Regime-Aware Semantic Windows

A preliminary scientific assessment proposal — Swiss university research discussion.

From field-derived engineering hypotheses to independent mathematical assessment and empirical validation.

1. Where this comes from — and who is opening it

Tegrity.AI is an applied research, engineering and pre-standardization initiative of The Integral Management Society, a Swiss non-profit association based in Geneva. We are currently reviewing, formalizing and generalizing prior field-deployed capabilities from private companies of the association.

JubAp.Net (México) / JubAp.EU (Estonia) is the original engineering lineage that created and operated several of the systems being reopened or studied, including xSeil, Phylons and related operational-intelligence artefacts. The broader engineering origin also includes earlier Nokia R&D systems practice, later carried into large-scale mission-critical environments.

Field lineage — selected systems and operational artefacts

That applied history is relevant as engineering provenance; it is not presented as mathematical validation of the new formulation.

Swiss and Swiss-headquartered engagements are also part of this provenance. The association and its engineering lineage have worked with specific Swiss or Switzerland-linked clients including SGS, Nestlé and Richemont. These references are included only to clarify field experience in demanding operational environments; they are not presented as endorsement, certification or mathematical validation.

The practical principle is not new for us: in real operations, machine learning and broader artificial intelligence, the relevant and efficient context is often not the last fixed period of time or all available history, but the part of history that still belongs to the same operating regime. Historical engineering work has tested this principle in safety-governance, prediction-support and compute-efficiency contexts; the current scientific step is to validate what can be generalised.

What is new is the current attempt to formalise that principle as a general, domain-agnostic mathematical object for the next generation of engineering: semantic windows.

2. What is being formalised now

The current formalization is theoretical. It is not only a retrospective generalization of historical systems. Those systems supplied operational evidence that the same structural problem appears across logistics, mobility, process intelligence and forecasting-support contexts. The present work makes a larger conceptual move: from domain-specific orchestration and operational control to a mathematical question about which part of the past still belongs to the current regime and how a wrong boundary contaminates downstream estimation.

For that reason, the formalization requires independent scientific assessment. The fact that related ideas have been useful in production does not establish the mathematical claim. The question for a university partner is whether the semantic-window formulation is internally coherent, experimentally testable, distinct from existing adaptive-window and change-point families, and sufficiently plausible to justify a formal validation programme.

3. What we want to test

We are seeking a Swiss university or research centre for a preliminary scientific assessment of the semantic-window approach. The objective is not to validate a full Early Warning System, nor to guarantee future commercial performance. The objective is narrower: to assess whether the semantic-window formulation has enough mathematical and experimental plausibility to justify a formal validation programme.

The method is intended to support three fenced uses. Each maps to one hypothesis in the current claim package (H1–H3), stated here with both the operational use and the test it must pass:

This public note summarizes H1–H3 but does not publish their full protocols. The controlled standalone notes contain the exact estimands, metrics, strong baselines, action-cost schedules, leakage controls and falsification rules. A separate public protocol article should expose those elements at claim level while withholding only implementation details that genuinely require confidentiality.

Why this is a distinct scientific question

The semantic window can be mistaken for a renamed change-point or adaptive-window problem — CUSUM, ADWIN, BOCPD, PELT — or for the physical early-warning family such as critical slowing down, bifurcation or attractor-reconstruction methods. The proposed distinction is that semantic-window selection is evaluated by downstream decision or estimation loss under a declared admissibility rule, rather than by boundary localization alone. This is a novelty hypothesis, not yet an established distinction: adaptive-window, changepoint, regime-switching and drift-aware methods can also alter downstream context. Incremental value must therefore be tested against matched implementations of those methods. Establishing whether this distinction is mathematically real, and where it adds value over those families, is itself part of the preliminary question.

4. Proposed preliminary assessment

The preliminary assessment would have two clearly separated parts.

Why this is a mathematical validation task

The first review is aimed at a mathematician, applied theorist, control/statistics researcher or formal-methods oriented scientist, not primarily at a model-benchmarking ML team. The immediate question is whether the object is well defined and non-circular: which observations remain admissible for a decision time, how a wrong boundary contaminates an estimator, and whether a causal, past-only procedure can approximate the ex post bases without leakage.

The technical work is expected to review finite time-indexed definitions, boundary and contamination identities, admissibility weights, bias–variance trade-offs, stability / false-reanchoring logic, and tractability of the proposed approximation. Typical objects include trailing-window averages, increments, admissibility weights a_t(s) ∈ [0,1], oracle-versus-live decompositions, and leakage-free causal maps.

The proposed first step is a bounded scientific scoping assessment: review of the core definitions and claim-status map, two or three technical discussions, and a memo identifying fatal ambiguities, missing assumptions, leakage risks and tractability questions. This step would not constitute mathematical validation; any validation claim would require a later agreed protocol and sufficient evidence. Deeper empirical benchmarking or software validation would be a later stage.

A.1 — Ex post assessment of the value of semantic windows

We will provide a function able to identify, ex post, candidate bases or boundaries of semantic windows — the anchors that define where a semantic window starts or changes. The function would be applied progressively over cumulative samples — period 1; period 1 + 2; period 1 + 2 + 3; and so on — to observe how the identified bases evolve as new information is added, and whether they show sufficient stability.

for n = 1 to N:                                  # cumulative samples: period 1; 1+2; 1+2+3; ...
    sample  = data[1 : n]
    bases_n = identify_semantic_bases(sample)      # ex post — uses the full sample up to n
    record(n, bases_n)

stability = assess_basis_stability(bases_1, bases_2, ..., bases_N) # assess whether the contextual bases remain stable as n grows

for each candidate base W in bases_N:
    series_fixed    = downstream_model.fit(fixed_window(W))
    series_semantic = downstream_model.fit(semantic_window(W))
    compare(series_fixed, series_semantic)          # stability around regime change, not raw accuracy

The main questions are:

• Do the ex post bases remain relatively stable as the sample expands?
• Before analysis, define “noise” and “internal coherence” with one primary metric and a small number of secondary metrics, including their direction of improvement and uncertainty analysis. Correct for repeated testing if multiple candidate metrics, windows or datasets are explored.
• Evaluate boundary changes against labels or adjudication criteria independent of the semantic selector. If labels are derived from the same statistic that selects the boundary, report only internal consistency, not detection validity.
• Do traditional predictive or estimation models show better stability, lower degradation, or improved performance on semantic windows than on fixed temporal windows?

Keep the downstream forecaster fixed within each paired comparison and give every method the same tuning budget. Context-selection baselines should include fixed and expanding windows, weighted/forgetting windows, ADWIN, CUSUM or another sequential detector, Bayesian Online Changepoint Detection, a regime-switching/HMM comparator, a soft-anchor or break-averaged comparator, and an offline changepoint oracle such as PELT where appropriate. Domain-standard strong forecasters should be included when the claim is prediction improvement; weak “basic ML” baselines are insufficient. The comparison keeps the downstream model as similar as possible; the main change is the segmentation method — fixed temporal windows versus ex post semantic windows.

This test deliberately uses future information to identify the bases. It therefore includes controlled data leakage in the segmentation, and its results must not be interpreted as real out-of-sample performance. Its function is to build an oracle benchmark: if the correct semantic-window bases were known, would they provide a more stable and useful representation for forecasting or estimation than fixed windows? The primary criterion is stability, especially around regime changes — not a punctual gain in accuracy, but evidence that semantic windows preserve a more consistent structure when the underlying behaviour of the series changes.

A.2 — Preliminary review of ex ante plausibility

The second part would be conducted under NDA. We would present the mathematical formulation and proposed algorithms for approximating or identifying those bases ex ante, using only information available up to each time point — past-only, leakage-free.

for cutoff, test in rolling_origin_splits(data, min_train, horizon):
    train, calibration = time_ordered_split(data[:cutoff])
    selector = fit_semantic_selector(train)
    base_t   = selector.select(calibration, past_only=True)
    model_t  = fit_downstream_model(data[base_t : cutoff])
    output_t = model_t.predict_or_decide(horizon)

    record(test, output_t, base_t,
           invalid_context_exposure(base_t, test),
           reanchor_cost(base_t), compute_cost(model_t))

stability = weighted_jaccard(admitted_history_sets)
boundary_stability = median_absolute_boundary_shift(bases)
compare_against = [fixed_rolling, expanding, ADWIN,
                   offline_change_point_oracle, regime_switching_model]
report_paired_uncertainty(metrics, compare_against)
# The final hold-out is evaluated once; it is never used to tune the selector.

At this stage we are not asking for a complete mathematical proof, a formal validation of the full algorithm, a guarantee that ex ante bases will match ex post bases exactly, or a commercial performance guarantee. We are asking for a preliminary scientific opinion on whether:

• the mathematical reasoning is internally consistent;
• the formulation avoids circularity;
• the variables can be computed without improper access to future information;
• the proposed approach could, under plausible assumptions, approximate the ex post semantic bases;
• the margin of error can be defined and managed;
• the problem appears mathematically and computationally tractable.

A favourable conclusion would not be “the algorithm works.” It would be narrower: under the identified assumptions, the formulation is mathematically plausible — with an error margin to be quantified by later formal validation.

Negative or inconclusive results would also be useful, because they may show that the formulation needs revision, that assumptions are too strong, that a circularity exists, or that more information is required.

5. Parallel and complementary validation tracks

The Swiss preliminary assessment is one track inside a wider but deliberately separated validation architecture. The separation is important: each reviewer is asked to assess a bounded object, not to certify the full platform, the historical systems, the quantum track, the commercial product or the governance extension at once.

Track A — QAVA/UV: historical computational structures and quantum-inspired relevance

A separate written research agreement is already in place with QAVA/UV — Quantum Algorithms and Applications at the Universitat de València. That track reviews the original system lineage and xSeil/Phylons-derived computational structures from the perspective of quantum, quantum-inspired or hybrid computational approaches, including whether the historical problem structures contain meaningful optimisation questions for that research line.

The earlier practical systems used primitive but still related versions of semantic-window reasoning: context boundaries, admissible histories, candidate bases, recombination of operational states and selective use of past information. They are not the same as the current formalisation, and they do not validate it, but they can reveal whether the older computational structures already contained tractable or hard optimisation patterns that matter for deployment.

Informs: implementation strategy and computational effort.   Does not validate: the Swiss mathematical validity claim.

Track B — Structural Awareness Foundation and Regime-Awareness Semantic-Windows Bridge

The Swiss preliminary assessment proposed here is the mathematical bridge track. It does not ask the same reviewers to assess quantum advantage. It asks whether the attached foundational and semantic-window materials form a coherent, scoped and testable chain from formalization to regime-aware validation.

The exact materials to be reviewed are the supplied Swiss package: The Given Universe paper, its dialogue companion, its field-notes companion, and the standalone H1/H2/H3 notes. The public Structural Awareness Program page and the Estonia RUP project brief are application-context anchors, not substitutes for the mathematical review. The mathematical core to be checked is the formalization-event layer: world-to-record maps, projection loss, factorization requirements, equivalence relations induced by representation, the distinction between contingent representation loss and scoped impossibility claims, and the access rule that internal validity cannot certify world-fidelity from below.

The connection to Estonia is explicit. The Estonia RUP / Cost of Clarity track studies whether, before an initiative is approved, the cost and risk of producing the minimal required information can be estimated. Track B supplies the formal plausibility bridge: when the organisational map or record universe lacks distinctions that decisions require, context validity degrades and the cost of recovering or declaring the missing information becomes a measurable risk object.

Would assess: whether the proposed mathematical bridge between formalization, semantic windows and structural awareness is coherent, non-circular and testable, while separating definitions, conditional implications and empirical hypotheses.   Would not establish: operational or commercial performance.

Track C — Swiss bridge paper / Innosuisse vehicle: platform extension to governance and architecture risk

A separate bridge note is being prepared to connect the semantic-window and regime-awareness work with a broader Structural Awareness measurement layer. Inside a possible Swiss / Innosuisse vehicle, this bridge would not replace the mathematical assessment; it would sit as a bounded work package explaining how the same context-validity object can support two market-facing lines: operational regime awareness in running information systems, and governance / architecture structural awareness in transformation contexts.

The bridge matters commercially because it allows the solution to be assessed as a platform, not only as a time-series or forecasting add-on. If the same notion of valid context can be measured in operational chains and in organisational maps — ownership, dependency, capability, data-lineage and process-model completeness — the project becomes more deployable across information systems, architecture, AI-governance and transformation-risk use cases, while keeping the core H1/H2/H3 validation fenced.

Opens: an Innosuisse / product path and platform framing.   Does not replace: the mathematical assessment.

Track D — TalTech practical exploration: regime change, missing information and enterprise-architecture cascades

The RUP proposal submitted to Enterprise Estonia / EIS includes a TalTech-supported practical track to explore whether regime awareness and regime change can help predict cascade effects caused by missing or degraded information in projects. The target problem is enterprise architecture and transformation governance: when incomplete ownership, hidden dependencies, weak process maps, missing capability coverage or human reconciliation work make a project structurally unsafe before failure is visible.

This track is practical and organisational rather than a proof of the semantic-window mathematics. Its value is to test whether the wider Structural Awareness thesis can be operationalised in architecture and governance settings, where the key signal is not only a numerical time-series break but the loss of sufficient information for safe decision-making. It may later supply product requirements, case material or deployment hypotheses.

Tests: organisational measurement feasibility.   Does not prove: the semantic-window mathematics.

6. Expected output of this preliminary step

The expected output is not a certification. It is a short scientific opinion or technical memo identifying which elements appear mathematically sound, which assumptions are critical, whether any circularity or leakage risk is present, and whether the ex post evidence justifies a later formal validation of the ex ante method. For Track B specifically, the memo should map the attached Given Universe materials and H1/H2/H3 notes to the Structural Awareness / Cost of Clarity / Human Intelligence Debt application context, while keeping proof, plausibility, empirical evidence and commercial claims separated.

The preliminary assessment should answer two questions:

1. Is it worth finding these windows? Do ex post semantic windows produce more stable, less noisy and more useful forecasting contexts than fixed temporal windows?
2. Is it plausible to find them ex ante? Does the mathematical formulation offer a reasonable, non-circular and potentially tractable path to approximate those bases without future information?

A preliminary favourable answer to both questions would justify moving toward a formal validation stage.

7. Next steps

The next steps are deliberately left open at this stage. Depending on the preliminary review, they may include a formal mathematical validation; a controlled empirical protocol; a reproducible reference implementation; open or synthetic datasets; joint publications; a funded research or innovation project; or a larger consortium work package. At this stage, the discussion is technical and scientific. The proposed work will proceed according to the research scope, institutional arrangements and funding decision applicable to the RUP submission.

Legal and institutional information

The Integral Management Society / IMSV — Geneva, Switzerland. Swiss non-profit association and steward of the Tegrity.AI applied research, engineering and pre-standardization initiative. Public legal notice: Code d’entreprise 310376, État de Genève, Suisse; current address MSA — 41A route des Jeunes, 1227 Carouge, Geneva, Switzerland. tegrity.ai/legal-notice

OÜ JUBAP / JubAp.EU — Estonia. Legal and commercial entity for JubAp.EU. Registry code 16941644 · Akadeemia tee 42-11, Tallinn, Estonia · info@jubap.eu · jubap.eu/legal-notice

JubAp.Net — original engineering lineage. Original systems and case-study environment for several historical artefacts, including xSeil and Phylons. jubap.net

This document is a capability and research-discussion note, not a funding commitment, offer, commercial claim or independent certification. Entity roles are functional; project participation and remuneration remain project-specific.

Public references

• Structural Awareness Program / Start Here — tegrity.ai/structural-awareness-program
• Tegrity.AI legal notice — tegrity.ai/legal-notice
• Tegrity.AI — tegrity.ai
• Systems Orchestration and Regime Awareness Capabilities — jubap.eu/orchestration-capabilities

TEGRITY.AI · applied research, engineering and pre-standardization initiative of The Integral Management Society · Geneva, Switzerland
Regime-Aware Systems series · Field note · Proved, conditional and empirical claims kept separate throughout.