{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp06_four_proposal_generalization60_20260803","cell_id":"predictive_residual_processing__human_computer_interaction","arm":"COMPLETE_PROPOSAL_PORTFOLIO","candidate_id":"prp-hci-task-flow-residual-session-review","proposal_index":2,"version":0,"title":"Task-Flow Residual Replay for Interface Breakdown Review","problem":"A UX research team evaluating a multi-step enterprise workflow may spend its limited review capacity watching complete interaction recordings in which most actions follow an understood task path. Subtle breakdown evidence—repeated field visits, reversals, oscillation between help and form content, an unexpected route around a control, or abandonment after apparent progress—can remain buried within routine interaction. Aggregate funnels remove the temporal context needed to interpret these recoveries, while reviewing only known error events cannot reveal departures the instrumentation did not anticipate.","actors":["Consenting workflow user","UX researcher","Application instrumentation client","Residual-session analysis service","Product accessibility and privacy reviewers","Application product team"],"observable_state":"For each consented task session, instrumentation can record the declared task boundary, versioned interface state, predicted next interaction and state transition, actual interaction and resulting state, uncertainty, structured residual, emitted review episode, requests for full context, and client-to-analysis heartbeat. The problematic state is observable when reviewers consume substantial replay time on predicted steps while breakdown candidates are discovered late, inconsistently, or only after downstream task failure.","consequence":"Researchers may allocate review attention to routine navigation while missing interaction sequences that challenge the team's model of how the workflow is understood. Product changes may then be based on completion counts or conspicuous errors without evidence about earlier confusion and recovery. Conversely, naive anomaly scoring can stigmatize legitimate alternative strategies or expose sensitive atypical behavior.","affected_objective":"Use bounded UX-research attention to examine interaction sequences that materially contradict a declared task-flow model while preserving enough raw context to interpret legitimate variation, accessibility strategies, and model failure.","intervention":"Introduce a consent-bound session-review pipeline that maintains a versioned predictive model of the next interaction and resulting interface state for one declared task and application version. Before each observed action, the instrumented client records the model's expected action distribution and state transition. It compares the actual action and post-action state with that expectation, producing a structured residual that distinguishes direction, affected interface object, timing, and sequence pattern. A consequence-aware gate groups selected residuals into reconstructible review episodes containing the expected path, actual departure, local pre/post context, uncertainty, and model version. Routine predicted stretches are represented by compact counts and timing summaries rather than propagated as full replay. The review service can reconstruct a session timeline from the shared model plus residuals or request the complete consented recording. Client and service verify model compatibility, exchange heartbeats, and periodically anchor with full state. Random full-session samples and stratified samples from accessibility modes independently test what the residual pipeline suppresses. Sustained residual structure, version mismatch, missing events, high uncertainty, consent changes, or reconstruction failure suspends residual-only capture and uses the governed full-session path. Model revisions occur only after researchers classify reviewed misses as interface breakdown, legitimate strategy, instrumentation error, or model-boundary failure.","structural_mapping":[{"archetype_element":"Prediction target and observation boundary","domain_realization":"The prediction target is the next user interaction and resulting interface state within one declared task, application version, and short horizon; activity outside that scope is not scored."},{"archetype_element":"Generative model state","domain_realization":"A versioned probabilistic task-flow model represents expected actions, transitions, timing ranges, and uncertainty for the current workflow state."},{"archetype_element":"Expected and actual behavior","domain_realization":"The client stores the predicted action distribution before observing the user's next action, then captures the actual action and resulting state with timestamp and interface provenance."},{"archetype_element":"Structured prediction comparator","domain_realization":"The comparator encodes unexpected objects, transition direction, timing, repetitions, reversals, and state divergence rather than assigning only a scalar anomaly score."},{"archetype_element":"Precision-weighted residual propagation","domain_realization":"Residual episodes compete for bounded researcher attention according to model uncertainty, observation reliability, possible task consequence, recurrence, and cost of review."},{"archetype_element":"Reconstructible representation","domain_realization":"Expected path segments remain implicit in the shared model; residual events, timing summaries, provenance, and periodic anchors permit timeline reconstruction or a governed request for full replay."},{"archetype_element":"Synchronization and validity","domain_realization":"The instrumentation client and review service exchange model and application checksums, reject incompatible residuals, maintain heartbeats, and expire models after declared interface or time boundaries."},{"archetype_element":"Residual-driven update","domain_realization":"Reviewed and classified departures can revise task-flow probabilities, model scope, instrumentation, or the interface hypothesis, with approved changes receiving a new version."},{"archetype_element":"Independent raw audit","domain_realization":"A consented random sample of complete sessions, supplemented by accessibility-mode strata, is reviewed independently of residual scores to expose systematic blind spots."},{"archetype_element":"Decompression and safety controls","domain_realization":"Consent withdrawal, sensitive-field entry, high uncertainty, event gaps, drift, version mismatch, or failed reconstruction stops residual processing; sensitive content is excluded rather than exposed through residual salience."}],"mechanism_mapping":[{"mechanism_slug":"anomaly_detection_model","role":"A task-conditioned model estimates expected next actions and state transitions, making departures candidates for review rather than treating every interaction as equally informative.","counterfactual_removal":"Without an explicit expected-behavior model, the pipeline becomes rule-based event tagging and cannot define or learn model-relative residuals."},{"mechanism_slug":"event_triggered_residual_reporting","role":"Only precision-weighted departures and compact transition summaries are propagated into the primary researcher queue, with client heartbeats making non-reporting observable.","counterfactual_removal":"Without event-triggered reporting, the review service continues receiving and presenting the complete interaction stream as its primary representation."},{"mechanism_slug":"precision_weighted_error_gate","role":"The gate combines departure structure, model uncertainty, instrumentation reliability, possible task consequence, recurrence, and review cost while retaining suppressed-event metadata for audit.","counterfactual_removal":"Without consequence- and uncertainty-aware weighting, visually conspicuous but noisy actions can crowd out small, reliable indicators of consequential confusion."},{"mechanism_slug":"model_version_checksum_handshake","role":"The client and analysis service verify identical task-flow and application baselines before encoding or interpreting a residual, and each episode carries both identities.","counterfactual_removal":"Without compatibility checks, an ordinary action under a changed interface could be reconstructed against an obsolete path and misclassified without an obvious processing error."},{"mechanism_slug":"periodic_full_state_resynchronization","role":"Periodic interface-state anchors and triggered snapshots bound reconstruction error after missing, delayed, or reordered interaction events.","counterfactual_removal":"Without full-state anchors, one missed transition can corrupt the inferred task position and every subsequent residual in the session."},{"mechanism_slug":"prediction_error_replay_buffer","role":"Selected residual episodes retain expected and actual paths, surrounding state, uncertainty, and provenance for classification, calibration, and regression testing.","counterfactual_removal":"Without a contextual replay buffer, residual scores would identify departures but not provide enough evidence to distinguish interface breakdown from legitimate user strategy."},{"mechanism_slug":"shadow_raw_channel_sampling","role":"Random complete-session review and separate accessibility-mode sampling compare raw trajectories with reconstructed residual timelines independently of the production gate.","counterfactual_removal":"Without raw sampling, the system can measure only departures already recognized by its own model and may certify its blind spots."},{"mechanism_slug":"model_drift_monitoring","role":"Residual distributions, reconstruction disagreement, interface-version changes, and model age determine whether residual representation remains valid for the workflow.","counterfactual_removal":"Without drift monitoring, a redesigned or gradually changing workflow could produce misleading residuals or normalize new breakdown patterns."},{"mechanism_slug":"raw_signal_fallback_switch","role":"High uncertainty, checksum mismatch, missing heartbeat, event loss, reconstruction failure, or an approved audit trigger switches the scoped session to full-state capture when consent permits; otherwise capture stops.","counterfactual_removal":"Without fallback or halt behavior, the pipeline would continue producing incomplete but plausible session reconstructions under invalid conditions."},{"mechanism_slug":"prediction_error_review","role":"Researchers classify material departures before any model or interface conclusion is accepted, separating breakdowns from valid alternatives, accessibility techniques, instrumentation faults, and scope violations.","counterfactual_removal":"Without human error review, the model could learn conformity to its own expected path and label legitimate user behavior as defective."},{"mechanism_slug":"forecast_backtesting","role":"Walk-forward testing on held-out task sessions identifies the application versions, task stages, and interaction modes in which residual suppression stays within the declared reconstruction budget.","counterfactual_removal":"Without scoped out-of-sample testing, a model fitted to reviewed sessions could be authorized to suppress routine stretches where its predictive envelope is unknown."}],"causal_chain":["A researcher defines one task, interface version, prediction horizon, review decision, consent boundary, and reconstruction tolerance.","Before each interaction, the client records a versioned probability distribution over expected next actions and resulting interface states.","The actual action and state are compared with the stored expectation to produce a signed, structured, provenance-tagged residual.","A precision-and-consequence gate groups qualifying residuals with nearby context while encoding predicted stretches as compact summaries.","The review queue therefore spends its primary capacity on episodes that challenge the declared task-flow model, while retaining the expected baseline needed for interpretation.","Matched model versions, heartbeats, and full-state anchors allow the service to reconstruct session order and distinguish predicted behavior from missing telemetry.","Random complete-session audits test whether consequential departures, legitimate alternative strategies, or accessibility interactions were suppressed by the model.","Sustained structured error or failed reconstruction triggers full capture or halt, rather than allowing the predictor to define the changed workflow as normal.","Researchers classify residuals, and only approved classifications revise the interface hypothesis, task-flow model, thresholds, or model boundary."],"baseline":"The baseline is consented full-session replay for the same task and application version, supplemented by aggregate completion funnels and manually defined error events. Reviewers receive the complete chronological interaction record without prediction-based suppression. Comparison must account for instrumentation, storage, model maintenance, audit, and reconstruction work as well as researcher review time.","nearest_rivals":["Conventional session replay, which preserves full chronological behavior but does not represent expected stretches through a synchronized model or allocate review by structured prediction error.","Funnel analysis, which highlights stage conversion and abandonment but discards much of the local sequence needed to interpret departures and recovery.","Rule-based frustration detection, which flags predefined behaviors such as repeated clicks without maintaining a reconstructible task-flow prediction or learning from model-relative error.","Generic behavioral anomaly scoring, which can identify unusual sessions but need not preserve signed transition residuals, matched model versions, raw audit samples, or full-state fallback.","Critical-incident sampling initiated by user reports, which provides rich accounts of recognized breakdowns but does not continuously compare expected and observed interaction paths."],"remaining_contrastive_claim":"The candidate is a predictive representation architecture for UX-research evidence, not merely a ranking model. Expected task stretches are held in a scoped, synchronized model; actual interaction is encoded as reconstructible structured residuals; validated errors govern both researcher attention and bounded model revision; and independent full-session audits and automatic decompression constrain what the predictor may hide. This structural claim does not assert novelty or superior performance.","authority_safety":{"decision_authority":"The user controls consent and withdrawal for interaction capture. The UX research lead may authorize the bounded study and review queue. Privacy and accessibility reviewers approve excluded content, raw-sample handling, accessibility strata, retention, and protected halt conditions. Only a designated model owner may publish a new predictor version, and product managers may not use residual scores as individual performance or competence measures.","authorized_first_step":"Conduct an offline shadow analysis of previously consented research sessions or scripted sessions from one non-production workflow version. Generate residual episodes beside, but do not replace or delete, the full recordings; have reviewers classify both a blinded random sample and residual-selected episodes.","excluded_actions":["No covert interaction recording or expansion beyond the participant's stated consent.","No capture or residualization of passwords, message contents, health information, payment data, or other fields designated sensitive by the study protocol.","No use of residual scores to evaluate, rank, discipline, or personalize consequences for individual users or employees.","No automatic interface change, task reassignment, or model update based on unreviewed residuals.","No deletion of the governed full-session evidence during the first evaluation.","No inference that an unusual path is user error, impairment, or interface failure without contextual review.","No tuning solely to reduce the number of episodes entering the researcher queue."],"halt_rollback":"Stop residual processing for the affected trace if consent is withdrawn, sensitive content enters capture, model or application checksums mismatch, telemetry heartbeat fails, reconstruction exceeds tolerance, a protected accessibility interaction is truncated, or an audit finds an unbudgeted omission. Disable the predictor version, retain or delete traces according to the original consent and incident protocol, return analysis to governed full replay, and require privacy, accessibility, and model-owner approval before retesting."},"negative_tests":{"strongest_counterevidence":"The strongest counterevidence would be that the interaction paths most valuable to UX research are inherently plural or weakly predictable, causing the model to treat legitimate strategies as residuals while compressing contextual behavior reviewers need to understand them. The intervention would also be undermined if model construction, synchronization, audit, and reconstruction require at least as much constrained review effort as examining full sessions.","problem_falsifier":"The inferred problem is falsified for the scoped workflow if time-coded review observations show that complete replays do not materially allocate reviewer attention to already-understood task steps and that breakdown candidates are neither delayed nor obscured by routine interaction.","intervention_falsifier":"The intervention is falsified for the tested scope if held-out and raw-audit sessions show residual reconstructions outside the preregistered tolerance, omit any protected event, systematically overselect legitimate accessibility or alternative strategies, fail to improve the proportion of reviewed evidence relevant to the declared research question after including maintenance costs, or require frequent full-session fallback.","risks":["The model may encode one preferred task path and mischaracterize legitimate alternatives as confusion.","Routine-looking behavior may conceal uncertainty that is visible only in pacing, context, or participant explanation.","Residual-selected sessions may expose distinctive or sensitive behavior more sharply than random replay.","Adaptive updates may normalize recurring interface defects if classifications are weak or product incentives favor a quiet queue.","Instrumentation gaps may be mistaken for perfect prediction unless heartbeat and missingness states are enforced.","A shared task model may reproduce the same accessibility blind spot in capture, reconstruction, and review.","Random auditing may still miss rare interaction patterns, while risk-stratified auditing may overfocus known concerns.","Reviewers may anchor on the predicted path and rationalize residuals instead of considering that the model boundary is wrong.","Full-session fallback may conflict with data-minimization goals unless capture stops when fuller recording lacks consent."]},"next_evidence_step":"Pre-register one offline comparison on a single task and application version. Define the task boundary, model version, protected content, consent constraints, exact reconstruction metric, maximum event-order error, residual-review budget, accessibility strata, classification rubric, and halt thresholds before scoring sessions. Use walk-forward fitting with an untouched holdout. Ask reviewers, blinded to selection source, to classify residual-selected episodes and equal-duration random full-replay segments; separately reconstruct every holdout session from model state, residuals, summaries, and anchors. Inject a missing heartbeat, dropped transition, interface-version mismatch, alternate valid route, accessibility-navigation sequence, and sensitive-field boundary to verify fallback and exclusions. Count modeling, audit, and review labor. The result can authorize only a supervised prospective shadow study within the same task scope.","prior_art_status":"UNSEARCHED","diversity_from_prior_proposals":"Earlier proposal 1 mediates the live auditory interface for a screen-reader user by predicting the consequences of the user's own commands and speaking unexpected accessibility-tree changes first. This proposal does not alter any user's live interface, speech, or task feedback. It addresses a different actor's constraint: a UX researcher's capacity to inspect consented task sessions after interaction. Its prediction target is the user's next action and resulting workflow state rather than the interface's accessible response to an issued command; its residual becomes a reconstructible research-review episode rather than user-facing speech; and its downstream action is hypothesis classification and bounded redesign evidence rather than immediate orientation. It is independently adoptable as an offline research pipeline without the screen-reader mediator, and the screen-reader intervention does not supply this proposal's session-level modeling, reviewer queue, consent-governed replay buffer, or task-flow audit path.","revision_record":{"parent_version":null,"progress_targets_addressed":[],"conceptual_changes":["Initial formulation of session-review overload as a task-conditioned predictive residual representation problem distinct from live assistive feedback."],"operational_changes":["Defined consent boundaries, task-flow prediction, structured interaction residuals, reconstructible episodes, synchronization, raw-session auditing, fallback, authority, and halt conditions."],"evidence_changes":["Specified a bounded offline replay and held-out reconstruction test; no external or prior-art evidence was used."],"claim_changes":["Limited the proposal to a falsifiable structural opportunity and made no claim about novelty, prevalence, demand, or effect size."]}}