-Temporal Transformer for Dynamic Scene Graph Generation","date":"2021-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yrcong/STTran","path":"lib/pytorch_misc.py","file_url":"https://github.com/yrcong/STTran/blob/HEAD/lib/pytorch_misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fef49730c9e9a2b8","mcp_get_code":{"code_sha256":"fef49730c9e9a2b8"}},{"arxiv_id":"2105.00855","paper":"/paper/computationally-efficient-optimization-of-1","title":"Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness","date":"2021-05-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HarrieO/2021-SIGIR-plackett-luce","path":"algorithms/pairwise.py","file_url":"https://github.com/HarrieO/2021-SIGIR-plackett-luce/blob/HEAD/algorithms/pairwise.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6d78cc79dd180c09","mcp_get_code":{"code_sha256":"6d78cc79dd180c09"}},{"arxiv_id":"2006.09623","paper":"/paper/learning-visual-commonsense-for-robust-scene","title":"Learning Visual Commonsense for Robust Scene Graph Generation","date":"2020-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZhecanJamesWang/GLAT_SGG","path":"lib/pytorch_misc.py","file_url":"https://github.com/ZhecanJamesWang/GLAT_SGG/blob/HEAD/lib/pytorch_misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fef49730c9e9a2b8","mcp_get_code":{"code_sha256":"fef49730c9e9a2b8"}},{"arxiv_id":"2003.12962","paper":"/paper/gps-net-graph-property-sensing-network-for","title":"GPS-Net: Graph Property Sensing Network for Scene Graph Generation","date":"2020-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"taksau/GPS-Net","path":"lib/pytorch_misc.py","file_url":"https://github.com/taksau/GPS-Net/blob/HEAD/lib/pytorch_misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fef49730c9e9a2b8","mcp_get_code":{"code_sha256":"fef49730c9e9a2b8"}},{"arxiv_id":"1905.10497","paper":"/paper/190510497","title":"Fair Resource Allocation in Federated Learning","date":"2019-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mukul-rathi/personalised-federated-learning","path":"client/perfedavg_client.py","file_url":"https://github.com/mukul-rathi/personalised-federated-learning/blob/HEAD/client/perfedavg_client.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"59f09562e5a4b998","mcp_get_code":{"code_sha256":"59f09562e5a4b998"}},{"arxiv_id":"1905.06401","paper":"/paper/correlating-neural-and-symbolic","title":"Correlating neural and symbolic representations of language","date":"2019-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gchrupala/ursa","path":"ursa/util.py","file_url":"https://github.com/gchrupala/ursa/blob/HEAD/ursa/util.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":"c76aee70b3246853","mcp_get_code":{"code_sha256":"c76aee70b3246853"}},{"arxiv_id":"1903.03326","paper":"/paper/knowledge-embedded-routing-network-for-scene","title":"Knowledge-Embedded Routing Network for Scene Graph Generation","date":"2019-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yuweihao/KERN","path":"lib/pytorch_misc.py","file_url":"https://github.com/yuweihao/KERN/blob/HEAD/lib/pytorch_misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fef49730c9e9a2b8","mcp_get_code":{"code_sha256":"fef49730c9e9a2b8"}},{"arxiv_id":"1812.01880","paper":"/paper/learning-to-compose-dynamic-tree-structures","title":"Learning to Compose Dynamic Tree Structures for Visual Contexts","date":"2018-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KaihuaTang/VCTree-Scene-Graph-Generation","path":"lib/pytorch_misc.py","file_url":"https://github.com/KaihuaTang/VCTree-Scene-Graph-Generation/blob/HEAD/lib/pytorch_misc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fef49730c9e9a2b8","mcp_get_code":{"code_sha256":"fef49730c9e9a2b8"}},{"arxiv_id":"1806.07366","paper":"/paper/neural-ordinary-differential-equations","title":"Neural Ordinary Differential Equations","date":"2018-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UofTrees/ProjectX2020","path":"mr_node/model/odefunc.py","file_url":"https://github.com/UofTrees/ProjectX2020/blob/HEAD/mr_node/model/odefunc.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"acc109795f939462","mcp_get_code":{"code_sha256":"acc109795f939462"}},{"arxiv_id":"2023.findings-acl.807","paper":null,"title":"arXiv:2023.findings-acl.807","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"amueller/word_cloud","path":"wordcloud/tokenization.py","file_url":"https://github.com/amueller/word_cloud/blob/HEAD/wordcloud/tokenization.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"21bacb95de249600","mcp_get_code":{"code_sha256":"21bacb95de249600"}}]}