{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp06_four_proposal_generalization60_20260803","cell_id":"predictive_residual_processing__chemistry_materials","arm":"COMPLETE_PROPOSAL_PORTFOLIO","candidate_id":"residual_governed_materials_discovery_queue","proposal_index":3,"version":0,"title":"Residual-Governed Characterization Queue for Materials Discovery","problem":"A materials-discovery campaign can produce a characterization package for every candidate composition and process condition. When scientists review each package equivalently, results that largely agree with the campaign model consume the same attention as results that contradict predicted phase formation, structure, or properties. Fixed feature filters can reduce the queue but may hide small, reliable contradictions or unfamiliar signals that do not match predefined features.","actors":["Materials discovery scientist","Synthesis chemist","Characterization specialist","Campaign model owner","Experiment-planning committee","Laboratory safety owner","Data steward"],"observable_state":"For each candidate, the system records intended and measured composition, synthesis conditions, specimen and instrument provenance, quality controls, complete raw characterization files, and standardized phase, structure, and property outputs. Before results are released, a versioned campaign model freezes predictions and uncertainty for those outputs. The system then records signed or structured prediction residuals, precision and consequence weights, routing decisions, reconstruction checks, model checksum, missingness, audit selection, follow-up ownership, and any full-review fallback.","consequence":"Scientist attention and follow-up measurements can be allocated to redundant confirmations while model-breaking evidence waits in an undifferentiated queue. Conversely, reviewing only predefined features or anomaly labels can remove the expected context, obscure direction and uncertainty, and reinforce the model's existing blind spots.","affected_objective":"Allocate bounded scientist-review and follow-up-characterization capacity toward informative model discrepancies while retaining reconstructable standardized results, accessible raw evidence, protected safety information, and governed model revision.","intervention":"Create a residual-first discovery queue between characterization and experiment planning. For every candidate, freeze a scoped model prediction of standardized phase, structure, and property outputs before revealing the measurements. Compare the actual standardized outputs with the prediction and encode their signed numerical differences, categorical disagreements, unpredicted peaks or phases, missingness, uncertainty, and provenance. A precision-weighted gate routes reliable or consequential residuals to named reviewers and proposed follow-up actions; expected results remain reconstructable from the compatible prediction plus residual and available in full on demand rather than occupying the primary review queue. Reviewed residuals update a Bayesian campaign model at a slower governed cadence and may influence the next planning round. Random candidates and risk-stratified classes receive full-package review regardless of residual score. New material classes, incompatible model versions, invalid measurements, sustained residual structure, excessive reconstruction error, safety flags, or model staleness suspend residual-only triage and return the affected scope to full-package review.","structural_mapping":[{"archetype_element":"Explicit prediction target and decision boundary","domain_realization":"The target is a preregistered standardized output vector for each candidate, including declared phase assignments, structural descriptors, measured properties, uncertainty, and missingness; the downstream decision is whether to accept routine characterization, request review, replicate, broaden characterization, or reconsider the campaign model."},{"archetype_element":"Versioned generative model with scope and horizon","domain_realization":"A campaign model predicts one candidate's characterization outcomes from intended composition, measured composition, synthesis history, and instrument context, within specified chemical families, process ranges, and assay definitions."},{"archetype_element":"Prediction produced before observation","domain_realization":"Predictions and uncertainty are timestamped and frozen before the candidate's standardized characterization results are revealed to the model or reviewers."},{"archetype_element":"Declared prediction comparator","domain_realization":"The comparator preserves signed property error, categorical phase disagreement, structured peak or feature mismatch, uncertainty overlap, assay quality, and explicit missing observations."},{"archetype_element":"Precision- and consequence-weighted residual","domain_realization":"A small discrepancy from a reliable assay or in a protected decision region may outrank a larger discrepancy from a noisy or invalid measurement."},{"archetype_element":"Residual propagation through a scarce channel","domain_realization":"The primary scientist-review and follow-up queue receives calibrated discrepancies with expected context, while model-consistent packages remain accessible on demand and contribute only bounded summary traffic."},{"archetype_element":"Reconstructable full standardized result","domain_realization":"A compatible receiver reconstructs numerical outputs from prediction plus correction and retrieves complete categorical, unpredicted-feature, or raw-instrument records whenever additive reconstruction is insufficient."},{"archetype_element":"Residual-driven model update","domain_realization":"Validated discrepancies revise parameter beliefs, model scope, likelihood assumptions, or campaign hypotheses only during an attributable review cycle separate from live routing."},{"archetype_element":"Synchronization and provenance","domain_realization":"Every residual names the prediction version, preprocessing pipeline, assay definition, specimen, instrument, timestamp, and transformation history; incompatible versions cannot be combined."},{"archetype_element":"Independent raw-state audit","domain_realization":"A random baseline sample and risk-stratified candidates receive complete review using raw characterization packages independently of the production residual score."},{"archetype_element":"Drift detection and decompression","domain_realization":"Structured residuals, calibration failure, model expiry, new chemical scope, measurement-quality failure, or audit disagreement restore full-package review for the affected class."},{"archetype_element":"Explicit attention and error budgets","domain_realization":"The campaign predeclares review capacity, follow-up capacity, tolerable reconstruction loss, protected residual classes, cumulative suppressed-error limits, and the cost of modeling, auditing, and fallback."}],"mechanism_mapping":[{"mechanism_slug":"event_triggered_residual_reporting","role":"Places a candidate in the primary review queue when its precision-weighted characterization residual crosses a predeclared information or consequence threshold.","counterfactual_removal":"Without event-triggered reporting, the intervention would not redirect bounded review capacity away from predictable characterization packages."},{"mechanism_slug":"precision_weighted_error_gate","role":"Combines discrepancy, assay uncertainty, source quality, consequence, novelty, and review cost while retaining suppressed residuals for audit.","counterfactual_removal":"Without precision weighting, noisy large discrepancies could displace small but reliable contradictions and protected material classes."},{"mechanism_slug":"confidence_threshold_table","role":"Defines versioned pass, review, replicate, broaden-characterization, and fallback actions by confidence, residual class, and consequence tier.","counterfactual_removal":"Without an explicit table, queue volume or reviewer preference could silently become the routing policy."},{"mechanism_slug":"bayesian_model_update","role":"Uses reviewed outcomes and their uncertainty to revise campaign-model beliefs after the operational routing cycle.","counterfactual_removal":"Without the update mechanism, residuals would attract attention but would not correct the expectations that generated the queue."},{"mechanism_slug":"anomaly_detection_model","role":"Identifies residual shapes, categorical conflicts, and feature combinations that are improbable under the scoped campaign model.","counterfactual_removal":"Without structured anomaly screening, informative departures not captured by a single numerical threshold could remain buried."},{"mechanism_slug":"prediction_error_review","role":"Requires reviewers to reconstruct why a material result differed and classify the cause as model, specimen, synthesis, measurement, preprocessing, or scope error before authorizing change.","counterfactual_removal":"Without review, automated updating could absorb contamination, instrument faults, or data-processing errors as chemical evidence."},{"mechanism_slug":"prediction_error_replay_buffer","role":"Stores reviewed residuals together with full characterization context and model provenance for calibration, regression testing, and later hypothesis review.","counterfactual_removal":"Without replay, the campaign could not test whether later models handle earlier contradictions without reopening every package manually."},{"mechanism_slug":"shadow_raw_channel_sampling","role":"Sends random and risk-stratified candidates through complete independent review and compares that judgment with residual-only routing and reconstruction.","counterfactual_removal":"Without raw-package audits, the model would determine which evidence is examined for failures of that same model."},{"mechanism_slug":"forecast_backtesting","role":"Tests whether the predictor's claimed scope and uncertainty were supported on strictly withheld campaign segments before residual triage is authorized.","counterfactual_removal":"Without bounded out-of-sample testing, historical fit could be mistaken for permission to suppress routine packages."},{"mechanism_slug":"model_version_checksum_handshake","role":"Ensures the laboratory encoder and planning-side receiver interpret every residual against the same campaign model, assay definitions, and preprocessing state.","counterfactual_removal":"Without version gating, a valid residual could be reconstructed against a different prediction and change the apparent material result."},{"mechanism_slug":"model_drift_monitoring","role":"Tracks residual distributions, calibration, assay changes, chemical-scope changes, and model age to detect when predictive triage is no longer justified.","counterfactual_removal":"Without drift monitoring, gradual changes in synthesis or characterization could be normalized rather than trigger broader review."},{"mechanism_slug":"raw_signal_fallback_switch","role":"Restores full-package review for invalid, stale, incompatible, out-of-scope, safety-relevant, or poorly reconstructed candidates.","counterfactual_removal":"Without fallback, the triage system could continue filtering results precisely when its expectations were least defensible."},{"mechanism_slug":"surprise_to_action_bridge","role":"Connects each validated discrepancy to a named owner and a defined action such as replicate, orthogonal assay, contamination check, or model-boundary review.","counterfactual_removal":"Without an owned action, residuals could decorate a discovery dashboard without changing evidence collection or learning."}],"causal_chain":["A scoped campaign model freezes expected characterization outcomes and uncertainty before each candidate's measurements are revealed.","Complete measurements are standardized with provenance and compared against the frozen expectation.","The comparator produces reconstructive numerical corrections plus categorical, missingness, and unpredicted-feature residuals.","Precision and consequence weighting separates reliable model contradictions from measurement noise while protecting designated classes and novel signals.","The primary review queue receives informative residuals with expected context, while routine packages remain reconstructable and available on demand.","Named reviewers use the residual to choose replication, orthogonal characterization, specimen investigation, or model-boundary review.","Validated discrepancies revise the campaign model only through a slower, attributable update cycle and can affect the next experiment-planning round.","Random full-package audits and residual-structure monitoring reveal evidence the production predictor or gate would otherwise suppress.","Version, scope, quality, drift, safety, or reconstruction failures restore complete review until human owners reauthorize predictive triage."],"baseline":"Send every candidate's complete standardized characterization package to the same scientist-review queue, retain all raw files, and select follow-up measurements through manual comparison or fixed campaign rules. The bounded comparison must count reviewer time, follow-up measurements, model operation, residual preparation, audits, fallback reviews, and error investigation rather than treating a shorter queue alone as success.","nearest_rivals":["Bayesian optimization, which selects candidate experiments from predicted objectives but does not necessarily represent each observed characterization package as a reconstructive residual with synchronized baselines, raw audits, and full-review fallback.","Active learning based on predictive uncertainty, which prioritizes uncertain candidates but can miss confident model contradictions and need not suppress expected report content.","Standalone anomaly detection, which flags unusual results without making signed model-relative correction the review message and model-teaching signal.","Fixed feature or threshold screening, which routes predefined outcomes without a versioned generative expectation or reconstructive context.","Automated phase or property classification, which produces labels but does not govern review capacity through residual reporting and independent audit.","Full manual campaign review, which preserves context but does not explicitly allocate attention according to calibrated prediction error."],"remaining_contrastive_claim":"The proposal is a governed discovery-learning architecture in which the predicted characterization package is the implicit baseline, the reconstructive discrepancy is the primary review message and teaching signal, and independent full-package audits constrain what the model may suppress. Its core is not merely choosing experiments, predicting properties, detecting anomalies, or classifying phases; it coordinates prediction, residual routing, owned follow-up, model revision, synchronization, and decompression around a finite scientific-review budget.","authority_safety":{"decision_authority":"Materials scientists retain authority over candidate selection, synthesis, replication, characterization, interpretation, and model-scope changes. Characterization specialists determine measurement validity, the safety owner controls protected procedures, and the model owner may propose but not unilaterally activate model or threshold revisions. The residual system may prioritize review and suggest predefined follow-up actions but may not initiate experiments or certify a material.","authorized_first_step":"Run the queue in shadow mode for one bounded, preauthorized candidate matrix using a frozen model and fixed characterization plan. Preserve every raw file and complete standardized package, keep the existing planning process authoritative, and prohibit residual rankings from changing synthesis or measurement decisions.","excluded_actions":["Autonomous synthesis, reagent selection, processing, replication, or characterization","Reducing existing laboratory safety review, interlocks, or hazardous-material controls","Discarding raw characterization files or complete standardized results during the first evidence step","Suppressing new chemical classes, invalid measurements, specimen-identity conflicts, safety flags, or unpredicted features","Changing the model, thresholds, consequence weights, or assay definitions during scoring","Using reconstructed results as the sole basis for material identity, performance certification, publication, or release","Optimizing thresholds to meet a desired review-queue size","Treating a missing package, failed instrument, or broken channel as a model-consistent result"],"halt_rollback":"Suspend residual-only triage and restore complete package review for the affected candidate class when checksums differ, observations are missing, assay quality fails, reconstruction exceeds a predeclared budget, residuals show sustained structure, raw audits reveal an omitted material discrepancy, chemical or process scope changes, the model expires, or any protected safety condition appears. Preserve the trigger, full package, routing trace, and model version; roll back to the last approved model and threshold table, and require the responsible scientist, characterization specialist, and model owner to approve re-entry."},"negative_tests":{"strongest_counterevidence":"Independent full-package review discovers a reproducible phase, structural feature, or property discrepancy that would alter follow-up or campaign interpretation but that the residual gate suppressed while reporting compatible versions, adequate confidence, and nominal reconstruction.","problem_falsifier":"The complete candidate packages fit within the declared review and follow-up capacity, and blinded reviewers do not experience delayed or inconsistent identification of model-relevant discrepancies; residual triage would then address no binding campaign constraint.","intervention_falsifier":"Under the predeclared evidence-preservation and safety constraints, the residual queue fails to produce the same protected-event and decision-relevant classifications as complete review, systematically favors known discrepancy types, or consumes no less total reviewer and follow-up capacity after audits, model maintenance, and fallback are counted.","risks":["A confident but misspecified campaign model can classify genuinely informative chemistry as expected.","Feature standardization can remove an unfamiliar peak or morphology before residual computation.","Published thresholds can become targets for queue management rather than scientific error control.","Assay uncertainty may be underestimated, giving noisy residuals excessive influence on model updates.","Residual-weighted replay can reinforce existing blind spots because it overrepresents what the current model already recognizes as surprising.","A random audit may miss rare discrepancies, while risk stratification may focus only on anticipated failure modes.","Measurement or specimen errors can contaminate the campaign model if review does not separate data faults from chemical evidence.","Frequent alerts can create reviewer fatigue and pressure owners to desensitize the gate.","Routine packages removed from the primary queue may lose contextual value needed to interpret a later campaign-wide pattern.","Actions prompted by residuals change later candidate selection, so intervention history can be mistaken for passive evidence unless explicitly recorded.","Residual packages can expose sensitive formulation or process information even when full raw files remain access-controlled." ]},"next_evidence_step":"Pre-register a shadow comparison on one finite candidate matrix, one material family, fixed synthesis ranges, and a fixed set of characterization assays. Freeze the prediction model, uncertainty estimates, standardization pipeline, checksum, confidence table, protected classes, review budget, audit draw, and model-update prohibition before results are revealed. Retain and review every complete package through the existing process while independently generating the residual queue. Include blinded, approved challenge packages that represent small reliable property shifts, categorical phase disagreements, unpredicted features, measurement-quality faults, missing observations, model-version mismatch, and out-of-scope compositions. Ask reviewers, without path labels, to assign the same predeclared actions from complete and residual presentations. Compare action concordance, protected-case capture, reconstruction fidelity, audit disagreement, residual structure, review workload, follow-up requests, acknowledgement, and fallback behavior. Reject operational triage if any protected or decision-relevant discrepancy is missed, any missing observation is treated as confirmation, any incompatible version is accepted, blinded actions materially disagree under the predeclared rule, or the governed residual path does not fit the campaign's capacity bounds better than complete review.","prior_art_status":"UNSEARCHED","diversity_from_prior_proposals":"Proposal 1 addresses live transmission and remote supervision of Raman spectra during a single physical reaction, using a matched spectral codec to reconstruct the monitored signal and trigger operational review. Proposal 3 instead governs scientist attention and follow-up allocation across a campaign of distinct synthesized candidates; its prediction target is a multimodal characterization outcome, its residual changes discovery review and later experiment planning, and all raw instrument files remain authoritative rather than serving primarily as a live communication stream. Proposal 2 addresses storage and reconstruction of computational atomistic trajectory frames through a hierarchy of motion predictors; Proposal 3 neither encodes atomic timesteps nor archives simulated coordinates and forces. Its actors, scarce resource, unit of analysis, intervention boundary, and downstream decision are different from both earlier proposals. It can be adopted as a campaign-review workflow without changing Raman monitoring or trajectory storage, while either earlier proposal can operate without a discovery queue.","revision_record":{"parent_version":null,"progress_targets_addressed":["Generate one additional complete candidate at proposal index 3","Address a problem distinct from live Raman monitoring and atomistic trajectory archival","Use residual processing to govern campaign review, follow-up characterization, and model learning","Provide complete authority, safeguards, rivals, falsifiers, and bounded evidence","Explain diversity from both earlier sealed proposals"],"conceptual_changes":[],"operational_changes":[],"evidence_changes":[],"claim_changes":[]}}