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01ClarusC64 /clinical-quad-endpoint-adjudication-drift-blinding-breach-pressure-governance-submission-v0.1Clarus Clinical Quad Coupling Endpoint Adjudication Integrity v0.1 PurposeDetect adjudication drift driven by four interacting nodes. Quad nodes Endpoint cluster shift Blinding gap or reviewer dominance Operational or vendor process change Governance submission or review pressure InputOne vignette. OutputStrict JSON only. Required keys adjudication_integrity_risk risk_type driver_nodes recommended_action action_detail rationale confidence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-endpoint-adjudication-drift-blinding-breach-pressure-governance-submission-v0.1.texttext-generationn<1K0 likes67 downloads7mo agoHugging Face02ClarusC64 /clinical_narrative_negative_evidence_handling_v0.4Clinical Narrative Negative Evidence Handling v0.4 Purpose Test whether a model handles negative evidence without narrative spin. This version adds timeline steps cross trial negative carryover suppression pressure prompts explicit evidence status and submission positioning Input columns data_anchor negative_pressures draft_narrative audience timeline_step Model task Return one JSON object negative_flagslist of short labels evidence_statusexploratory, mixed, negative… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_narrative_negative_evidence_handling_v0.4.texttext-classificationn<1K0 likes56 downloads7mo agoHugging Face03ClarusC64 /modality-state-consistency-v0.1 What this dataset tests Inputs arrive in many forms. State must stay coherent. Why it exists Models drift when switching modality. Facts change. Promises vanish. This set checks whether state stays consistent. Data format Each row contains modality_context user_message modality_pressure constraints failure_modes_to_avoid target_behaviors gold_checklist Feed the model modality_context user_message constraints Score for cross-modal… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/modality-state-consistency-v0.1.texttext-generationn<1K0 likes45 downloads8mo agoHugging Face04ClarusC64 /clinical-parallel-process-awareness-v0.1Clinical Parallel Process Awareness v0.1 Goal Test if a model can hold separate reasoning streams at once Detect constraint dismissal Detect bleed-over where one stream turns into claims in the other What it measures streams_heldResponse acknowledges and maintains both streams bleed_overConstraint stream improperly becomes a medical claim, or vice versa premature_synthesisResponse forces a single solution that silences one stream assumption_collapseResponse drops a premise entirely Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-parallel-process-awareness-v0.1.texttext-generationn<1K0 likes43 downloads8mo agoHugging Face05ClarusC64 /clinical-anamnesis-fidelity-v0.1Clinical Anamnesis Fidelity v0.1 Goal Test accurate recall and integration of patient history across time Detect distortion, blending, or invention after intervening tasks What it measures assumption_trackingFailure to honor prior stated history fabricationIntroduction of unstated conditions or treatments inference_chainFilling memory gaps with unsupported links Dataset format Each row simulates multi-session history Intervening tasks introduce context pressure Candidate response is… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-anamnesis-fidelity-v0.1.texttext-generationn<1K0 likes42 downloads8mo agoHugging Face06ClarusC64 /clinical-perception-intervention-justification-v0.1Clinical Perception–Intervention Justification v0.1 Goal Test whether actions follow directly from perceptual evidence Detect interventions that appear without a visual cause Detect escalation that exceeds image-supported severity What it measures action_without_causeAn intervention is proposed with no supporting image evidence over_escalationThe action exceeds what the visual severity supports justification_okThe response links perception to action explicitly or proportionally How it… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-perception-intervention-justification-v0.1.texttext-generationn<1K0 likes41 downloads8mo agoHugging Face07ClarusC64 /clinical_structural_drift_detection_v0.1Clinical Structural Drift Detection PurposeDetect when a clinical plan drifts from the evolving patient reality. You get a case with time change signals.You decide if drift exists.You label the drift type.You propose the corrective adjustment. Input fields patient_summary time_series current_plan observed_change drift_signal Required outputReturn one JSON object drift_detectedyes or no drift_typeMust match the allowed list adjustmentOne sentence Allowed drift_type values… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_structural_drift_detection_v0.1.texttext-classificationn<1K0 likes41 downloads8mo agoHugging Face08ClarusC64 /category-boundary-integrity-v01Cardinal Meta Dataset 3.2Category Boundary Integrity Purpose Test whether claims stay within the correct category Test whether distinct categories are not collapsed Test whether certainty is not imported across category boundaries Central question What category is this claim in What this dataset catches Legal treated as ethical Statistical treated as causal Performance treated as safety Procedure treated as morality Reporting treated as impact Consistency treated as truth… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/category-boundary-integrity-v01.texttext-generationn<1K0 likes38 downloads8mo agoHugging Face09ClarusC64 /differential-preservation-narrative-v01Differential Preservation Under Narrative PressureClinical Narrative Integrity v0.2 Purpose Test whether models preserve multiple plausible diagnoses Test whether narrative fluency collapses uncertainty Test resistance to premature diagnostic closure Central question What else could this be Why this dataset exists Narrative pressure rewards coherence.Clinical safety requires openness. This dataset isolates the moment where a single story becomes dominant despite nonspecific evidence.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/differential-preservation-narrative-v01.texttext-generationn<1K0 likes38 downloads8mo agoHugging Face10ClarusC64 /clinical-cross-modal-memory-fidelity-v0.1Clinical Cross-Modal Memory Fidelity v0.1 Goal Test whether prior image evidence is recalled accurately over time Detect retroactive distortion driven by later narrative Detect fabrication used to patch memory gaps What it measures memory_driftEarlier image facts are altered or inverted fabricationNew findings are invented at recall cross_modal_consistencyRecalled description matches original image evidence How it works Initial image facts are fixed and explicit Intervening tasks… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-cross-modal-memory-fidelity-v0.1.texttext-generationn<1K0 likes38 downloads8mo agoHugging Face11ClarusC64 /clinical_evidence_coherence_breakdown_v0.1Clinical Evidence Coherence Breakdown PurposeDetect when a clinical plan stops matching the evidence. You get evidence signals and a stated plan.You decide if a coherence break exists.You label the breakdown type.You propose the corrective action. Input fields patient_summary evidence_signals stated_diagnosis planned_action Required outputReturn one JSON object coherence_breakyes or no breakdown_typeMust match the allowed list correctionOne sentence Allowed breakdown_type… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_evidence_coherence_breakdown_v0.1.texttext-classificationn<1K0 likes38 downloads8mo agoHugging Face12ClarusC64 /clinical-quad-consent-version-drift-reconsent-gap-enrollment-pressure-governance-audit-v0.1Clarus Clinical Quad Coupling Informed Consent Integrity v0.1 PurposeDetect consent integrity failures driven by four interacting nodes. Quad nodes Consent version drift or addendum mismatch Re-consent gap after material risk change Enrollment pressure or incentives Governance audit or regulator timing InputOne vignette. OutputStrict JSON only. Required keys consent_integrity_risk risk_type driver_nodes recommended_action action_detail rationale confidence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-consent-version-drift-reconsent-gap-enrollment-pressure-governance-audit-v0.1.texttext-generationn<1K0 likes38 downloads7mo agoHugging Face13ClarusC64 /embodied-perceptual-state-integrity-v0.1Embodied Perceptual State Integrity v0.1 What this tests Whether an embodied agent preserves a coherent internal world state across movement, delay, and perceptual absence. Failure modes state_driftThe response contradicts the true state at time t1 fabricated_updateThe response claims a state change when the world facts did not change state_integrityThe response states the correct t1 state without contradiction How it works world_facts_t0 provides initial ground truth actions_between… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/embodied-perceptual-state-integrity-v0.1.texttext-generationn<1K0 likes37 downloads8mo agoHugging Face14ClarusC64 /clinical_chain_break_audit_v0.1Clinical Chain Break Audit What this dataset tests You detect the correct break point in a care chain You stop a harmful continuation You name the right corrective action Data fields id setting patient_summary chain_stage expected_break_point signal_available continuation_action correct_action harm_outcome gold_break_stage gold_correct_action Task Input setting patient_summary chain_stage expected_break_point signal_available continuation_action OutputReturn a single JSON object… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_chain_break_audit_v0.1.texttext-classificationn<1K0 likes36 downloads8mo agoHugging Face15ClarusC64 /autonomous-driving-ethical-cost-field-construction-v0.1 What this dataset tests Whether an intelligence system can constructan ethical cost field for a driving scene. The task is not to choose an action.The task is to model how harm distributes across agents. Required outputs ethical cost field agent harm vectors aggregate deformation score rights infringement index uncertainty band Use case Foundation layer for ethical navigation systems.Trains models to map harm before selecting actions.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-ethical-cost-field-construction-v0.1.tabulartext-generationn<1K0 likes35 downloads8mo agoHugging Face16ClarusC64 /clinical-quad-recruitment-selection-bias-protocol-pressure-operational-drift-v0.1Clarus Clinical Quad Coupling Recruitment Selection Bias Protocol Pressure Operational Drift v0.1 What this dataset isThis dataset tests whether a model can detect recruitment and selection bias caused by four interacting nodes. Quad coupling nodes Recruitment speed or site pressure Eligibility or baseline data gaps Operational or staffing drift Governance or milestone pressure Input One vignette OutputReturn strict JSON only. Required output JSON keys recruitment_bias_risk… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-recruitment-selection-bias-protocol-pressure-operational-drift-v0.1.texttext-generationn<1K0 likes34 downloads7mo agoHugging Face17ClarusC64 /invariant_preservation_under_rewrite_v01Invariant Preservation Under Rewrite v0.1 This evaluation dataset tests whether a model preserves absolute truths when asked to rewrite, simplify, or paraphrase them. This is not training data. What it tests Integrity of truth under paraphrase Resistance to semantic softening Preservation of absolute statements during transformation Drift introduced by “make it clearer” or “make it flexible” requests Core idea A simple invariant is stated The model agrees with it The model is asked to… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/invariant_preservation_under_rewrite_v01.tabulartext-generationn<1K0 likes33 downloads8mo agoHugging Face18ClarusC64 /map-territory-control-v01Cardinal Meta Dataset 3.3Map–Territory Control Purpose Test whether representations are not mistaken for reality Test whether models, metrics, and frameworks are treated as tools Test whether certainty is not imported from maps into territory Central question Is this a representation or the thing itself What this dataset catches Benchmark score treated as safety Model prediction treated as outcome Simulation treated as real world behavior Framework treated as proof Estimate… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/map-territory-control-v01.texttext-generationn<1K0 likes33 downloads8mo agoHugging Face19ClarusC64 /clinical_frontier_unknown_detection_v0.1Clinical Frontier Unknown Detection PurposeDetect when a case sits beyond routine clinical knowledge and needs escalation. You receive: patient_summary workup_summary current_plan You decide: frontier_caseyes or no reason_typemust match the allowed list next_stepone sentence Allowed reason_type values no_frontier rare_disease_suspected conflicting_evidence refractory_to_standard atypical_multisystem novel_adverse_event unexplained_biomarker_pattern unknown_unknown… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_frontier_unknown_detection_v0.1.texttext-classificationn<1K0 likes33 downloads8mo agoHugging Face20ClarusC64 /clinical-intervention-sequencing-and-state-control-v0.2 Clinical Multi-Evidence State Integration Benchmark CMESI v0.2 The Clinical Multi-Evidence State Integration Benchmark (CMESI) evaluates whether an AI system can reconstruct the evolving state of a complex clinical case across a sequence of heterogeneous evidence events. CMESI does not test whether a model can identify a diagnosis from a static vignette alone. It tests whether the model can: maintain several competing clinical hypotheses simultaneously;… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-intervention-sequencing-and-state-control-v0.2.texttext-generationn<1K1 likes33 downloads2mo agoHugging Face21ClarusC64 /clinical_time_gap_resilience_v0.1Clinical Time Gap Resilience PurposeTest whether a model avoids anchoring on stale data when time passes and new information arrives. Input fields last_known_state time_gap new_info proposed_action Required outputOne JSON object time_gap_resilientyes or no gap_risklow, medium, high correct_actionone sentence Run scoringpython scorer.py --predictions predictions.jsonl --test_csv data/test.csv texttext-classificationn<1K0 likes32 downloads8mo agoHugging Face22ClarusC64 /clinical-narrative-boundary-control-v01Clinical Narrative Boundary ControlCardinal Clinical Meta Dataset Purpose Test whether models preserve the boundary between description and diagnosis Test whether narrative tone introduces unsupported certainty Test whether clinical fluency masks evidential limits Central question Is this describing findings, or asserting a conclusion Why this dataset exists Clinical narratives are where reasoning fails quietly. Language becomes confident.Structure dissolves.Diagnosis slips in without… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-narrative-boundary-control-v01.texttext-generationn<1K0 likes31 downloads8mo agoHugging Face23ClarusC64 /clinical_identity_frame_shift_detection_v0.1Clinical Identity Frame Shift Detection PurposeDetect when the current clinical label no longer fits the evolving evidence. You get: an initial identity label new evidence signals a continuing plan You decide: is the current identity still valid what the new identity should be what action should follow Input fields patient_summary initial_identity new_evidence current_plan Required outputReturn one JSON object identity_validyes or no new_identityshort phrase… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_identity_frame_shift_detection_v0.1.texttext-classificationn<1K0 likes31 downloads8mo agoHugging Face24ClarusC64 /clinical-evidence-conclusion-alignment-v0.1 What this dataset tests Clinical conclusions must reflect evidence. Language must track statistics. Why it exists Clinical papers drift at the conclusion. Spin enters here. This set detects misalignment between results and claims. Data format Each row contains trial_result conclusion_statement alignment_pressure constraints failure_modes_to_avoid target_behaviors gold_checklist Feed the model trial_result conclusion_statement Score for… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-evidence-conclusion-alignment-v0.1.texttext-classificationn<1K0 likes30 downloads8mo agoHugging Face25ClarusC64 /clinical-quad-site-performance-signal-drift-oversight-lag-v0.1Clarus Clinical Quad Coupling Site Performance Signal Drift Oversight Lag v0.1 What this dataset isThis dataset tests whether a model can detect site-level performance drift driven by four interacting nodes. Quad coupling nodes Enrollment or reporting signal shift Data capture or documentation gaps Operational staffing or monitoring lag Governance pressure such as reviews, incentives, or interim analyses Input One site vignette OutputReturn strict JSON only. Required output JSON… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-site-performance-signal-drift-oversight-lag-v0.1.texttext-generationn<1K0 likes30 downloads7mo agoHugging Face26ClarusC64 /embodied-constraint-aware-recovery-v0.1Embodied Constraint-Aware Recovery v0.1 What this tests Whether an embodied agent recovers from failed or blocked actions without inventing success and without looping blindly. Failure modes false_successResponse claims completion despite a failure outcome non_adaptive_repeatResponse repeats the same failed strategy without change recovery_okResponse proposes a constraint-aware alternative step How it works world_facts_t0 defines the initial state goal defines intent action_attempt is the… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/embodied-constraint-aware-recovery-v0.1.texttext-generationn<1K0 likes29 downloads8mo agoHugging Face27ClarusC64 /clinical-quad-enrollment-criteria-drift-site-selection-bias-screening-pressure-v0.1Clarus Clinical Quad Coupling Enrollment Criteria Drift Site Selection Bias Screening Pressure v0.1 PurposeDetect enrollment population drift driven by four interacting nodes. Quad nodes Criteria relaxation or documentation gap Site selection or recruitment bias Screening workflow pressure Governance or interim timing pressure InputOne vignette. OutputStrict JSON only. Required keys enrollment_drift_risk risk_type driver_nodes recommended_action action_detail rationale… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-enrollment-criteria-drift-site-selection-bias-screening-pressure-v0.1.texttext-generationn<1K0 likes29 downloads7mo agoHugging Face28ClarusC64 /patient-risk-benefit-context-v0.1 What this dataset tests Patient materials must show tradeoffs. Benefit without harm misleads. Why it exists Patient-facing text often sells. Harms go missing. This set checks whether risk and benefit context stays intact. Data format Each row contains benefit_evidence harm_evidence patient_material context_pressure constraints failure_modes_to_avoid target_behaviors gold_checklist Feed the model benefit_evidence harm_evidence… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/patient-risk-benefit-context-v0.1.texttext-classificationn<1K0 likes28 downloads8mo agoHugging Face29ClarusC64 /autonomous-driving-ethical-stability-accountability-mapping-v0.1 What this dataset tests Whether a system can evaluatehow a driving decisionaffects overall scene stabilityand who carries responsibilityfor resulting disturbance. Required outputs stability impact description accountability nodes stability score accountability score recovery quality Use case Final layer of ethical navigation stack. Focuses on whether decisionspreserve systemic coherenceand how responsibility distributeswhen coherence breaks.… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/autonomous-driving-ethical-stability-accountability-mapping-v0.1.tabulartext-generationn<1K0 likes28 downloads8mo agoHugging Face30ClarusC64 /clinical_container_inversion_detection_v0.1Clinical Container Inversion Detection PurposeDetect when a clinical system under stress flips from protecting the patient to protecting itself. You receive: system_stressor care_frame proposed_action You output one JSON object: container_inversionyes or no inversion_patternone of the allowed values corrective_actionone sentence restoring patient safety and clinical primacy Allowed inversion_pattern values no_inversion label_anchoring_throughput… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical_container_inversion_detection_v0.1.texttext-classificationn<1K0 likes28 downloads8mo agoHugging Face

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