CNT Cross-Domain Decoder v1 — Sign-Aware Reconstruction of Domains from Multi-Domain Drift Fields

OverviewThis dataset provides the first Cross-Domain Decoder release for Cognitive Nexus Theory (CNT): CNT Cross-Domain Decoder v1 (CDDT v1). It quantifies how well one domain’s behaviour can be decoded from the others using CNT drift/hazard fields and related features, across finance, crypto, volatility, and other domains used in the CNT backbone. In plain language: given the CNT field, how well can we reconstruct “what SPY is doing” from {BTC, VIX, …}, or “what VIX is doing” from the others? CDDT v1 is intended as a companion to: CNT Cross-Domain Hazard Pure v1 (baseline hazard planes), CNT Global Drift Field v1 (the D_global index), and CNT Cross-Domain Echo Window v1 (lead–lag “echo windows”). Where those datasets describe the geometry and timing of drift, CDDT v1 tests whether that shared geometry is informational: whether one system can be reliably decoded from the rest. Files in this release (CNT_CDDT_v1_release.zip) CDDT_v1_README.txt Human-readable documentation for this release: how the decoder was set up, which domains and targets are included, and how to interpret each matrix and summary file. cnt_cddt_auc_matrix.csv Matrix of decoding performance (e.g. AUC values) for all source → target domain pairs. Each cell summarises how well the decoder predicts the target domain from the remaining domains under the chosen setup (see README for exact details). cnt_cddt_pval_matrix.csv Matrix of p-values comparing observed decoding performance against a null or surrogate baseline, indicating which source→target decoders carry statistically significant information beyond chance (see README for the exact null model). cnt_cddt_signaware_auc_matrix.csv “Sign-aware” decoding performance matrix: a variant that takes into account not just the magnitude but the direction (sign) of the target behaviour, useful for distinguishing “gets the side of the move right” from “just segments the regime.” cnt_cddt_summary_pairs.csv Flattened summary table of decoder results for each source→target pair, including AUC and possibly other metrics/flags. cnt_cddt_signaware_summary_pairs.csv Sign-aware version of the pairwise summary, focusing on sign-sensitive decoding performance. Together, these files provide a compact view of who can decode whom, and with what strength, in the CNT field. Role in Cognitive Nexus Theory (CNT)CDDT v1 plays three roles in the CNT ecosystem: Information test: It tests whether drift/hazard features across domains truly share structure, by asking if one domain can be reconstructed from the rest. Complement to echoes: It complements the Cross-Domain Echo Window dataset: echoes say “X tends to move before Y at lag H”, while the decoder asks “can we recover Y from the entire field?” Backbone check: It serves as a consistency check on the hazard and drift backbones, probing whether they encode meaningful cross-domain information or just cosmetic correlations. Intended useThis dataset is suitable for: Analysing which domains are easiest or hardest to decode from others. Comparing decoding patterns against echo windows and Forbidden Drift Zones. Testing alternative decoder architectures or null models using the same matrices as benchmarks. Exploring whether certain domains act as “information hubs” (good sources) or “opaque nodes” (hard targets). License and reuseThis dataset is released to enable open replication, criticism, and extension. Please cite this Zenodo record and related CNT backbone datasets when using it.本数据集发布了认知关联理论（Cognitive Nexus Theory, CNT）的首个跨域解码器数据集：CNT跨域解码器v1（CDDT v1）。本数据集量化了借助CNT漂移场/风险场及相关特征，在金融、加密货币、波动率等CNT核心框架所涵盖的领域中，从其余领域解码某一领域行为的效果。 直白来说：若已获取CNT场，我们从{BTC、VIX等}中还原「SPY的走势」的效果如何，或是从其余领域中还原「VIX的走势」的效果如何？ CDDT v1 作为配套数据集，与以下三者搭配使用：CNT跨域风险纯数据集v1（基准风险平面）、CNT全局漂移场v1（D_global指数）以及CNT跨域回声窗口v1（超前滞后「回声窗口」）。 上述数据集描述了漂移的几何特征与时序信息，而CDDT v1则用于验证这类共享几何特征是否具备信息价值：即能否从其余系统可靠解码出某一系统的行为。 本次发布的文件（打包于CNT_CDDT_v1_release.zip）包括： CDDT_v1_README.txt：本发布版本的可读文档，涵盖解码器设置方式、涵盖的领域与目标域、以及如何解读各矩阵与汇总文件。 cnt_cddt_auc_matrix.csv：所有源→目标域对的解码性能矩阵（例如AUC值）。每个单元格汇总了在指定设置下，解码器从其余领域预测目标域行为的效果（详见README文件的具体细节）。 cnt_cddt_pval_matrix.csv：p值矩阵，用于将观测到的解码性能与零假设或替代基线进行比较，可指示哪些源→目标解码器具备超出随机概率的统计显著信息（详见README文件的零假设模型细节）。 cnt_cddt_signaware_auc_matrix.csv：「符号感知型」解码性能矩阵：该变体不仅考量目标行为的幅度，还兼顾其方向（符号），有助于区分「准确把握走势方向」与「仅能划分状态区间」的效果。 cnt_cddt_summary_pairs.csv：所有源→目标域对的解码器结果扁平化汇总表，涵盖AUC值及其他可能的指标与标记。 cnt_cddt_signaware_summary_pairs.csv：符号感知型成对结果汇总表，聚焦于对符号敏感的解码性能。 上述文件可直观展现CNT场中各领域间的解码关系与解码强度。 CDDT v1 在CNT生态系统中承担三大作用： 1. 信息有效性测试：通过验证「能否从其余领域还原某一领域行为」，检验跨领域的漂移/风险特征是否真正共享结构。 2. 回声数据集补充：作为CNT跨域回声窗口数据集的配套内容：回声数据集描述「X往往在滞后H期领先于Y变动」，而本解码器则验证「能否从完整场域中还原Y的行为」。 3. 核心框架一致性校验：作为对风险与漂移核心框架的一致性检验，探查其是否编码了有意义的跨域信息，而非仅为表面相关性。 本数据集适用场景包括： 1. 分析哪些领域最易或最难从其余领域解码。 2. 将解码模式与回声窗口及「禁漂移区」进行对比。 3. 以本数据集的矩阵为基准，测试替代解码器架构或零假设模型。 4. 探索特定领域是否充当「信息枢纽（优质源域）」或「不透明节点（难解码目标域）」。 本数据集以开放共享的形式发布，旨在支持可复现研究、批评性讨论与扩展开发。使用本数据集时，请引用本Zenodo记录及相关CNT核心框架数据集。