## Performance evidence update / 18 September 2026 MODALITY PERFORMANCE / EVIDENCE REVIEWED 18 SEPTEMBER 2026 Define the useful result. Measure the complete job. A modality-specific timing is not yet linked to this guide. There may be supporting engineering measurements in the internal archive. We are reconciling their workload, result quality and comparison conditions before presenting a public chart. A benchmark for an adjacent model does not establish this model’s performance. 01 Shape, dtype & output correctness 02 Contraction / slice latency 03 Peak working memory Include memory, storage, energy and hardware writes # Tensors | 8DB Product reference. Reviewed 18 September 2026. Implementation and evaluation scope are stated below. Explore the models (https://8braid.com/modalities) / 8DB Tensors Work across the dimensions that matter. Represent multidimensional arrays and explore supported factorization and multiscale analysis paths. Explore the capabilities See a complete workload Factor · project · analyze Model / Tensors Conceptual diagram [Diagram: tensors representation: conceptual diagram] An array is shown as slices through several dimensions. A selected slice provides one view of the larger structure. Capabilities What changes Input to value Alternatives Performance Adoption Related articles Reference Start with the operations your question needs. TensorN/Tucker N-way array factorization Paired scalar transforms and reconstruction Trained per-series/window layers with staleness metadata Documented product scope, reviewed 17 September 2026. The underlying build and deployment must be qualified for your application. Tucker arrays and scalar multiscale layers are separate capabilities. The current evidence does not establish general 2-D/3-D MERA, lossless compression for every dataset or local I/O for every partial reconstruction. Connected native models Keep the next question within reach. 8DB’s tensor and multiscale work includes N-way factorization as well as a distinct scalar path with reconstruction and trained time-series layers. These representations let the application ask different questions of the same source information. [Diagram: time series representation: conceptual diagram] Time series Keep the meaning in every observation. Observe · window · analyze Explore model ↗ (https://8braid.com/modalities/time-series) [Diagram: lattices representation: conceptual diagram] Lattices Preserve what can be compared. Order · join · meet Explore model ↗ (https://8braid.com/modalities/lattices) [Diagram: graphs representation: conceptual diagram] Graphs Follow the relationship to a better answer. Connect · traverse · follow Explore model ↗ (https://8braid.com/modalities/graphs) These connections show the models involved in the application design. Compare the supported combination and full workflow with a realistic alternative. Input → usable value / interactive design example Examine a sensor change at several resolutions 0 1 Receive observations → 0 2 Preserve typed values → 0 3 Derive a projection → 0 4 Inspect detail → 0 5 Return the view → [Diagram: time series representation: conceptual diagram] Step 1 / Time series Receive source observations with time and units Measured points, an interval bound and a visible gap retain different meanings along the same timeline. Next: Preserve typed values Conceptual application workflow. Qualify the supported operations, external tools, protection and persistence for the complete path. These diagrams are explanatory, not measured results. Read the complete workflow and comparison scope Receive source observations with time and units Preserve their typed values and version Derive the supported multiscale projection Inspect coarse behaviour and retained detail/staleness Return a scale-aware view linked to source observations Compare reconstruction error, retained information, stale-state behaviour and the complete path. A compact approximation should not be compared with full-fidelity raw data as though the outputs were identical. Compare a realistic alternative. Xarray provides labelled multidimensional arrays. It is an analysis-library alternative within a larger stack; compare axes, operations and persistence/context responsibilities explicitly. Xarray overview (https://8braid.com/modalities/https://docs.xarray.dev/en/stable/getting-started-guide/why-xarray) Compare equivalent outputs and required guarantees, including the simplest single-product alternative where one exists. A specialist may remain the better fit when its operators, ecosystem or operational maturity are decisive. Combining native models becomes valuable when your actual question crosses them. Performance & resources Measure the work that delivers the answer. Declared reconstruction error/fidelity; factorization and reconstruction time; stale/update handling; memory/disk; useful query latency at each scale. This draft does not assign a new speed ratio or score to this modality. Existing measurements and source leads are retained in the evidence programme; a qualified public result must identify the particular operation and path tested. Explore the published benchmark evidence (https://8braid.com/benchmarks) What belongs inside the timer and resource budget? For input-to-value testing, declare the input state and the consumer-ready output first. Include parsing, derivation, storage/protection, index readiness, querying, result assembly and delivery as applicable. Identify amortized setup and any work deferred past the finish line. Report database-stage and full-job results separately. Do not add unrelated medians or p95 values, or multiply component speedups into a claimed workflow result. Energy needs measured joules; reduced handoffs alone do not establish a saving. Choose the path that fits your deployment. Verify the supported API/SDK, data format, persistence and indexing behaviour for the release you plan to use. Match the operation-by-modality protection profile and device qualification to the workload. Post-quantum protection (https://8braid.com/pqc) · Plan a workload evaluation (https://8braid.com/build) Related articles Take the next step into the thinking. Mechanism & application 8DB | What Can Geometry Reveal That Data Points Alone Cannot? (https://8braid.com/journal/one-mathematical-object-six-views-what-a-table-of-samples-can-never-ask) Explore the ideas and implementation context behind this capability. Mechanism & application 8DB | Native Multimodal Data: Connect the Dots Across Graphs, Maps and Time Series (https://8braid.com/journal/modality-is-a-projection) Explore the ideas and implementation context behind this capability. Reference / Reviewed 17 September 2026 Readable, shareable and traceable. This reference explains 8DB’s public modality description (https://8braid.com/modalities#explore) with an explicit workload and selection criteria. It is a documentary review, not fresh execution of every operator or platform. Public sources: 8DB modality scope (https://8braid.com/modalities) ; Xarray overview (https://8braid.com/modalities/https://docs.xarray.dev/en/stable/getting-started-guide/why-xarray) ; the related articles above. Internal source leads and outstanding release checks remain in the editorial record. Download this reference (https://8braid.com/modality-reference/tensors.md) Evaluate the question your application needs to answer. Bring the data shapes, input state, expected output and the systems you use today. Define a comparison around that complete job. Discuss your workload (https://8braid.com/build)