{"schema_version":1,"assessment_id":"eoa_inverse_innovation_exp03_opportunity320_20260801","source_experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__data_science","archetype_slug":"invariant_mode_decomposition_design","domain_slug":"data_science","title":"Retrospective modal detection of coupled feedback-driven model drift","opportunity_summary":"Evaluate whether fitted transition modes can reveal consequential coupled drift across features, predictions, interventions, and delayed outcomes earlier or more reliably than marginal monitoring and generic multivariate alerts. The candidate is technically coherent and safely testable on historical data, but stable modes, incremental performance, control responsiveness, demand, and distinctiveness remain unverified.","adopter_authorizer":"The model-owning data-science team is the prospective adopter. Its accountable model owner may authorize retrospective analysis; existing domain, risk, and change-control authorities must approve any production monitoring or control change.","scores":{"meaningful_impact":{"score":4,"rationale":"Earlier detection of hidden degradation could protect calibration, subgroup performance, and downstream decisions. The stated consequence is important, although the frequency and magnitude of coupled-mode failures are unsupported."},"stakeholder_pull":{"score":3,"rationale":"Model owners, risk reviewers, and affected subgroups have plausible interests in earlier degradation detection, but the packet contains no interviews, incident demand, budget commitment, or evidence that stakeholders prefer this approach."},"incremental_advantage":{"score":3,"rationale":"Invariant growth directions and sensitivity-linked controls are meaningfully different from marginal checks and generic joint-deviation alerts. Whether they improve warning time, precision, or consequence reduction is explicitly untested."},"distinctiveness_plausibility":{"score":3,"rationale":"The composition of modal decomposition, intervention sensitivity, and residual, gap, and rotation governance is specific and coherent, but prior-art status is unsearched and world novelty is unmeasured."},"technical_implementability":{"score":3,"rationale":"A retrospective transition-operator analysis is feasible in principle using time-windowed production records. Short autocorrelated histories, delayed labels, changing versions, ill-conditioning, non-normal dynamics, and reflexive feedback could prevent a valid implementation."},"adoption_authority_feasibility":{"score":4,"rationale":"The candidate identifies the accountable model owner, separates analytical from production authority, and retains existing risk and change-control processes. Feasibility still depends on organizational access and approval not evidenced in the packet."},"evidence_readiness":{"score":4,"rationale":"The candidate supplies a bounded temporal validation design, explicit baseline and rival, preregistered validity gates, and separate problem and intervention falsifiers. Actual data completeness and the number of documented incidents are unknown."},"safety_net_benefit":{"score":4,"rationale":"Read-only evaluation, subgroup checks, residual and conditioning budgets, rotation limits, and regime-change halts provide useful safeguards against misleading modes. Monitoring-induced overconfidence and feedback effects remain possible."},"scalability":{"score":3,"rationale":"The analytical pattern could be reused across production prediction systems, but each pipeline would require a new state definition, version-consistent history, delay treatment, thresholds, subgroup review, and governance approval."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"50K_TO_250K","scope":"One retrospective, read-only study covering data assembly, temporal leakage review, operator fitting, preregistration, comparison with marginal and multivariate monitors, subgroup evaluation, and reporting.","confidence":"LOW","assumptions":["Versioned feature, score, intervention, and outcome records already exist.","One data-science team and limited domain and risk review are sufficient.","No new data collection or live deployment is required.","Historical outcomes can be aligned without extensive reconstruction."]},"initial_deployment_startup":{"band_2026_usd":"250K_TO_1M","scope":"Production-grade shadow-monitoring preparation, including reproducible pipelines, alert governance, version and regime tracking, audit logging, subgroup dashboards, validation, security review, and change-control documentation.","confidence":"LOW","assumptions":["The retrospective study passes its preregistered thresholds.","Deployment remains advisory and does not automatically control the model.","Existing observability and model-governance infrastructure can be extended.","Only one production system is initially included."]},"operational_launch":{"band_2026_usd":"250K_TO_1M","scope":"Controlled launch of advisory modal alerts for one production system, including parallel monitoring, operator training, incident procedures, acceptance testing, domain and risk approval, and post-launch evaluation.","confidence":"LOW","assumptions":["No automated production intervention is introduced.","Baseline and rival monitors continue operating as safety comparators.","Existing authorities can review the launch without a new regulatory program.","A rollback to ordinary monitoring is technically available."]},"annual_recurring":{"band_2026_usd":"50K_TO_250K","scope":"Ongoing data-quality checks, refitting and temporal validation, threshold and subgroup review, regime-change handling, alert triage, audit support, and periodic comparison against existing monitors for one system.","confidence":"LOW","assumptions":["Alert volume is moderate.","The system does not require continuous bespoke research.","Major pipeline or policy changes trigger revalidation rather than routine maintenance.","Software and compute needs are modest relative to labor and governance effort."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"YES","reason":"The candidate defines observable coupled movement, affected objectives, operational consequences, historical records, and an incident-level falsifier, making the problem testable without assuming its prevalence."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"The model-owning data-science team and accountable model owner are identified, with production authority explicitly assigned to existing domain, risk, and change-control bodies."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The proposal claims that stable modal growth provides useful precursor or control-leverage information beyond univariate monitoring and a generic multivariate change-point or anomaly detector; honest temporal validation can test this."},"bounded_next_evidence_step":{"status":"YES","reason":"A single-regime, retrospective, read-only temporal study is authorized and includes preregistered stability, gap, residual, consequence, and comparative-performance thresholds."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"The first step changes no production behavior, excludes harmful excitation and automatic control, preserves subgroup metrics, and specifies halt conditions; later production action remains separately authorized."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"The candidate describes analytical and governance components but provides no data-readiness, staffing, infrastructure, compliance, system-count, or integration evidence sufficient to validate implementation cost."}},"blocking_evidence":["Whether documented consequential incidents contain a repeatable coupled precursor rather than only abrupt shocks, single-variable failures, or label-delay artifacts.","Whether a version-consistent window produces a well-conditioned operator with acceptable held-out residuals, supported spectral gaps, and stable mode identity.","Whether modal alerts outperform marginal monitoring and a generic multivariate detector on preregistered warning-time and false-alert criteria.","Whether aggregate reconstruction and alert performance remain acceptable for relevant subgroups.","Whether sensitivity-selected feasible controls reduce held-out modal gain or consequence relative to baseline or rival controls.","Whether historical records, incident labels, intervention histories, and delayed outcomes are sufficiently complete and leakage-free.","Whether external prior-art research supports any distinctiveness claim."],"next_evidence_step":"With one model-owning partner, run the authorized read-only temporal holdout study on one unchanged deployment regime: fit modes only on earlier windows, compare later-window warning time and false alerts against univariate monitoring and a generic multivariate detector, and reject the candidate if no stable well-conditioned mode precedes consequential degradation or if it provides no comparative advantage within uncertainty.","research_questions":["How many documented degradation incidents exhibit coupled trajectories before harm, and how many are explained by abrupt shocks or single variables?","Do held-out residual, conditioning, spectral-gap, and mode-rotation results satisfy preregistered tolerances across reasonable window choices?","Does the modal method improve warning time, precision, or subgroup-sensitive detection relative to both stated comparators?","Can delayed outcomes be aligned without leakage, and are conclusions robust to autocorrelation and non-normal transient growth?","Do sensitivity-mapped controls reduce projected gain or consequences under retrospective or safe offline evaluation?","What prior methods already combine dynamic-mode estimation, drift detection, control sensitivity, and governance gates in deployed-model monitoring?","What data engineering, review workload, and false-alert burden would an advisory deployment impose?","Will model owners and risk authorities act on modal alerts when baseline metrics remain acceptable?"] ,"recommendation":"PARTNERED_RESEARCH","uncertainty_constraints":["Closed-book assessment provides no evidence of problem prevalence, market size, stakeholder demand, realized impact, or external distinctiveness.","Transition modes may be descriptive rather than causal, especially under retraining, intervention, and user adaptation.","Cost bands are resource-equivalent estimates with low confidence because organizational scale, data readiness, compliance requirements, and infrastructure are unspecified.","The existence of time-windowed records does not establish that they are complete, version-consistent, sufficiently long, or leakage-free.","Results from one deployment regime may not transfer across models, policies, populations, or feedback mechanisms.","Production control is outside the authorized first step and cannot be inferred from successful retrospective detection."],"closed_book_prior_art_boundary":"Prior-art status is explicitly unsearched. This assessment makes no claim about novelty, prevalence, competitive availability, market adoption, or whether equivalent modal drift and control methods already exist."}