{"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/test-time-adaptation/papers/5","list_of":"/task/test-time-adaptation","task":"Test-time Adaptation","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":5,"pages_in_order":5,"rows_per_page":100,"rows":[401,484],"of":484,"counts":{"archive_papers_tagged":484,"with_a_code_link":229,"where_syntology_ran_a_sample":111,"not_listed_spam_title":0,"listed":484,"listed_where_code_ran":111,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":91,"every_run_a_failure_of_syntologys_instrument":20,"listed_with_a_run_with_no_instrument_failure":91,"listed_every_run_a_failure_of_syntologys_instrument":20,"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/test-time-adaptation","prev":"/task/test-time-adaptation/papers/4","next":null,"papers":[{"url":"/paper/3dhr-co-a-collaborative-test-time-refinement","slug":"3dhr-co-a-collaborative-test-time-refinement","title":"3DHR-Co: A Collaborative Test-time Refinement Framework for In-the-Wild 3D Human-Body Reconstruction Task","date":"2023-10-02","arxiv_id":"2310.01291","repositories_listed":0,"syntology":null},{"url":null,"slug":"vpa-fully-test-time-visual-prompt-adaptation","title":"VPA: Fully Test-Time Visual Prompt Adaptation","date":"2023-09-26","arxiv_id":"2309.15251","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-image-test-time-adaptation-for","title":"Single Image Test-Time Adaptation for Segmentation","date":"2023-09-25","arxiv_id":"2309.14052","repositories_listed":0,"syntology":null},{"url":null,"slug":"can-whisper-perform-speech-based-in-context","title":"Can Whisper perform speech-based in-context learning?","date":"2023-09-13","arxiv_id":"2309.07081","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-real-time-training-of-physics","title":"A Novel Training Framework for Physics-informed Neural Networks: Towards Real-time Applications in Ultrafast Ultrasound Blood Flow Imaging","date":"2023-09-09","arxiv_id":"2309.04755","repositories_listed":0,"syntology":null},{"url":null,"slug":"better-practices-for-domain-adaptation","title":"Better Practices for Domain Adaptation","date":"2023-09-07","arxiv_id":"2309.03879","repositories_listed":0,"syntology":null},{"url":null,"slug":"realm-robust-entropy-adaptive-loss","title":"REALM: Robust Entropy Adaptive Loss Minimization for Improved Single-Sample Test-Time Adaptation","date":"2023-09-07","arxiv_id":"2309.03964","repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-adaptation-for-satellite-borne","title":"Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection","date":"2023-09-05","arxiv_id":"2309.02150","repositories_listed":0,"syntology":null},{"url":null,"slug":"point-tta-test-time-adaptation-for-point","title":"Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning","date":"2023-08-31","arxiv_id":"2308.16481","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-for-point-cloud","title":"Test-Time Adaptation for Point Cloud Upsampling Using Meta-Learning","date":"2023-08-31","arxiv_id":"2308.16484","repositories_listed":0,"syntology":null},{"url":null,"slug":"is-it-an-i-or-an-l-test-time-adaptation-of","title":"Is it an i or an l: Test-time Adaptation of Text Line Recognition Models","date":"2023-08-29","arxiv_id":"2308.15037","repositories_listed":0,"syntology":null},{"url":null,"slug":"label-shift-adapter-for-test-time-adaptation","title":"Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts","date":"2023-08-17","arxiv_id":"2308.08810","repositories_listed":0,"syntology":null},{"url":null,"slug":"geoadapt-self-supervised-test-time-adaption","title":"GeoAdapt: Self-Supervised Test-Time Adaptation in LiDAR Place Recognition Using Geometric Priors","date":"2023-08-09","arxiv_id":"2308.04638","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-for-nighttime-color","title":"Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation","date":"2023-07-10","arxiv_id":"2307.04470","repositories_listed":0,"syntology":null},{"url":null,"slug":"pay-attention-to-the-atlas-atlas-guided-test","title":"Pay Attention to the Atlas: Atlas-Guided Test-Time Adaptation Method for Robust 3D Medical Image Segmentation","date":"2023-07-02","arxiv_id":"2307.00676","repositories_listed":0,"syntology":null},{"url":null,"slug":"design-from-policies-conservative-test-time-1","title":"Design from Policies: Conservative Test-Time Adaptation for Offline Policy Optimization","date":"2023-06-26","arxiv_id":"2306.14479","repositories_listed":0,"syntology":null},{"url":null,"slug":"factorised-speaker-environment-adaptive","title":"Factorised Speaker-environment Adaptive Training of Conformer Speech Recognition Systems","date":"2023-06-26","arxiv_id":"2306.14608","repositories_listed":0,"syntology":null},{"url":null,"slug":"fourier-test-time-adaptation-with-multi-level","title":"Fourier Test-time Adaptation with Multi-level Consistency for Robust Classification","date":"2023-06-05","arxiv_id":"2306.02544","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-with-perturbation","title":"Test-Time Adaptation with Perturbation Consistency Learning","date":"2023-04-25","arxiv_id":"2304.12764","repositories_listed":0,"syntology":null},{"url":null,"slug":"sata-source-anchoring-and-target-alignment","title":"SATA: Source Anchoring and Target Alignment Network for Continual Test Time Adaptation","date":"2023-04-20","arxiv_id":"2304.10113","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-auxiliary-learning-for-adaptive-human","title":"Meta-Auxiliary Learning for Adaptive Human Pose Prediction","date":"2023-04-13","arxiv_id":"2304.06411","repositories_listed":0,"syntology":null},{"url":null,"slug":"stfar-improving-object-detection-robustness","title":"STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization","date":"2023-03-31","arxiv_id":"2303.17937","repositories_listed":0,"syntology":null},{"url":null,"slug":"tta-cope-test-time-adaptation-for-category","title":"TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation","date":"2023-03-29","arxiv_id":"2303.16730","repositories_listed":0,"syntology":null},{"url":null,"slug":"train-test-time-adaptation-with-retrieval","title":"Train/Test-Time Adaptation with Retrieval","date":"2023-03-25","arxiv_id":"2303.14333","repositories_listed":0,"syntology":null},{"url":null,"slug":"tempt-temporal-consistency-for-test-time","title":"TempT: Temporal consistency for Test-time adaptation","date":"2023-03-19","arxiv_id":"2303.10536","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-modal-continual-test-time-adaptation","title":"Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation","date":"2023-03-18","arxiv_id":"2303.10457","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-adapt-to-online-streams-with","title":"Learning to Adapt to Online Streams with Distribution Shifts","date":"2023-03-02","arxiv_id":"2303.01630","repositories_listed":0,"syntology":null},{"url":null,"slug":"neuro-modulated-hebbian-learning-for-fully","title":"Neuro-Modulated Hebbian Learning for Fully Test-Time Adaptation","date":"2023-03-02","arxiv_id":"2303.00914","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-convolutional-visual-prompts","title":"Convolutional Visual Prompt for Robust Visual Perception","date":"2023-03-01","arxiv_id":"2303.00198","repositories_listed":0,"syntology":null},{"url":null,"slug":"transadapt-a-transformative-framework-for","title":"TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation","date":"2023-02-24","arxiv_id":"2302.14611","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-image-segmentation-two-decades-of","title":"Semantic Image Segmentation: Two Decades of Research","date":"2023-02-13","arxiv_id":"2302.06378","repositories_listed":0,"syntology":null},{"url":null,"slug":"ttn-a-domain-shift-aware-batch-normalization","title":"TTN: A Domain-Shift Aware Batch Normalization in Test-Time Adaptation","date":"2023-02-10","arxiv_id":"2302.05155","repositories_listed":0,"syntology":null},{"url":null,"slug":"delta-degradation-free-fully-test-time","title":"DELTA: degradation-free fully test-time adaptation","date":"2023-01-30","arxiv_id":"2301.13018","repositories_listed":0,"syntology":null},{"url":null,"slug":"uncovering-adversarial-risks-of-test-time","title":"Uncovering Adversarial Risks of Test-Time Adaptation","date":"2023-01-29","arxiv_id":"2301.12576","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethinking-precision-of-pseudo-label-test","title":"Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning","date":"2023-01-15","arxiv_id":"2301.06013","repositories_listed":0,"syntology":null},{"url":null,"slug":"back-to-the-source-diffusion-driven","title":"Back to the Source: Diffusion-Driven Adaptation To Test-Time Corruption","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"command-driven-articulated-object","title":"Command-Driven Articulated Object Understanding and Manipulation","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-with-regularized-loss","title":"Test Time Adaptation With Regularized Loss for Weakly Supervised Salient Object Detection","date":"2023-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"cd-tta-compound-domain-test-time-adaptation","title":"Test-time Adaptation in the Dynamic World with Compound Domain Knowledge Management","date":"2022-12-16","arxiv_id":"2212.08356","repositories_listed":0,"syntology":null},{"url":"/paper/test-time-adaptation-vs-training-time","slug":"test-time-adaptation-vs-training-time","title":"Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation","date":"2022-12-12","arxiv_id":"2212.06242","repositories_listed":0,"syntology":null},{"url":null,"slug":"decorate-the-newcomers-visual-domain-prompt","title":"Decorate the Newcomers: Visual Domain Prompt for Continual Test Time Adaptation","date":"2022-12-08","arxiv_id":"2212.04145","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-distribution-shift-at-test-time-in","title":"Addressing Distribution Shift at Test Time in Pre-trained Language Models","date":"2022-12-05","arxiv_id":"2212.02384","repositories_listed":0,"syntology":null},{"url":null,"slug":"cloud-device-collaborative-adaptation-to","title":"Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world","date":"2022-12-02","arxiv_id":"2212.00972","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-conscious-active-fine-tuning-to","title":"Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation","date":"2022-11-02","arxiv_id":"2211.01315","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-understanding-gd-with-hard-and","title":"Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation","date":"2022-10-18","arxiv_id":"2210.10019","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-less-generalizable-patterns-with-an","title":"Learning Less Generalizable Patterns with an Asymmetrically Trained Double Classifier for Better Test-Time Adaptation","date":"2022-10-17","arxiv_id":"2210.09834","repositories_listed":0,"syntology":null},{"url":"/paper/visual-prompt-tuning-for-test-time-domain","slug":"visual-prompt-tuning-for-test-time-domain","title":"Visual Prompt Tuning for Test-time Domain Adaptation","date":"2022-10-10","arxiv_id":"2210.04831","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-test-time-self-training-under","title":"TeST: Test-time Self-Training under Distribution Shift","date":"2022-09-23","arxiv_id":"2209.11459","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-with-principal-component","title":"Test-Time Adaptation with Principal Component Analysis","date":"2022-09-13","arxiv_id":"2209.05779","repositories_listed":0,"syntology":null},{"url":null,"slug":"anticipating-the-unseen-discrepancy-for","title":"Anticipating the Unseen Discrepancy for Vision and Language Navigation","date":"2022-09-10","arxiv_id":"2209.04725","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphtta-test-time-adaptation-on-graph-neural","title":"GraphTTA: Test Time Adaptation on Graph Neural Networks","date":"2022-08-19","arxiv_id":"2208.09126","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-test-time-adaptation-via-shift","title":"Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes","date":"2022-07-24","arxiv_id":"2207.11707","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalizing-to-unseen-domains-with","title":"Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge","date":"2022-07-11","arxiv_id":"2207.04913","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-for-real-image-denoising","title":"Test-time Adaptation for Real Image Denoising via Meta-transfer Learning","date":"2022-07-05","arxiv_id":"2207.02066","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-domain-generalization-in-medical-image","title":"Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary","date":"2022-06-29","arxiv_id":"2206.14467","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-for-visual-document","title":"Test-Time Adaptation for Visual Document Understanding","date":"2022-06-15","arxiv_id":"2206.07240","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-effectiveness-of-fine-tuning-versus","title":"On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning","date":"2022-06-07","arxiv_id":"2206.03271","repositories_listed":0,"syntology":null},{"url":null,"slug":"cafa-class-aware-feature-alignment-for-test","title":"CAFA: Class-Aware Feature Alignment for Test-Time Adaptation","date":"2022-06-01","arxiv_id":"2206.00205","repositories_listed":0,"syntology":null},{"url":null,"slug":"exemplar-free-class-agnostic-counting","title":"Exemplar Free Class Agnostic Counting","date":"2022-05-27","arxiv_id":"2205.14212","repositories_listed":0,"syntology":null},{"url":null,"slug":"covariance-aware-feature-alignment-with-pre","title":"Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation to Multiple Image Corruptions","date":"2022-04-28","arxiv_id":"2204.13263","repositories_listed":0,"syntology":null},{"url":null,"slug":"mm-tta-multi-modal-test-time-adaptation-for","title":"MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation","date":"2022-04-27","arxiv_id":"2204.12667","repositories_listed":0,"syntology":null},{"url":null,"slug":"unseen-object-instance-segmentation-with","title":"Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation","date":"2022-04-21","arxiv_id":"2204.09847","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-instance-specific-adaptation-for","title":"Learning Instance-Specific Adaptation for Cross-Domain Segmentation","date":"2022-03-30","arxiv_id":"2203.16530","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-for-semantic-3","title":"Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey","date":"2021-12-06","arxiv_id":"2112.03241","repositories_listed":0,"syntology":null},{"url":null,"slug":"sita-single-image-test-time-adaptation","title":"SITA: Single Image Test-time Adaptation","date":"2021-12-04","arxiv_id":"2112.02355","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-adaptation-of-semantic","title":"Unsupervised Adaptation of Semantic Segmentation Models without Source Data","date":"2021-12-04","arxiv_id":"2112.02359","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-test-time-adaptive-face","title":"Federated Test-Time Adaptive Face Presentation Attack Detection with Dual-Phase Privacy Preservation","date":"2021-10-25","arxiv_id":"2110.12613","repositories_listed":0,"syntology":null},{"url":null,"slug":"mixnorm-test-time-adaptation-through-online","title":"MixNorm: Test-Time Adaptation Through Online Normalization Estimation","date":"2021-10-21","arxiv_id":"2110.11478","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-domain-adaptation-for-visual","title":"Self-Supervised Domain Adaptation for Visual Navigation with Global Map Consistency","date":"2021-10-14","arxiv_id":"2110.07184","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-batch-statistics-calibration-for","title":"Test-time Batch Statistics Calibration for Covariate Shift","date":"2021-10-06","arxiv_id":"2110.04065","repositories_listed":0,"syntology":null},{"url":null,"slug":"source-free-few-shot-domain-adaptation","title":"Source-Free Few-Shot Domain Adaptation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-robustification-of-deep-models-via","title":"Test Time Robustification of Deep Models via Adaptation and Augmentation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"testing-time-adaptation-through-online","title":"Testing-Time Adaptation through Online Normalization Estimation","date":"2021-09-29","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-to-distribution-shift-by","title":"Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation","date":"2021-06-28","arxiv_id":"2106.14999","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-self-training-for-cross-domain","title":"Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis","date":"2021-06-23","arxiv_id":"2106.12499","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-fast-adaptation-for-dynamic-scene","title":"Test-Time Fast Adaptation for Dynamic Scene Deblurring via Meta-Auxiliary Learning","date":"2021-06-19","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-to-distribution-shifts-1","title":"Test-Time Adaptation to Distribution Shifts by Confidence Maximization and Input Transformation","date":"2021-05-21","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-toward-personalized","title":"Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation","date":"2021-05-08","arxiv_id":"2105.03544","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-for-super-resolution-you","title":"Test-Time Adaptation for Super-Resolution: You Only Need to Overfit on a Few More Images","date":"2021-04-06","arxiv_id":"2104.02663","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-for-out-of-distributed","title":"Test-Time Adaptation for Out-of-distributed Image Inpainting","date":"2021-02-02","arxiv_id":"2102.01360","repositories_listed":0,"syntology":null},{"url":null,"slug":"test-time-adaptation-and-adversarial","title":"Test-Time Adaptation and Adversarial Robustness","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"self-adaptively-learning-to-demoire-from-1","title":"Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs","date":"2020-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"style-invariant-cardiac-image-segmentation","title":"Style-invariant Cardiac Image Segmentation with Test-time Augmentation","date":"2020-09-24","arxiv_id":"2009.12193","repositories_listed":0,"syntology":null},{"url":null,"slug":"lattice-based-unsupervised-test-time","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","date":"2019-06-27","arxiv_id":"1906.11521","repositories_listed":0,"syntology":null}],"record_sha256":"792cd433c85593c0e8c6ddceee90ec116495a258c155bc5b46fbaf7624808955","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}