{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp09_archetype_breadth150_20260804","cell_id":"predictive_residual_processing__accounting_auditing","arm":"BREADTH_PROBE_ONE_SHOT","candidate_id":"predictive_residual_processing__accounting_auditing__P1","proposal_index":1,"version":0,"title":"Versioned Residual Review for Recurring Close Entries","problem":"During a period-end close, reviewers repeatedly inspect high-volume recurring journal entries whose accounts, amounts, timing, preparers, approvals, and supporting-document patterns are usually stable. Material deviations can remain buried in this predictable workload, while simple exception rules can miss changed relationships, stale baselines, or small but consequential departures.","actors":["Journal-entry preparers","Entity controller","Close reviewers","Internal auditors","External auditors","Predictive-model owner"],"observable_state":"For each in-scope recurring entry, the ledger contains the complete posted entry and provenance, while a versioned model produces an expected structured entry comprising account combination, signed amount range, posting time, preparer and approver pattern, reversal behavior, and expected supporting-evidence state. The observable residual is the field-by-field difference between that expectation and the actual entry, accompanied by uncertainty, consequence class, model version, and missingness or channel-health status.","consequence":"Reviewer attention is consumed by predictable entries, potentially delaying investigation of entries that depart from established close behavior; an ungoverned exception filter could also normalize a developing misstatement or conceal evidence outside its modeled fields.","affected_objective":"Preserve complete accounting records while allocating close-review attention toward decision-relevant deviations and maintaining the ability to detect model drift and suppressed blind spots.","intervention":"Run a shadow predictive-review layer over a narrowly defined population of recurring close entries. Before review, a frozen, versioned model predicts each entry's structured state. A comparator creates signed and categorical residuals, and a precision-weighted gate assembles review packets only for residuals crossing consequence- and uncertainty-specific thresholds. Each packet includes the predicted baseline, actual fields, residuals, provenance, and model version. The full ledger remains authoritative and unchanged. Random raw-entry samples, periodic full-population reconciliation, model-version checks, and explicit fallback triggers independently test what the residual channel does not surface. Validated residuals enter a replay buffer for controlled, separately approved model revision after the close.","structural_mapping":[{"archetype_element":"Prediction Target Definition","domain_realization":"The complete structured state of an approved recurring journal entry at a specified close stage."},{"archetype_element":"Generative Model State","domain_realization":"A versioned model of expected account combinations, signed values, timing, workflow identities, reversals, and supporting-evidence states for each recurring-entry class."},{"archetype_element":"Prediction Comparator and Prediction-Error Signal","domain_realization":"A field-level comparison that emits numerical, categorical, timing, workflow, and missing-document residuals rather than a single opaque risk score."},{"archetype_element":"Precision-Weighting Rule","domain_realization":"Routing weights combine residual size with model uncertainty, account sensitivity, posting authority, evidence reliability, and consequence class."},{"archetype_element":"Residual Propagation Channel","domain_realization":"A reviewer queue carries selected residual packets while predictable content is reconstructed from the displayed model baseline; the complete source entry remains available on demand."},{"archetype_element":"Model-State Synchronization Rule","domain_realization":"Every prediction and review packet carries a model-version checksum, and packets are rejected if the review interface and comparator use incompatible versions."},{"archetype_element":"Raw-Signal Audit Sample","domain_realization":"A random sample plus a risk-stratified sample of entries that produced no routed residual receives full conventional review."},{"archetype_element":"Decompression Trigger and Fallback to On-Demand Path","domain_realization":"Missing heartbeats, version mismatch, sustained residual shift, excessive uncertainty, reconstruction disagreement, or scope change suspends residual routing and restores full-population review."},{"archetype_element":"Safety-Critical Bypass Rule","domain_realization":"Manual top-side entries, related-party entries, new account combinations, privileged overrides, and entries requiring legally complete review bypass suppression and appear in full."},{"archetype_element":"Update Rule","domain_realization":"Validated discrepancies are stored during the close but alter the model only after controller-approved review, backtesting, and versioned release."}],"mechanism_mapping":[{"mechanism_slug":"event_triggered_residual_reporting","role":"Routes a journal entry to the attention queue when its consequence- and uncertainty-weighted residual crosses a declared threshold.","counterfactual_removal":"Without event-triggered routing, reviewers would still receive the full predictable population, so the intervention would not reallocate scarce attention."},{"mechanism_slug":"precision_weighted_error_gate","role":"Prevents residual magnitude alone from determining priority by incorporating uncertainty, source reliability, account sensitivity, and potential consequence.","counterfactual_removal":"Without precision weighting, noisy large differences could crowd out small reliable differences in sensitive fields."},{"mechanism_slug":"shadow_raw_channel_sampling","role":"Selects entries independently of the predictive model for complete conventional review and reconstruction comparison.","counterfactual_removal":"Without raw sampling, the model could suppress systematic omissions while appearing accurate from its own residual stream."},{"mechanism_slug":"model_version_checksum_handshake","role":"Ensures that predictions, residuals, and reviewer reconstructions refer to the same approved model state.","counterfactual_removal":"Without version checks, identical residual values could be interpreted against different baselines and yield incompatible conclusions."},{"mechanism_slug":"raw_signal_fallback_switch","role":"Returns the in-scope population to full conventional review when validity, synchronization, drift, or reconstruction conditions fail.","counterfactual_removal":"Without fallback, a stale or broken predictor could continue defining entries as normal during a regime change."},{"mechanism_slug":"prediction_error_replay_buffer","role":"Retains validated residuals and their contexts for post-close calibration and governed model revision.","counterfactual_removal":"Without replay, deviations could trigger one-time investigations but would not reliably correct the predictor that generated them."}],"causal_chain":["Recurring close-entry classes provide a bounded stream with an explicit, testable expected structure.","A frozen versioned model predicts the structured state of every in-scope entry before reviewer triage.","The comparator computes actual-minus-expected field residuals and distinguishes missing observation from zero residual.","Consequence and uncertainty weights select which residual packets enter the scarce reviewer-attention channel.","Reviewers reconstruct the entry context from the displayed baseline plus residual and can request the complete authoritative record.","Bypass classes, shadow raw samples, and full-population reconciliation expose consequential entries the model or gate might suppress.","Drift, synchronization, or reconstruction failures activate full review rather than allowing silence to count as confirmation.","Validated residuals support bounded post-close model updates without letting immediate reviewer actions contaminate the active model."],"baseline":"The existing close review remains unchanged: reviewers use full ledger listings, fixed materiality or exception rules, established sampling, and manual supporting-document inspection. In the first test, the residual layer operates only in shadow and does not remove entries from required procedures.","nearest_rivals":["Risk-based audit sampling, which selects subsets using assessed risk but does not reconstruct each omitted entry from a synchronized expected-state model plus residual.","Fixed journal-entry exception reports, which flag declared conditions but generally lack model-relative structured residuals, uncertainty weighting, drift monitoring, raw-channel audits, and version handshakes.","Journal-entry anomaly scoring, which ranks unusual records but may output a score rather than a reconstructive field residual tied to an explicit model version and fallback regime.","Variance analysis against budgets or prior periods, which compares aggregates but does not maintain an entry-level predictive representation and residual review channel."],"remaining_contrastive_claim":"The candidate's distinguishing testable feature is not merely prioritizing unusual journal entries: it makes an explicit versioned prediction of each entry's structured state, presents the field residual as the review message, verifies reconstruction against independently sampled full entries, and disables residual-only triage when synchronization, drift, or fidelity conditions fail.","authority_safety":{"decision_authority":"The entity controller owns operational close procedures; internal or external audit leadership retains authority over audit scope and required evidence. The model owner may prepare versions but cannot independently reduce review coverage or alter bypass classes.","authorized_first_step":"Run a read-only shadow evaluation on already authorized ledger extracts for one entity, one close cycle, and two recurring-entry classes, with no change to postings, approvals, audit scope, or required reviews.","excluded_actions":["Automatically post, reverse, approve, reject, or modify journal entries.","Remove any entry from legally, professionally, or policy-required review.","Update the active model during the close from reviewer actions or unresolved residuals.","Use employee atypicality alone as evidence of misconduct.","Suppress manual top-side, related-party, privileged-override, new-account, or other designated bypass entries.","Replace the authoritative ledger or supporting-document archive with residual packets."],"halt_rollback":"Stop the shadow run and discard its triage outputs if version checks fail, required fields are missing, raw-sample reconstruction exceeds the predeclared error budget, bypass entries are omitted, residual volume exceeds the attention budget, or the population definition changes. Because the pilot is read-only, rollback consists of reverting entirely to the unchanged conventional review record and preserving logs for diagnosis."},"negative_tests":{"strongest_counterevidence":"The residual layer fails to reduce review material after including model maintenance and audit controls, or full conventional review repeatedly identifies consequential issues among entries that the residual channel suppressed.","problem_falsifier":"Time-stamped baseline observation shows that recurring-entry review is not attention-constrained, that entries are insufficiently predictable within declared classes, or that consequential deviations are already surfaced promptly by existing controls.","intervention_falsifier":"Against the unchanged review, the shadow system misses a predeclared consequential deviation, violates bypass coverage, cannot reconstruct sampled entries within the declared decision-relevant tolerance, exhibits unresolved model-version mismatch, or produces no usable attention savings after control costs are counted.","risks":["A shared stale baseline could normalize a systematic misstatement.","Residual thresholds could suppress small but consequential changes.","Preparers could learn to shape entries so they resemble the predicted pattern.","Residual packets could expose sensitive atypical behavior more sharply than full listings.","Reviewers could lose baseline context or over-trust silence.","Model updates could learn effects caused by prior review interventions.","False residuals could shift attention away from other audit procedures.","Model maintenance, sampling, and reconciliation costs could exceed any released attention."]},"next_evidence_step":"For one completed close, select two pre-specified recurring-entry classes and replay them chronologically through a frozen model using only information available before each posting. Independently label the full population through the already required review, then compare the shadow residual queue with that record. Measure queue size, reviewer minutes needed to interpret packets, reconstruction disagreements in a predeclared random and risk-stratified raw sample, bypass coverage, version mismatches, residuals associated with already documented review findings, and total model-control effort. Do not alter audit conclusions or procedures; decide only whether a prospective, bounded shadow pilot is warranted.","prior_art_status":"UNSEARCHED","diversity_from_prior_proposals":"No other experiment candidates or proposals were inspected. This candidate is derived solely from the supplied predictive-residual archetype and the accounting-and-auditing domain card.","revision_record":{"parent_version":null,"progress_targets_addressed":["One-shot generation from the supplied archetype and domain record","Concrete accounting-and-auditing problem inference","Causal preservation of prediction, residual routing, synchronization, raw audit, controlled updating, and fallback"],"conceptual_changes":[],"operational_changes":[],"evidence_changes":[],"claim_changes":[]}}