{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp09_archetype_breadth150_20260804","cell_id":"predictive_residual_processing__computer_science","arm":"BREADTH_PROBE_ONE_SHOT","candidate_id":"predictive_residual_processing__computer_science__P1","proposal_index":1,"version":0,"title":"Reconstructable Residual Tracing for Microservice Requests","problem":"A microservice platform exports complete distributed traces for high-volume request classes whose service path and span outcomes are usually repetitive. Trace-storage and analyst-attention budgets are consumed by expected spans, while missing calls, novel dependencies, abnormal outcomes, and timing shifts can be dropped by quotas or remain difficult to isolate.","actors":["Application runtime instrumentation agents","Trace-model service owner","Central trace collector","Site reliability engineer or incident investigator","Security and compliance owner"],"observable_state":"For a defined endpoint and deployment version, successive traces repeatedly contain the same ordered service dependencies, successful span outcomes, and similar timing bands; the collector approaches its ingestion or retention budget, and investigators must search many structurally equivalent traces to find the few diagnostically material differences.","consequence":"The platform must discard, sample, or shorten traces without knowing which omitted details will matter, and investigators may receive late or incomplete evidence about an emerging dependency, missing span, changed error state, or latency source.","affected_objective":"Preserve decision-relevant trace observability and incident-diagnostic evidence within bounded collection, storage, and human-attention capacity.","intervention":"For each eligible request class and deployment version, maintain a versioned generative model of the expected span graph, outcome categories, and coarse timing bands. Runtime agents compare each observed trace with that expectation and export a structured residual containing unexpected or missing spans, changed edges or outcomes, and precision-weighted timing deviations, together with model version, trace identity, missingness state, and provenance. The collector reconstructs the decision-relevant trace as expected structure plus residual. Validated residuals enter a replay buffer for slower model updates. Independent random and risk-stratified full traces test reconstruction and blind spots; model mismatch, drift, missing heartbeats, deployment changes, and protected request classes force full-trace export.","structural_mapping":[{"archetype_element":"Prediction Target Definition","domain_realization":"The service dependency graph, span outcome category, and coarse timing band expected for one request class under a specified deployment configuration."},{"archetype_element":"Generative Model State","domain_realization":"A versioned expected-trace template with conditional branches, uncertainty bands, scope, owner, and validity window."},{"archetype_element":"Actual Behavior and Prediction Comparator","domain_realization":"The locally assembled observed trace is compared with the applicable template to identify added, absent, reordered, outcome-changed, and timing-deviant spans."},{"archetype_element":"Prediction-Error Signal and Residual Propagation Channel","domain_realization":"Instrumentation exports only the structured trace differences and reconstruction metadata through the constrained telemetry channel."},{"archetype_element":"Precision-Weighting Rule","domain_realization":"Residual priority combines deviation type, observation confidence, endpoint criticality, security class, and timing uncertainty rather than raw latency magnitude alone."},{"archetype_element":"Model-State Synchronization Rule","domain_realization":"Agent and collector exchange a model-version checksum; an unknown version, deployment transition, or update gap prevents residual interpretation and requests a full trace."},{"archetype_element":"Raw-Signal Audit Sample","domain_realization":"An independently selected set of complete traces is retained and compared with reconstructed traces, including samples from low-volume and protected request segments."},{"archetype_element":"Decompression Trigger and Safety-Critical Bypass Rule","domain_realization":"Drift, reconstruction failure, telemetry silence, novel service versions, security incidents, and designated audit request classes suspend suppression and export complete traces."}],"mechanism_mapping":[{"mechanism_slug":"event_triggered_residual_reporting","role":"Exports trace differences when consequence- and uncertainty-weighted gates are crossed instead of exporting every expected span.","counterfactual_removal":"Without it, the design returns to full-trace transmission or indiscriminate sampling and no longer allocates the channel to model-relative differences."},{"mechanism_slug":"model_version_checksum_handshake","role":"Ensures runtime agent and collector interpret each residual against the same expected-trace template.","counterfactual_removal":"Without it, a valid residual can be combined with the wrong deployment template and produce a misleading reconstruction."},{"mechanism_slug":"shadow_raw_channel_sampling","role":"Retains independently selected complete traces for detecting systematic omissions and testing diagnostic reconstruction.","counterfactual_removal":"Without it, the predictor can suppress evidence of its own blind spots and apparent agreement becomes self-confirming."},{"mechanism_slug":"prediction_error_replay_buffer","role":"Stores residuals with context for model review, calibration, and regression testing at a slower cadence than operational routing.","counterfactual_removal":"Without it, deviations may attract attention but cannot reliably correct or challenge the model that defined them."},{"mechanism_slug":"raw_signal_fallback_switch","role":"Restores full-trace export when model validity, synchronization, completeness, or safety conditions fail.","counterfactual_removal":"Without it, the system fails closed around an unreliable predictor precisely during regime changes or instrumentation faults."}],"causal_chain":["A request-class model predicts the decision-relevant trace structure before the observed trace is exported.","Runtime instrumentation compares observed spans with the version-matched prediction and explicitly distinguishes true agreement from missing observation or channel failure.","A precision-and-consequence gate selects structured residuals while protected classes bypass suppression.","The collector combines the compatible model and residual to reconstruct the dependency path, outcomes, and timing bands required by predefined diagnostic questions.","Expected trace content does not occupy the constrained channel or primary analyst view, while model-invalidating differences remain represented.","Shadow full traces expose reconstruction error and suppressed structure; sustained discrepancies trigger full export and model review.","Validated residuals update the expected-trace model separately from immediate incident actions, preserving a governed prediction-and-correction loop."],"baseline":"Export complete traces until an ingestion or retention quota is reached, then apply fixed-rate head sampling, tail sampling based on completed-trace attributes, or blanket retention limits without a reconstructable model-relative representation.","nearest_rivals":["Head-based distributed-trace sampling that selects requests before outcomes are known","Tail-based sampling that retains traces matching error or latency rules","Fixed span filtering or attribute deletion","Trace deduplication and aggregate service maps","Anomaly scoring that flags unusual traces but does not encode them as reconstructable residuals"],"remaining_contrastive_claim":"The candidate is distinguished by treating a versioned expected trace as shared reconstructive state and exporting calibrated deviations as both the trace representation and a model-correction signal, with independent raw audits and forced decompression. Its contrast with sampling or anomaly scoring does not imply novelty or superior performance.","authority_safety":{"decision_authority":"The observability platform owner may define an offline pilot, but production activation and protected-class rules require approval from the responsible service owner and security or compliance owner.","authorized_first_step":"Run an offline replay on an access-controlled historical trace slice, generate residual packages, reconstruct decision-relevant traces, and compare them with the untouched originals; do not change live instrumentation or retention.","excluded_actions":["Automatic incident remediation based solely on a residual","Suppression of security, audit, billing, or other designated completeness-required traces","Deletion or weakening of existing retention obligations","Use of payload contents or personal attributes to train the predictor without existing authorization","Automatic promotion of a revised model into production"],"halt_rollback":"Stop the replay or any later pilot if model versions cannot be reconciled, missingness is mistaken for agreement, protected traces enter suppression, or reconstruction exceeds the declared diagnostic error budget. Rollback consists of disabling residual mode and returning to the existing full-trace and sampling path while preserving the original traces."},"negative_tests":{"strongest_counterevidence":"Complete traces within an eligible request class remain structurally diverse or change so frequently that residuals, model metadata, synchronization, audits, and fallback consume at least as much constrained capacity as the existing trace representation.","problem_falsifier":"The scoped collector is not constrained by trace ingestion, retention, or analyst-attention capacity, or expected trace content is not responsible for the observed diagnostic evidence loss.","intervention_falsifier":"On held-out full traces, model-plus-residual reconstruction cannot answer the predefined dependency, outcome, and timing-source questions within the declared tolerance, or raw audits reveal material structures that the residual gate systematically suppresses.","risks":["A shared incorrect template can hide the same dependency or instrumentation gap across many services.","Version desynchronization can yield plausible but false reconstructed traces.","Low-amplitude timing changes may be consequential despite falling inside average uncertainty bands.","Residuals may expose unusually identifying request behavior even when expected traces are suppressed.","Model updates may normalize a persistent fault and cease reporting it as surprising.","Residual export may omit baseline context needed for safe incident interpretation.","Added agent comparison work may increase application overhead or perturb trace timing.","Saved telemetry capacity may be filled by additional instrumentation rather than reserved for diagnostic fidelity."]},"next_evidence_step":"Select one request class spanning two fixed deployment versions and an access-controlled set of 500 previously captured complete traces. Freeze a simple expected-trace model using an initial subset; encode and reconstruct the remaining traces without altering production. Before inspection, define diagnostic questions, a consequence-weighted reconstruction tolerance, protected-span rules, and accounting for residual bytes, model distribution, audits, and fallback. Review every disagreement in a random full-trace sample plus all protected or high-consequence cases, then decide only whether a limited shadow-mode test is warranted.","prior_art_status":"UNSEARCHED","diversity_from_prior_proposals":"No comparison with other experiment candidates was performed under runtime isolation; this candidate is situated specifically in distributed-trace representation and diagnostic observability.","revision_record":{"parent_version":null,"progress_targets_addressed":["One-shot generation from the supplied archetype and computer-science domain card","Concrete problem, intervention, mappings, safeguards, falsifiers, and bounded evidence step"],"conceptual_changes":[],"operational_changes":[],"evidence_changes":[],"claim_changes":[]}}