{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/computational-efficiency/papers/3","list_of":"/task/computational-efficiency","task":"Computational Efficiency","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":49,"rows_per_page":100,"rows":[201,300],"of":4891,"counts":{"archive_papers_tagged":4891,"with_a_code_link":1644,"where_syntology_ran_a_sample":369,"not_listed_spam_title":0,"listed":4891,"listed_where_code_ran":369,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":307,"every_run_a_failure_of_syntologys_instrument":62,"listed_with_a_run_with_no_instrument_failure":307,"listed_every_run_a_failure_of_syntologys_instrument":62,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/computational-efficiency","prev":"/task/computational-efficiency/papers/2","next":"/task/computational-efficiency/papers/4","papers":[{"url":"/paper/neural-multisensory-scene-inference","slug":"neural-multisensory-scene-inference","title":"Neural Multisensory Scene Inference","date":"2019-10-06","arxiv_id":"1910.02344","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/neural-multisensory-scene-inference#ran","syntology_url":"https://syntology.ai/paper/1910.02344","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1910.02344"}},"official":{"repos":["lim0606/pytorch-generative-multisensory-network"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/covariance-free-partial-least-squares-an","slug":"covariance-free-partial-least-squares-an","title":"Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method","date":"2019-10-05","arxiv_id":"1910.02319","repositories_listed":2,"syntology":null},{"url":"/paper/place-recognition-for-stereo-visualodometry","slug":"place-recognition-for-stereo-visualodometry","title":"A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors","date":"2019-09-16","arxiv_id":"1909.07267","repositories_listed":2,"syntology":null},{"url":"/paper/improved-techniques-for-training-adaptive","slug":"improved-techniques-for-training-adaptive","title":"Improved Techniques for Training Adaptive Deep Networks","date":"2019-08-17","arxiv_id":"1908.06294","repositories_listed":2,"syntology":null},{"url":"/paper/least-squares-approximation-for-a-distributed","slug":"least-squares-approximation-for-a-distributed","title":"Least Squares Approximation for a Distributed System","date":"2019-08-14","arxiv_id":"1908.04904","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/least-squares-approximation-for-a-distributed#ran","syntology_url":"https://syntology.ai/paper/1908.04904","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.04904"}},"official":{"repos":["feng-li/dlsa"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/190513536","slug":"190513536","title":"Scaling Video Analytics on Constrained Edge Nodes","date":"2019-05-24","arxiv_id":"1905.13536","repositories_listed":2,"syntology":null},{"url":"/paper/semantics-guided-neural-networks-for","slug":"semantics-guided-neural-networks-for","title":"Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition","date":"2019-04-02","arxiv_id":"1904.01189","repositories_listed":2,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/semantics-guided-neural-networks-for#ran","syntology_url":"https://syntology.ai/paper/1904.01189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.01189"}},"official":{"repos":["microsoft/SGN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/macow-masked-convolutional-generative-flow","slug":"macow-masked-convolutional-generative-flow","title":"MaCow: Masked Convolutional Generative Flow","date":"2019-02-12","arxiv_id":"1902.04208","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/macow-masked-convolutional-generative-flow#ran","syntology_url":"https://syntology.ai/paper/1902.04208","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.04208"}},"official":{"repos":["XuezheMax/macow"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-kernel-prediction-networks-for","slug":"multi-kernel-prediction-networks-for","title":"Multi-Kernel Prediction Networks for Denoising of Burst Images","date":"2019-02-05","arxiv_id":"1902.05392","repositories_listed":2,"syntology":null},{"url":"/paper/surrogate-assisted-bayesian-inversion-for","slug":"surrogate-assisted-bayesian-inversion-for","title":"Surrogate-assisted Bayesian inversion for landscape and basin evolution models","date":"2018-12-12","arxiv_id":"1812.08655","repositories_listed":2,"syntology":null},{"url":"/paper/iterative-projection-and-matching-finding","slug":"iterative-projection-and-matching-finding","title":"Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision","date":"2018-11-29","arxiv_id":"1811.12326","repositories_listed":2,"syntology":null},{"url":"/paper/a-simple-yet-effective-baseline-for-non","slug":"a-simple-yet-effective-baseline-for-non","title":"A simple yet effective baseline for non-attributed graph classification","date":"2018-11-08","arxiv_id":"1811.03508","repositories_listed":2,"syntology":null},{"url":"/paper/geoseg-a-computer-vision-package-for","slug":"geoseg-a-computer-vision-package-for","title":"Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline Extraction","date":"2018-09-10","arxiv_id":"1809.03175","repositories_listed":2,"syntology":null},{"url":"/paper/attention-gated-networks-learning-to-leverage","slug":"attention-gated-networks-learning-to-leverage","title":"Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images","date":"2018-08-22","arxiv_id":"1808.08114","repositories_listed":2,"syntology":null},{"url":"/paper/adaptive-skip-intervals-temporal-abstraction","slug":"adaptive-skip-intervals-temporal-abstraction","title":"Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models","date":"2018-08-14","arxiv_id":"1808.04768","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adaptive-skip-intervals-temporal-abstraction#ran","syntology_url":"https://syntology.ai/paper/1808.04768","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1808.04768"}},"official":{"repos":["neitzal/adaptive-skip-intervals"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-convolutional-recurrent-autoencoders-for","slug":"deep-convolutional-recurrent-autoencoders-for","title":"Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems","date":"2018-08-03","arxiv_id":"1808.01346","repositories_listed":2,"syntology":null},{"url":"/paper/gated-fusion-network-for-joint-image","slug":"gated-fusion-network-for-joint-image","title":"Gated Fusion Network for Joint Image Deblurring and Super-Resolution","date":"2018-07-27","arxiv_id":"1807.10806","repositories_listed":2,"syntology":null},{"url":"/paper/a-fast-and-scalable-joint-estimator-for-1","slug":"a-fast-and-scalable-joint-estimator-for-1","title":"A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models","date":"2018-06-01","arxiv_id":"1806.00548","repositories_listed":2,"syntology":null},{"url":"/paper/estimating-time-varying-graphical-models","slug":"estimating-time-varying-graphical-models","title":"Estimating Time-Varying Graphical Models","date":"2018-04-11","arxiv_id":"1804.03811","repositories_listed":2,"syntology":null},{"url":"/paper/fast-sequence-based-embedding-with-diffusion","slug":"fast-sequence-based-embedding-with-diffusion","title":"Fast Sequence Based Embedding with Diffusion Graphs","date":"2018-03-20","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/geometric-dimensionality-reduction-for","slug":"geometric-dimensionality-reduction-for","title":"Supervised Dimensionality Reduction for Big Data","date":"2017-09-05","arxiv_id":"1709.01233","repositories_listed":2,"syntology":null},{"url":"/paper/multilingual-hierarchical-attention-networks","slug":"multilingual-hierarchical-attention-networks","title":"Multilingual Hierarchical Attention Networks for Document Classification","date":"2017-07-04","arxiv_id":"1707.00896","repositories_listed":2,"syntology":null},{"url":"/paper/reconstruction-of-three-dimensional-porous","slug":"reconstruction-of-three-dimensional-porous","title":"Reconstruction of three-dimensional porous media using generative adversarial neural networks","date":"2017-04-11","arxiv_id":"1704.03225","repositories_listed":2,"syntology":null},{"url":"/paper/a-fast-and-scalable-joint-estimator-for","slug":"a-fast-and-scalable-joint-estimator-for","title":"A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models","date":"2017-02-09","arxiv_id":"1702.02715","repositories_listed":2,"syntology":null},{"url":"/paper/improving-variational-auto-encoders-using-1","slug":"improving-variational-auto-encoders-using-1","title":"Improving Variational Auto-Encoders using Householder Flow","date":"2016-11-29","arxiv_id":"1611.09630","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-variational-auto-encoders-using-1#ran","syntology_url":"https://syntology.ai/paper/1611.09630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1611.09630"}},"official":null}},{"url":"/paper/graph-learning-from-data-under-structural-and","slug":"graph-learning-from-data-under-structural-and","title":"Graph Learning from Data under Structural and Laplacian Constraints","date":"2016-11-16","arxiv_id":"1611.05181","repositories_listed":2,"syntology":null},{"url":"/paper/the-deterministic-information-bottleneck","slug":"the-deterministic-information-bottleneck","title":"The deterministic information bottleneck","date":"2016-04-01","arxiv_id":"1604.00268","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-deterministic-information-bottleneck#ran","syntology_url":"https://syntology.ai/paper/1604.00268","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1604.00268"}},"official":null}},{"url":"/paper/fast-discrete-distribution-clustering-using","slug":"fast-discrete-distribution-clustering-using","title":"Fast Discrete Distribution Clustering Using Wasserstein Barycenter with Sparse Support","date":"2015-09-30","arxiv_id":"1510.00012","repositories_listed":2,"syntology":null},{"url":"/paper/places205-vggnet-models-for-scene-recognition","slug":"places205-vggnet-models-for-scene-recognition","title":"Places205-VGGNet Models for Scene Recognition","date":"2015-08-07","arxiv_id":"1508.01667","repositories_listed":2,"syntology":null},{"url":"/paper/random-projection-forests","slug":"random-projection-forests","title":"Sparse Projection Oblique Randomer Forests","date":"2015-06-10","arxiv_id":"1506.03410","repositories_listed":2,"syntology":null},{"url":"/paper/matchnet-unifying-feature-and-metric-learning","slug":"matchnet-unifying-feature-and-metric-learning","title":"MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching","date":"2015-06-01","arxiv_id":null,"repositories_listed":2,"syntology":null},{"url":"/paper/inner-and-inter-label-propagation-salient","slug":"inner-and-inter-label-propagation-salient","title":"Inner and Inter Label Propagation: Salient Object Detection in the Wild","date":"2015-05-27","arxiv_id":"1505.07192","repositories_listed":2,"syntology":null},{"url":"/paper/variational-reformulation-of-bayesian-inverse","slug":"variational-reformulation-of-bayesian-inverse","title":"Variational Reformulation of Bayesian Inverse Problems","date":"2014-10-21","arxiv_id":"1410.5522","repositories_listed":2,"syntology":null},{"url":"/paper/graph-degree-linkage-agglomerative-clustering","slug":"graph-degree-linkage-agglomerative-clustering","title":"Graph Degree Linkage: Agglomerative Clustering on a Directed Graph","date":"2012-08-25","arxiv_id":"1208.5092","repositories_listed":2,"syntology":null},{"url":"/paper/bayesian-inference-for-logistic-models-using","slug":"bayesian-inference-for-logistic-models-using","title":"Bayesian inference for logistic models using Polya-Gamma latent variables","date":"2012-05-02","arxiv_id":"1205.0310","repositories_listed":2,"syntology":null},{"url":"/paper/from-roots-to-rewards-dynamic-tree-reasoning","slug":"from-roots-to-rewards-dynamic-tree-reasoning","title":"From Roots to Rewards: Dynamic Tree Reasoning with RL","date":"2025-07-17","arxiv_id":"2507.13142","repositories_listed":1,"syntology":null},{"url":"/paper/fourcastnet-3-a-geometric-approach-to","slug":"fourcastnet-3-a-geometric-approach-to","title":"FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale","date":"2025-07-16","arxiv_id":"2507.12144","repositories_listed":1,"syntology":null},{"url":"/paper/dcr-quantifying-data-contamination-in-llms","slug":"dcr-quantifying-data-contamination-in-llms","title":"DCR: Quantifying Data Contamination in LLMs Evaluation","date":"2025-07-15","arxiv_id":"2507.11405","repositories_listed":1,"syntology":null},{"url":"/paper/u-rwkv-lightweight-medical-image-segmentation","slug":"u-rwkv-lightweight-medical-image-segmentation","title":"U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV","date":"2025-07-15","arxiv_id":"2507.11415","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":1,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/u-rwkv-lightweight-medical-image-segmentation#ran","syntology_url":"https://syntology.ai/paper/2507.11415","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.11415"}},"official":{"repos":["hbyecoding/u-rwkv"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/i-2-world-intra-inter-tokenization-for","slug":"i-2-world-intra-inter-tokenization-for","title":"$I^{2}$-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene Forecasting","date":"2025-07-12","arxiv_id":"2507.09144","repositories_listed":1,"syntology":null},{"url":"/paper/geo-orbit-a-federated-digital-twin-framework","slug":"geo-orbit-a-federated-digital-twin-framework","title":"Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection","date":"2025-07-11","arxiv_id":"2507.08743","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-on-prompt-tuning","slug":"a-survey-on-prompt-tuning","title":"A Survey on Prompt Tuning","date":"2025-07-08","arxiv_id":"2507.06085","repositories_listed":1,"syntology":null},{"url":"/paper/decomposing-the-time-series-forecasting","slug":"decomposing-the-time-series-forecasting","title":"Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection","date":"2025-07-08","arxiv_id":"2507.05891","repositories_listed":1,"syntology":null},{"url":"/paper/mvnet-hyperspectral-remote-sensing-image","slug":"mvnet-hyperspectral-remote-sensing-image","title":"MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone Architecture","date":"2025-07-06","arxiv_id":"2507.04409","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-adaptive-transfer-network","slug":"generalized-adaptive-transfer-network","title":"Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains","date":"2025-07-02","arxiv_id":"2507.03026","repositories_listed":1,"syntology":null},{"url":"/paper/instant-particle-size-distribution","slug":"instant-particle-size-distribution","title":"Instant Particle Size Distribution Measurement Using CNNs Trained on Synthetic Data","date":"2025-07-01","arxiv_id":"2507.00822","repositories_listed":1,"syntology":null},{"url":"/paper/fadrm-fast-and-accurate-data-residual","slug":"fadrm-fast-and-accurate-data-residual","title":"FADRM: Fast and Accurate Data Residual Matching for Dataset Distillation","date":"2025-06-30","arxiv_id":"2506.24125","repositories_listed":1,"syntology":{"n":18,"n_ran":17,"n_constructed":0,"n_ran_checked":15,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":14,"n_pointer_only":3,"phrase":"17 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 1 violated, 14 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/fadrm-fast-and-accurate-data-residual#ran","syntology_url":"https://syntology.ai/paper/2506.24125","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.24125"}},"official":{"repos":["jiacheng8/fadrm"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":0,"n_ran_no_instrument_failure":15,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/improve-underwater-object-detection-through","slug":"improve-underwater-object-detection-through","title":"Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation","date":"2025-06-30","arxiv_id":"2506.23505","repositories_listed":1,"syntology":null},{"url":"/paper/volumetricsmpl-a-neural-volumetric-body-model","slug":"volumetricsmpl-a-neural-volumetric-body-model","title":"VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions","date":"2025-06-29","arxiv_id":"2506.23236","repositories_listed":1,"syntology":null},{"url":"/paper/antibody-design-and-optimization-with-multi","slug":"antibody-design-and-optimization-with-multi","title":"Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding","date":"2025-06-26","arxiv_id":"2506.20957","repositories_listed":1,"syntology":null},{"url":"/paper/fast-ground-penetrating-radar-dual-parameter","slug":"fast-ground-penetrating-radar-dual-parameter","title":"Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch","date":"2025-06-25","arxiv_id":"2506.20513","repositories_listed":1,"syntology":null},{"url":"/paper/adaptive-action-duration-with-contextual","slug":"adaptive-action-duration-with-contextual","title":"Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic Environments","date":"2025-06-17","arxiv_id":"2507.00030","repositories_listed":1,"syntology":null},{"url":"/paper/expressive-score-based-priors-for","slug":"expressive-score-based-priors-for","title":"Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization","date":"2025-06-17","arxiv_id":"2506.14607","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/expressive-score-based-priors-for#ran","syntology_url":"https://syntology.ai/paper/2506.14607","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.14607"}},"official":{"repos":["inouye-lab/saub"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/shgcn-simplified-hyperbolic-graph","slug":"shgcn-simplified-hyperbolic-graph","title":"sHGCN: Simplified hyperbolic graph convolutional neural networks","date":"2025-06-17","arxiv_id":"2506.14438","repositories_listed":1,"syntology":null},{"url":"/paper/ebs-cfl-efficient-and-byzantine-robust-secure","slug":"ebs-cfl-efficient-and-byzantine-robust-secure","title":"EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning","date":"2025-06-16","arxiv_id":"2506.13612","repositories_listed":1,"syntology":null},{"url":"/paper/vectorized-sparse-second-order-forward","slug":"vectorized-sparse-second-order-forward","title":"Vectorized Sparse Second-Order Forward Automatic Differentiation for Optimal Control Direct Methods","date":"2025-06-13","arxiv_id":"2506.11537","repositories_listed":1,"syntology":null},{"url":"/paper/it-s-not-the-target-it-s-the-background","slug":"it-s-not-the-target-it-s-the-background","title":"It's Not the Target, It's the Background: Rethinking Infrared Small Target Detection via Deep Patch-Free Low-Rank Representations","date":"2025-06-12","arxiv_id":"2506.10425","repositories_listed":1,"syntology":null},{"url":"/paper/2506-10142","slug":"2506-10142","title":"Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective","date":"2025-06-11","arxiv_id":"2506.10142","repositories_listed":1,"syntology":null},{"url":"/paper/attention-please-revisiting-attentive-probing","slug":"attention-please-revisiting-attentive-probing","title":"Attention, Please! Revisiting Attentive Probing for Masked Image Modeling","date":"2025-06-11","arxiv_id":"2506.10178","repositories_listed":1,"syntology":null},{"url":"/paper/the-four-color-theorem-for-cell-instance","slug":"the-four-color-theorem-for-cell-instance","title":"The Four Color Theorem for Cell Instance Segmentation","date":"2025-06-11","arxiv_id":"2506.09724","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/the-four-color-theorem-for-cell-instance#ran","syntology_url":"https://syntology.ai/paper/2506.09724","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.09724"}},"official":{"repos":["zhangye-zoe/fcis"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/2506-08844","slug":"2506-08844","title":"IMAGIC-500: IMputation benchmark on A Generative Imaginary Country (500k samples)","date":"2025-06-10","arxiv_id":"2506.08844","repositories_listed":1,"syntology":null},{"url":"/paper/inceptionmamba-an-efficient-hybrid-network","slug":"inceptionmamba-an-efficient-hybrid-network","title":"InceptionMamba: An Efficient Hybrid Network with Large Band Convolution and Bottleneck Mamba","date":"2025-06-10","arxiv_id":"2506.08735","repositories_listed":1,"syntology":null},{"url":"/paper/karma-a-multilevel-decomposition-hybrid-mamba","slug":"karma-a-multilevel-decomposition-hybrid-mamba","title":"KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting","date":"2025-06-10","arxiv_id":"2506.08939","repositories_listed":1,"syntology":null},{"url":"/paper/offline-rl-with-smooth-ood-generalization-in","slug":"offline-rl-with-smooth-ood-generalization-in","title":"Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood","date":"2025-06-10","arxiv_id":"2506.08417","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/offline-rl-with-smooth-ood-generalization-in#ran","syntology_url":"https://syntology.ai/paper/2506.08417","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.08417"}},"official":{"repos":["yqpqry/sqog"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rulereasoner-reinforced-rule-based-reasoning","slug":"rulereasoner-reinforced-rule-based-reasoning","title":"RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Sampling","date":"2025-06-10","arxiv_id":"2506.08672","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":6,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":4,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/rulereasoner-reinforced-rule-based-reasoning#ran","syntology_url":"https://syntology.ai/paper/2506.08672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.08672"}},"official":{"repos":["bigai-nlco/rulereasoner"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/streamsplat-towards-online-dynamic-3d","slug":"streamsplat-towards-online-dynamic-3d","title":"StreamSplat: Towards Online Dynamic 3D Reconstruction from Uncalibrated Video Streams","date":"2025-06-10","arxiv_id":"2506.08862","repositories_listed":1,"syntology":null},{"url":"/paper/spatio-temporal-state-space-model-for","slug":"spatio-temporal-state-space-model-for","title":"Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow","date":"2025-06-09","arxiv_id":"2506.07878","repositories_listed":1,"syntology":null},{"url":"/paper/2506-08048","slug":"2506-08048","title":"Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and Prompts","date":"2025-06-08","arxiv_id":"2506.08048","repositories_listed":1,"syntology":null},{"url":"/paper/dam-dynamic-attention-mask-for-long-context","slug":"dam-dynamic-attention-mask-for-long-context","title":"DAM: Dynamic Attention Mask for Long-Context Large Language Model Inference Acceleration","date":"2025-06-06","arxiv_id":"2506.11104","repositories_listed":1,"syntology":null},{"url":"/paper/sds-net-shallow-deep-synergism-detection","slug":"sds-net-shallow-deep-synergism-detection","title":"SDS-Net: Shallow-Deep Synergism-detection Network for infrared small target detection","date":"2025-06-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unlocking-chemical-insights-superior","slug":"unlocking-chemical-insights-superior","title":"Unlocking Chemical Insights: Superior Molecular Representations from Intermediate Encoder Layers","date":"2025-06-06","arxiv_id":"2506.06443","repositories_listed":1,"syntology":null},{"url":"/paper/deepoly-a-high-order-accuracy-scientific","slug":"deepoly-a-high-order-accuracy-scientific","title":"DeePoly: A High-Order Accuracy Scientific Machine Learning Framework for Function Approximation and Solving PDEs","date":"2025-06-05","arxiv_id":"2506.04613","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-robust-conformal-prediction-via","slug":"efficient-robust-conformal-prediction-via","title":"Efficient Robust Conformal Prediction via Lipschitz-Bounded Networks","date":"2025-06-05","arxiv_id":"2506.05434","repositories_listed":1,"syntology":null},{"url":"/paper/garg-aml-against-smurfing-a-scalable-and","slug":"garg-aml-against-smurfing-a-scalable-and","title":"GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering","date":"2025-06-04","arxiv_id":"2506.04292","repositories_listed":1,"syntology":null},{"url":"/paper/context-is-gold-to-find-the-gold-passage","slug":"context-is-gold-to-find-the-gold-passage","title":"Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings","date":"2025-05-30","arxiv_id":"2505.24782","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-raw-image-deblurring-with-adaptive","slug":"efficient-raw-image-deblurring-with-adaptive","title":"Efficient RAW Image Deblurring with Adaptive Frequency Modulation","date":"2025-05-30","arxiv_id":"2505.24407","repositories_listed":1,"syntology":null},{"url":"/paper/geo-sign-hyperbolic-contrastive-1","slug":"geo-sign-hyperbolic-contrastive-1","title":"Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation","date":"2025-05-30","arxiv_id":"2506.00129","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":3,"n_ran_checked":3,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"6 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/geo-sign-hyperbolic-contrastive-1#ran","syntology_url":"https://syntology.ai/paper/2506.00129","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.00129"}},"official":{"repos":["ed-fish/Geo-Sign"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hyperbolic-dataset-distillation","slug":"hyperbolic-dataset-distillation","title":"Hyperbolic Dataset Distillation","date":"2025-05-30","arxiv_id":"2505.24623","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hyperbolic-dataset-distillation#ran","syntology_url":"https://syntology.ai/paper/2505.24623","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.24623"}},"official":{"repos":["Guang000/Awesome-Dataset-Distillation"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/k-2-ie-kernel-method-based-kernel-intensity","slug":"k-2-ie-kernel-method-based-kernel-intensity","title":"K$^2$IE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes","date":"2025-05-30","arxiv_id":"2505.24704","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/k-2-ie-kernel-method-based-kernel-intensity#ran","syntology_url":"https://syntology.ai/paper/2505.24704","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.24704"}},"official":{"repos":["hidkim/k2ie"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/pretraining-deformable-image-registration","slug":"pretraining-deformable-image-registration","title":"Pretraining Deformable Image Registration Networks with Random Images","date":"2025-05-30","arxiv_id":"2505.24167","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-deep-learning-based-approach-for-mrna","slug":"a-new-deep-learning-based-approach-for-mrna","title":"A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization Factors","date":"2025-05-29","arxiv_id":"2505.23862","repositories_listed":1,"syntology":null},{"url":"/paper/amber-adaptive-mesh-generation-by-iterative","slug":"amber-adaptive-mesh-generation-by-iterative","title":"AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution Prediction","date":"2025-05-29","arxiv_id":"2505.23663","repositories_listed":1,"syntology":null},{"url":"/paper/deeprte-pre-trained-attention-based-neural","slug":"deeprte-pre-trained-attention-based-neural","title":"DeepRTE: Pre-trained Attention-based Neural Network for Radiative Tranfer","date":"2025-05-29","arxiv_id":"2505.23190","repositories_listed":1,"syntology":null},{"url":"/paper/lemore-learn-more-details-for-lightweight","slug":"lemore-learn-more-details-for-lightweight","title":"LeMoRe: Learn More Details for Lightweight Semantic Segmentation","date":"2025-05-29","arxiv_id":"2505.23093","repositories_listed":1,"syntology":null},{"url":"/paper/neural-interpretable-pdes-harmonizing-fourier","slug":"neural-interpretable-pdes-harmonizing-fourier","title":"Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery","date":"2025-05-29","arxiv_id":"2505.23106","repositories_listed":1,"syntology":null},{"url":"/paper/a-false-discovery-rate-control-method-using-a","slug":"a-false-discovery-rate-control-method-using-a","title":"A False Discovery Rate Control Method Using a Fully Connected Hidden Markov Random Field for Neuroimaging Data","date":"2025-05-27","arxiv_id":"2505.20688","repositories_listed":1,"syntology":null},{"url":"/paper/dimosr-feature-modulation-via-multi-branch","slug":"dimosr-feature-modulation-via-multi-branch","title":"DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution","date":"2025-05-27","arxiv_id":"2505.21262","repositories_listed":1,"syntology":null},{"url":"/paper/neuralom-neural-ocean-model-for-subseasonal","slug":"neuralom-neural-ocean-model-for-subseasonal","title":"NeuralOM: Neural Ocean Model for Subseasonal-to-Seasonal Simulation","date":"2025-05-27","arxiv_id":"2505.21020","repositories_listed":1,"syntology":null},{"url":"/paper/taylor-expansion-based-kolmogorov-arnold","slug":"taylor-expansion-based-kolmogorov-arnold","title":"Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment","date":"2025-05-27","arxiv_id":"2505.21592","repositories_listed":1,"syntology":null},{"url":"/paper/decoupling-spatio-temporal-prediction-when","slug":"decoupling-spatio-temporal-prediction-when","title":"Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs","date":"2025-05-26","arxiv_id":"2505.19620","repositories_listed":1,"syntology":null},{"url":"/paper/route-to-reason-adaptive-routing-for-llm-and","slug":"route-to-reason-adaptive-routing-for-llm-and","title":"Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection","date":"2025-05-26","arxiv_id":"2505.19435","repositories_listed":1,"syntology":null},{"url":"/paper/smart-pc-skeletal-model-adaptation-for-robust","slug":"smart-pc-skeletal-model-adaptation-for-robust","title":"SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds","date":"2025-05-26","arxiv_id":"2505.19546","repositories_listed":1,"syntology":null},{"url":"/paper/faithful-group-shapley-value","slug":"faithful-group-shapley-value","title":"Faithful Group Shapley Value","date":"2025-05-25","arxiv_id":"2505.19013","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/faithful-group-shapley-value#ran","syntology_url":"https://syntology.ai/paper/2505.19013","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.19013"}},"official":{"repos":["kiljael/faithful_gsv"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/limopro-reasoning-refinement-for-efficient","slug":"limopro-reasoning-refinement-for-efficient","title":"LIMOPro: Reasoning Refinement for Efficient and Effective Test-time Scaling","date":"2025-05-25","arxiv_id":"2505.19187","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/limopro-reasoning-refinement-for-efficient#ran","syntology_url":"https://syntology.ai/paper/2505.19187","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.19187"}},"official":{"repos":["gair-nlp/limopro"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pats-process-level-adaptive-thinking-mode","slug":"pats-process-level-adaptive-thinking-mode","title":"PATS: Process-Level Adaptive Thinking Mode Switching","date":"2025-05-25","arxiv_id":"2505.19250","repositories_listed":1,"syntology":null},{"url":"/paper/geometry-aware-operator-transformer-as-an","slug":"geometry-aware-operator-transformer-as-an","title":"Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains","date":"2025-05-24","arxiv_id":"2505.18781","repositories_listed":1,"syntology":null},{"url":"/paper/lota-qaf-lossless-ternary-adaptation-for","slug":"lota-qaf-lossless-ternary-adaptation-for","title":"LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning","date":"2025-05-24","arxiv_id":"2505.18724","repositories_listed":1,"syntology":null},{"url":"/paper/partition-generative-modeling-masked-modeling","slug":"partition-generative-modeling-masked-modeling","title":"Partition Generative Modeling: Masked Modeling Without Masks","date":"2025-05-24","arxiv_id":"2505.18883","repositories_listed":1,"syntology":{"n":14,"n_ran":13,"n_constructed":0,"n_ran_checked":11,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":12,"phrase":"13 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/partition-generative-modeling-masked-modeling#ran","syntology_url":"https://syntology.ai/paper/2505.18883","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.18883"}},"official":{"repos":["kuleshov-group/mdlm"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-tensor-network-approach-for-chaotic-time","slug":"a-tensor-network-approach-for-chaotic-time","title":"A tensor network approach for chaotic time series prediction","date":"2025-05-23","arxiv_id":"2505.17740","repositories_listed":1,"syntology":null},{"url":"/paper/neighbour-driven-gaussian-process-variational","slug":"neighbour-driven-gaussian-process-variational","title":"Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling","date":"2025-05-22","arxiv_id":"2505.16481","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/neighbour-driven-gaussian-process-variational#ran","syntology_url":"https://syntology.ai/paper/2505.16481","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2505.16481"}},"official":{"repos":["shixinxing/nngpvae-official"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"552cb711da4555aebe542697ea51f70c73b09ee09a5ddee8ba4e70831bd9cc2c","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}