{"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":"/method/pruning/papers/39","list_of":"/method/pruning","method":"Pruning","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":39,"pages_in_order":39,"rows_per_page":100,"rows":[3801,3874],"of":3874,"counts":{"archive_papers_tagged":3874,"with_a_code_link":1508,"where_syntology_ran_a_sample":478,"not_listed_spam_title":0,"listed":3874,"listed_where_code_ran":478,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":395,"every_run_a_failure_of_syntologys_instrument":83,"listed_with_a_run_with_no_instrument_failure":395,"listed_every_run_a_failure_of_syntologys_instrument":83,"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":"/method/pruning","prev":"/method/pruning/papers/38","next":null,"papers":[{"paper":null,"slug":"goal-driven-query-answering-for-existential","title":"Goal-Driven Query Answering for Existential Rules with Equality","date":"2017-11-14","arxiv_id":"1711.05227","n_code_links":0,"syntology":null},{"paper":"/paper/weightless-lossy-weight-encoding-for-deep","slug":"weightless-lossy-weight-encoding-for-deep","title":"Weightless: Lossy Weight Encoding For Deep Neural Network Compression","date":"2017-11-13","arxiv_id":"1711.04686","n_code_links":2,"syntology":null},{"paper":null,"slug":"block-sparse-recurrent-neural-networks","title":"Block-Sparse Recurrent Neural Networks","date":"2017-11-08","arxiv_id":"1711.02782","n_code_links":0,"syntology":null},{"paper":null,"slug":"interpreting-convolutional-neural-networks","title":"Interpreting Convolutional Neural Networks Through Compression","date":"2017-11-07","arxiv_id":"1711.02329","n_code_links":0,"syntology":null},{"paper":null,"slug":"nest-a-neural-network-synthesis-tool-based-on","title":"NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm","date":"2017-11-06","arxiv_id":"1711.02017","n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-grained-pattern-matching-over-streaming","title":"Fine-grained Pattern Matching Over Streaming Time Series","date":"2017-10-27","arxiv_id":"1710.10088","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-survey-of-model-compression-and","title":"A Survey of Model Compression and Acceleration for Deep Neural Networks","date":"2017-10-23","arxiv_id":"1710.09282","n_code_links":0,"syntology":null},{"paper":"/paper/to-prune-or-not-to-prune-exploring-the","slug":"to-prune-or-not-to-prune-exploring-the","title":"To prune, or not to prune: exploring the efficacy of pruning for model compression","date":"2017-10-05","arxiv_id":"1710.01878","n_code_links":4,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"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","official":null}},{"paper":"/paper/structured-probabilistic-pruning-for","slug":"structured-probabilistic-pruning-for","title":"Structured Probabilistic Pruning for Convolutional Neural Network Acceleration","date":"2017-09-20","arxiv_id":"1709.06994","n_code_links":2,"syntology":null},{"paper":null,"slug":"vehicle-tracking-in-wide-area-motion-imagery","title":"Vehicle Tracking in Wide Area Motion Imagery via Stochastic Progressive Association Across Multiple Frames (SPAAM)","date":"2017-09-18","arxiv_id":"1709.06035","n_code_links":0,"syntology":null},{"paper":null,"slug":"specious-rules-an-efficient-and-effective","title":"Specious rules: an efficient and effective unifying method for removing misleading and uninformative patterns in association rule mining","date":"2017-09-12","arxiv_id":"1709.03915","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-compact-and-fast-neural-machine","title":"Towards Compact and Fast Neural Machine Translation Using a Combined Method","date":"2017-09-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"circnn-accelerating-and-compressing-deep","title":"CirCNN: Accelerating and Compressing Deep Neural Networks Using Block-CirculantWeight Matrices","date":"2017-08-29","arxiv_id":"1708.08917","n_code_links":0,"syntology":null},{"paper":null,"slug":"trannsformer-neural-network-transformation","title":"TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design","date":"2017-08-26","arxiv_id":"1708.07949","n_code_links":0,"syntology":null},{"paper":null,"slug":"galileo-a-generalized-low-entropy-mixture","title":"GALILEO: A Generalized Low-Entropy Mixture Model","date":"2017-08-24","arxiv_id":"1708.07242","n_code_links":0,"syntology":null},{"paper":null,"slug":"massively-parallel-feature-selection-for-big","title":"Massively-Parallel Feature Selection for Big Data","date":"2017-08-23","arxiv_id":"1708.07178","n_code_links":0,"syntology":null},{"paper":null,"slug":"reduced-space-and-faster-convergence-in","title":"Reduced Space and Faster Convergence in Imperfect-Information Games via Pruning","date":"2017-08-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"fine-pruning-joint-fine-tuning-and","title":"Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization","date":"2017-07-28","arxiv_id":"1707.09102","n_code_links":0,"syntology":null},{"paper":null,"slug":"effective-inference-for-generative-neural","title":"Effective Inference for Generative Neural Parsing","date":"2017-07-27","arxiv_id":"1707.08976","n_code_links":0,"syntology":null},{"paper":"/paper/end-to-end-neural-coreference-resolution","slug":"end-to-end-neural-coreference-resolution","title":"End-to-end Neural Coreference Resolution","date":"2017-07-21","arxiv_id":"1707.07045","n_code_links":4,"syntology":{"ran":3,"of":3,"n_ran_checked":0,"n_instrument":3,"unverified":0,"pointer_only":3,"phrase":"3 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; 3 where Syntology's instrument failed) · 0 unverified","official":{"repos":["kentonl/e2e-coref"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"neuron-pruning-for-compressing-deep-networks","title":"Neuron Pruning for Compressing Deep Networks using Maxout Architectures","date":"2017-07-21","arxiv_id":"1707.06838","n_code_links":0,"syntology":null},{"paper":null,"slug":"thinet-a-filter-level-pruning-method-for-deep","title":"ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression","date":"2017-07-20","arxiv_id":"1707.06342","n_code_links":0,"syntology":null},{"paper":"/paper/channel-pruning-for-accelerating-very-deep","slug":"channel-pruning-for-accelerating-very-deep","title":"Channel Pruning for Accelerating Very Deep Neural Networks","date":"2017-07-19","arxiv_id":"1707.06168","n_code_links":1,"syntology":null},{"paper":null,"slug":"pruning-convolutional-neural-networks-for-1","title":"Pruning Convolutional Neural Networks for Image Instance Retrieval","date":"2017-07-18","arxiv_id":"1707.05455","n_code_links":0,"syntology":null},{"paper":"/paper/model-compression-as-constrained-optimization-1","slug":"model-compression-as-constrained-optimization-1","title":"Model compression as constrained optimization, with application to neural nets. Part I: general framework","date":"2017-07-05","arxiv_id":"1707.01209","n_code_links":1,"syntology":null},{"paper":null,"slug":"on-compressing-deep-models-by-low-rank-and","title":"On Compressing Deep Models by Low Rank and Sparse Decomposition","date":"2017-07-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"structured-sparse-ternary-weight-coding-of","title":"Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations","date":"2017-07-01","arxiv_id":"1707.03684","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-entropy-based-pruning-method-for-cnn","title":"An Entropy-based Pruning Method for CNN Compression","date":"2017-06-19","arxiv_id":"1706.05791","n_code_links":0,"syntology":null},{"paper":null,"slug":"improving-scalability-of-inductive-logic","title":"Improving Scalability of Inductive Logic Programming via Pruning and Best-Effort Optimisation","date":"2017-06-16","arxiv_id":"1706.05171","n_code_links":0,"syntology":null},{"paper":null,"slug":"local-feature-descriptor-learning-with","title":"Local Feature Descriptor Learning with Adaptive Siamese Network","date":"2017-06-16","arxiv_id":"1706.05358","n_code_links":0,"syntology":null},{"paper":"/paper/to-index-or-not-to-index-optimizing-exact","slug":"to-index-or-not-to-index-optimizing-exact","title":"To Index or Not to Index: Optimizing Exact Maximum Inner Product Search","date":"2017-06-05","arxiv_id":"1706.01449","n_code_links":1,"syntology":null},{"paper":null,"slug":"exploring-the-regularity-of-sparse-structure","title":"Exploring the Regularity of Sparse Structure in Convolutional Neural Networks","date":"2017-05-24","arxiv_id":"1705.08922","n_code_links":0,"syntology":null},{"paper":null,"slug":"scnn-an-accelerator-for-compressed-sparse","title":"SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks","date":"2017-05-23","arxiv_id":"1708.04485","n_code_links":0,"syntology":null},{"paper":"/paper/learning-to-prune-deep-neural-networks-via","slug":"learning-to-prune-deep-neural-networks-via","title":"Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon","date":"2017-05-22","arxiv_id":"1705.07565","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":0,"n_instrument":1,"unverified":0,"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","official":{"repos":["csyhhu/L-OBS"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"structural-compression-of-convolutional","title":"Structural Compression of Convolutional Neural Networks","date":"2017-05-20","arxiv_id":"1705.07356","n_code_links":0,"syntology":null},{"paper":null,"slug":"building-effective-deep-neural-network","title":"Building effective deep neural network architectures one feature at a time","date":"2017-05-18","arxiv_id":"1705.06778","n_code_links":0,"syntology":null},{"paper":null,"slug":"evolving-ensemble-fuzzy-classifier","title":"Evolving Ensemble Fuzzy Classifier","date":"2017-05-18","arxiv_id":"1705.06460","n_code_links":0,"syntology":null},{"paper":null,"slug":"identifying-hazardousness-of-sewer-pipeline","title":"Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study","date":"2017-05-16","arxiv_id":"1707.00561","n_code_links":0,"syntology":null},{"paper":null,"slug":"design-of-a-very-compact-cnn-classifier-for","title":"Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling","date":"2017-05-15","arxiv_id":"1705.05207","n_code_links":0,"syntology":null},{"paper":"/paper/learning-with-confident-examples-rank-pruning","slug":"learning-with-confident-examples-rank-pruning","title":"Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels","date":"2017-05-04","arxiv_id":"1705.01936","n_code_links":3,"syntology":{"ran":2,"of":3,"n_ran_checked":0,"n_instrument":2,"unverified":1,"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) · 1 unverified","official":{"repos":["cgnorthcutt/rankpruning"],"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"]}}},{"paper":null,"slug":"a-reverse-hex-solver","title":"A Reverse Hex Solver","date":"2017-04-26","arxiv_id":"1707.00627","n_code_links":0,"syntology":null},{"paper":"/paper/efficient-gender-classification-using-a-deep","slug":"efficient-gender-classification-using-a-deep","title":"Efficient Gender Classification Using a Deep LDA-Pruned Net","date":"2017-04-20","arxiv_id":"1704.06305","n_code_links":1,"syntology":null},{"paper":"/paper/exploring-sparsity-in-recurrent-neural","slug":"exploring-sparsity-in-recurrent-neural","title":"Exploring Sparsity in Recurrent Neural Networks","date":"2017-04-17","arxiv_id":"1704.05119","n_code_links":1,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":1,"phrase":"1 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; 0 where Syntology's instrument failed) · 0 unverified","official":null}},{"paper":"/paper/learning-detection-with-diverse-proposals","slug":"learning-detection-with-diverse-proposals","title":"Learning Detection with Diverse Proposals","date":"2017-04-11","arxiv_id":"1704.03533","n_code_links":1,"syntology":null},{"paper":null,"slug":"k-best-iterative-viterbi-parsing","title":"K-best Iterative Viterbi Parsing","date":"2017-04-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-thinner-convolutional-neural-networks","title":"Towards thinner convolutional neural networks through Gradually Global Pruning","date":"2017-03-29","arxiv_id":"1703.09916","n_code_links":0,"syntology":null},{"paper":null,"slug":"temporal-information-extraction-for-question","title":"Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture","date":"2017-03-17","arxiv_id":"1703.05851","n_code_links":0,"syntology":null},{"paper":null,"slug":"resilience-a-criterion-for-learning-in-the","title":"Resilience: A Criterion for Learning in the Presence of Arbitrary Outliers","date":"2017-03-15","arxiv_id":"1703.04940","n_code_links":0,"syntology":null},{"paper":null,"slug":"death-and-rebirth-of-neural-activity-in","title":"Death and rebirth of neural activity in sparse inhibitory networks","date":"2017-03-12","arxiv_id":"1610.07181","n_code_links":0,"syntology":null},{"paper":"/paper/a-log-linear-time-algorithm-for-constrained","slug":"a-log-linear-time-algorithm-for-constrained","title":"A log-linear time algorithm for constrained changepoint detection","date":"2017-03-09","arxiv_id":"1703.03352","n_code_links":7,"syntology":null},{"paper":null,"slug":"theoretical-and-experimental-analysis-of-the","title":"Theoretical and Experimental Analysis of the Canadian Traveler Problem","date":"2017-02-22","arxiv_id":"1702.07001","n_code_links":0,"syntology":null},{"paper":"/paper/soft-weight-sharing-for-neural-network","slug":"soft-weight-sharing-for-neural-network","title":"Soft Weight-Sharing for Neural Network Compression","date":"2017-02-13","arxiv_id":"1702.04008","n_code_links":3,"syntology":null},{"paper":"/paper/incremental-network-quantization-towards","slug":"incremental-network-quantization-towards","title":"Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights","date":"2017-02-10","arxiv_id":"1702.03044","n_code_links":3,"syntology":null},{"paper":null,"slug":"pruned-non-local-means","title":"Pruned non-local means","date":"2017-01-28","arxiv_id":"1701.08280","n_code_links":0,"syntology":null},{"paper":null,"slug":"personalized-classifier-ensemble-pruning","title":"Personalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing","date":"2017-01-25","arxiv_id":"1701.07166","n_code_links":0,"syntology":null},{"paper":null,"slug":"compression-of-deep-neural-networks-for-image","title":"Compression of Deep Neural Networks for Image Instance Retrieval","date":"2017-01-18","arxiv_id":"1701.04923","n_code_links":0,"syntology":null},{"paper":null,"slug":"the-incredible-shrinking-neural-network-new","title":"The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning","date":"2017-01-16","arxiv_id":"1701.04465","n_code_links":0,"syntology":null},{"paper":null,"slug":"dynamic-deep-neural-networks-optimizing","title":"Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-offs by Selective Execution","date":"2017-01-02","arxiv_id":"1701.00299","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-to-prune-exploring-the-frontier-of","title":"Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing","date":"2017-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"scalable-group-level-probabilistic-sparse","title":"Scalable Group Level Probabilistic Sparse Factor Analysis","date":"2016-12-14","arxiv_id":"1612.04555","n_code_links":0,"syntology":null},{"paper":"/paper/modeling-cognitive-deficits-following","slug":"modeling-cognitive-deficits-following","title":"Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks","date":"2016-12-13","arxiv_id":"1612.04423","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-general-framework-for-density-based-time","title":"A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy","date":"2016-12-02","arxiv_id":"1612.00637","n_code_links":0,"syntology":null},{"paper":null,"slug":"ese-efficient-speech-recognition-engine-with","title":"ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA","date":"2016-12-01","arxiv_id":"1612.00694","n_code_links":0,"syntology":null},{"paper":null,"slug":"neural-attention-for-learning-to-rank","title":"Neural Attention for Learning to Rank Questions in Community Question Answering","date":"2016-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"voronoi-based-compact-image-descriptors","title":"Voronoi-based compact image descriptors: Efficient Region-of-Interest retrieval with VLAD and deep-learning-based descriptors","date":"2016-11-27","arxiv_id":"1611.08906","n_code_links":0,"syntology":null},{"paper":"/paper/pruning-convolutional-neural-networks-for","slug":"pruning-convolutional-neural-networks-for","title":"Pruning Convolutional Neural Networks for Resource Efficient Inference","date":"2016-11-19","arxiv_id":"1611.06440","n_code_links":8,"syntology":{"ran":2,"of":2,"n_ran_checked":0,"n_instrument":2,"unverified":0,"pointer_only":2,"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","official":null}},{"paper":null,"slug":"noiseout-a-simple-way-to-prune-neural","title":"NoiseOut: A Simple Way to Prune Neural Networks","date":"2016-11-18","arxiv_id":"1611.06211","n_code_links":0,"syntology":null},{"paper":null,"slug":"designing-energy-efficient-convolutional","title":"Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning","date":"2016-11-16","arxiv_id":"1611.05128","n_code_links":0,"syntology":null},{"paper":"/paper/neural-symbolic-machines-learning-semantic-1","slug":"neural-symbolic-machines-learning-semantic-1","title":"Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision","date":"2016-10-31","arxiv_id":"1611.00020","n_code_links":2,"syntology":null},{"paper":null,"slug":"compact-deep-convolutional-neural-networks","title":"Compact Deep Convolutional Neural Networks With Coarse Pruning","date":"2016-10-30","arxiv_id":"1610.09639","n_code_links":0,"syntology":null},{"paper":"/paper/ssh-sketch-shingle-hash-for-indexing-massive","slug":"ssh-sketch-shingle-hash-for-indexing-massive","title":"SSH (Sketch, Shingle, & Hash) for Indexing Massive-Scale Time Series","date":"2016-10-24","arxiv_id":"1610.07328","n_code_links":1,"syntology":null},{"paper":null,"slug":"learning-cost-effective-treatment-regimes","title":"Learning Cost-Effective Treatment Regimes using Markov Decision Processes","date":"2016-10-21","arxiv_id":"1610.06972","n_code_links":0,"syntology":null},{"paper":null,"slug":"regularized-dynamic-boltzmann-machine-with","title":"Regularized Dynamic Boltzmann Machine with Delay Pruning for Unsupervised Learning of Temporal Sequences","date":"2016-09-22","arxiv_id":"1610.01989","n_code_links":0,"syntology":null},{"paper":"/paper/pruning-filters-for-efficient-convnets","slug":"pruning-filters-for-efficient-convnets","title":"Pruning Filters for Efficient ConvNets","date":"2016-08-31","arxiv_id":"1608.08710","n_code_links":21,"syntology":{"ran":23,"of":36,"n_ran_checked":9,"n_instrument":14,"unverified":13,"pointer_only":22,"phrase":"23 ran (of which 4 constructed an object rather than computing a result; 9 with no instrument failure: 1 honoured, 1 violated, 7 with no contract checked; 14 where Syntology's instrument failed) · 13 unverified","official":null}}],"record_sha256":"f5c02b7265155f2ff3e07d181d23a8d4bd329435c990ebead0fc3636ca60331f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}