{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp06_four_proposal_generalization60_20260803","cell_id":"predictive_residual_processing__chemistry_materials","portfolio_valid":true,"proposal_assessments":[{"proposal_index":1,"complete":true,"causally_faithful":true,"materially_distinct":true,"reason":"Operationally specifies the Raman prediction target, matched versioned predictors, signed structured residuals, precision/consequence weighting, reconstructive transmission, governed updating, independent raw audits, synchronization, drift monitoring, protected bypasses, fallback, authority, falsifiers, and a bounded shadow test. Its live spectral-communication problem and codec-mediated causal path are distinct from the other proposals."},{"proposal_index":2,"complete":true,"causally_faithful":true,"materially_distinct":true,"reason":"Completely defines a scoped baseline-plus-residual evaluator, reference observations, signed energy/force/stress discrepancies, uncertainty-weighted learning, provenance, independent full-reference audits, validity gates, fallback, authority, rivals, falsifiers, and an equal-query test. The residual is both the correction representation and teaching signal, preserving the archetype while addressing scarce authoritative calculations rather than stream transmission or human triage."},{"proposal_index":3,"complete":true,"causally_faithful":true,"materially_distinct":true,"reason":"Fully specifies frozen campaign predictions, structured characterization residuals, precision-weighted routing, reconstructable expected results, owned follow-up actions, slower governed model revision, independent full-package audits, drift and decompression controls, authority, falsifiers, and a shadow evaluation. Its intervention reallocates campaign review and follow-up capacity rather than encoding a live sensor stream or producing a numerical physics evaluator."},{"proposal_index":4,"complete":true,"causally_faithful":true,"materially_distinct":true,"reason":"Completely defines the outgoing command as an efference copy, a scoped forward model, time-aligned multisensor subtraction, precision-weighted residual propagation, reconstruction, slow governed updating, raw audits, synchronization, protected safety channels, fallback, authority, falsifiers, and controlled testing. Its defining command-conditioned cancellation path differs materially from generic prediction of spectra, reference-method correction, and campaign triage."}],"pairwise_assessments":[{"proposal_a":1,"proposal_b":2,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 1 reduces live Raman transmission and review load with matched endpoint spectral residual coding; Proposal 2 reduces dependence on costly reference calculations by learning and applying a physical-model discrepancy to energies, forces, and stresses."},{"proposal_a":1,"proposal_b":3,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 1 reconstructs a time-ordered Raman stream for remote reaction supervision; Proposal 3 routes multimodal candidate-level characterization contradictions into a campaign review and follow-up workflow."},{"proposal_a":1,"proposal_b":4,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 1 predicts chemical spectra from prior reaction state to save communication capacity; Proposal 4 uses a copied actuator command to causally predict and cancel self-generated machine signals so external powder interactions become observable."},{"proposal_a":2,"proposal_b":3,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 2 creates a corrected computational evaluator under a reference-query budget; Proposal 3 creates a scientist-attention queue whose residuals govern candidate review, follow-up characterization, and later campaign learning."},{"proposal_a":2,"proposal_b":4,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 2 learns discrepancies between two numerical materials methods and falls back to the reference oracle; Proposal 4 subtracts predicted command-caused physical sensor returns during recoating and falls back to full live sensing."},{"proposal_a":3,"proposal_b":4,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 3 addresses campaign-scale review congestion through model-relative characterization triage; Proposal 4 addresses masking of transient machine interactions through efference-copy cancellation and immediate operator inspection routing."}],"replacement_indices":[],"rationale":"All four proposals are operationally complete and preserve the archetype's prediction-before-observation, structured residual, calibrated routing or learning, versioning, independent raw/full-state audit, drift detection, and decompression safeguards. Every pair differs in the affected problem, load-bearing intervention, and causal path: live spectral coding, reference-level numerical correction, discovery-review triage, and command-conditioned self-signal cancellation are independently adoptable opportunities rather than renamed variants or implementation changes."}