| from __future__ import annotations |
| import json |
| import re |
| from typing import Any |
| COMMUNICATION_ROLE_NAMES = ['Forensic Scheduling Controller (FSC)'] |
| GOLD = {'conclusion': 'Exists', 'numeric_result': 8.0} |
| POSITIVE_CONCLUSION = 'Exists' |
| NEGATIVE_CONCLUSION = 'Does not exist' |
| NUMERIC_PRECISION = 2 |
| PUBLIC_ROLES = [{'role_name': 'Chief Forensic Metallurgist (CFM)', 'responsibility': 'Maintains high-resolution microscopy validation and microscopic resolution benchmarks', 'role_abbreviation': 'CFM'}, {'role_name': 'Evidence Custody Director (ECD)', 'responsibility': 'Maintains secure intake records and physical evidence chain-of-custody tracking', 'role_abbreviation': 'ECD'}, {'role_name': 'Hazardous Environments Safety Inspector (HES)', 'responsibility': 'Audits containment compliance and explosion-proof equipment standards for hazardous zones', 'role_abbreviation': 'HES'}, {'role_name': 'Regulatory Integrity Auditor (RIA)', 'responsibility': 'Monitors third-party technical oversight and independent verification accreditations', 'role_abbreviation': 'RIA'}, {'role_name': 'Diagnostic Laboratory Lead (DLL)', 'responsibility': 'Manages core analytical pipeline capacities and specimen allocation volumes', 'role_abbreviation': 'DLL'}, {'role_name': 'Forensic Scheduling Controller (FSC)', 'responsibility': 'Tracks bench testing schedules and allocated diagnostic operating windows', 'role_abbreviation': 'FSC'}, {'role_name': 'Public Inquest Liaison (PIL)', 'responsibility': 'Maintains external investigative stakeholder communication and inquiry tracking', 'role_abbreviation': 'PIL'}] |
| ROLE_KEY_PREFIXES = {'Chief Forensic Metallurgist (CFM)': 'CFM-', 'Evidence Custody Director (ECD)': 'ECD-', 'Hazardous Environments Safety Inspector (HES)': 'HES-', 'Regulatory Integrity Auditor (RIA)': 'RIA-', 'Diagnostic Laboratory Lead (DLL)': 'DLL-', 'Forensic Scheduling Controller (FSC)': 'FSC-', 'Public Inquest Liaison (PIL)': 'PIL-'} |
| ROLE_VALID_KEYS = {'Chief Forensic Metallurgist (CFM)': ['CFM-DIFF-KLW', 'CFM-DIFF-W8I', 'CFM-DIFF-DJP', 'CFM-MICR-TJV', 'CFM-MICR-9ZF', 'CFM-MICR-4P6'], 'Evidence Custody Director (ECD)': ['ECD-CUST-6PZ', 'ECD-CUST-U4F', 'ECD-CUST-MKJ', 'ECD-VAULT-KKN', 'ECD-VAULT-QUB', 'ECD-VAULT-TNI'], 'Hazardous Environments Safety Inspector (HES)': ['HES-ATEX-HKN', 'HES-ATEX-ZXJ', 'HES-ATEX-4OD', 'HES-ATEX-LI5', 'HES-VENT-9O5', 'HES-VENT-OBI', 'HES-VENT-O8F'], 'Regulatory Integrity Auditor (RIA)': ['RIA-ETHIC-4OC', 'RIA-ETHIC-XBM', 'RIA-ETHIC-YST', 'RIA-PEER-D59', 'RIA-PEER-AMF', 'RIA-PEER-JLK'], 'Diagnostic Laboratory Lead (DLL)': ['DLL-CLEAN-L53', 'DLL-CLEAN-8YN', 'DLL-CLEAN-6KH', 'DLL-SPEC-2D0', 'DLL-SPEC-ZWK', 'DLL-SPEC-8L8'], 'Forensic Scheduling Controller (FSC)': ['FSC-TIME-E06', 'FSC-TIME-0XS', 'FSC-TIME-E4T', 'FSC-SHIFT-QK4', 'FSC-SHIFT-UHI', 'FSC-SHIFT-T02'], 'Public Inquest Liaison (PIL)': ['PIL-MEDIA-USE', 'PIL-MEDIA-AYU', 'PIL-MEDIA-18J', 'PIL-COMM-THZ', 'PIL-COMM-OG9', 'PIL-COMM-AAX']} |
| RECORD_REPLY_KEYS = {'CFM-MICR-9ZF': ['CFM-MICR-9ZF'], 'CFM-MICR-4P6': ['CFM-MICR-4P6'], 'CFM-MICR-TJV': ['CFM-MICR-TJV'], 'ECD-CUST-U4F': ['ECD-CUST-U4F'], 'ECD-CUST-MKJ': ['ECD-CUST-MKJ'], 'ECD-CUST-6PZ': ['ECD-CUST-6PZ'], 'HES-ATEX-LI5': ['HES-ATEX-LI5'], 'HES-ATEX-ZXJ': ['HES-ATEX-ZXJ'], 'HES-ATEX-HKN': ['HES-ATEX-HKN'], 'RIA-PEER-JLK': ['RIA-PEER-JLK'], 'RIA-PEER-AMF': ['RIA-PEER-AMF'], 'RIA-PEER-D59': ['RIA-PEER-D59'], 'DLL-SPEC-8L8': ['DLL-SPEC-8L8'], 'DLL-SPEC-2D0': ['DLL-SPEC-2D0'], 'DLL-SPEC-ZWK': ['DLL-SPEC-ZWK'], 'FSC-TIME-0XS': ['FSC-TIME-0XS'], 'FSC-TIME-E4T': ['FSC-TIME-E4T'], 'FSC-TIME-E06': ['FSC-TIME-E06'], 'PIL-COMM-OG9': ['PIL-COMM-OG9'], 'PIL-COMM-THZ': ['PIL-COMM-THZ'], 'PIL-COMM-AAX': ['PIL-COMM-AAX'], 'CFM-DIFF-DJP': ['CFM-DIFF-DJP'], 'CFM-DIFF-KLW': ['CFM-DIFF-KLW'], 'CFM-DIFF-W8I': ['CFM-DIFF-W8I'], 'ECD-VAULT-TNI': ['ECD-VAULT-TNI'], 'ECD-VAULT-KKN': ['ECD-VAULT-KKN'], 'ECD-VAULT-QUB': ['ECD-VAULT-QUB'], 'HES-VENT-OBI': ['HES-VENT-OBI'], 'HES-VENT-9O5': ['HES-VENT-9O5'], 'HES-VENT-O8F': ['HES-VENT-O8F'], 'RIA-ETHIC-4OC': ['RIA-ETHIC-4OC'], 'RIA-ETHIC-XBM': ['RIA-ETHIC-XBM'], 'RIA-ETHIC-YST': ['RIA-ETHIC-YST'], 'DLL-CLEAN-8YN': ['DLL-CLEAN-8YN'], 'DLL-CLEAN-6KH': ['DLL-CLEAN-6KH'], 'DLL-CLEAN-L53': ['DLL-CLEAN-L53'], 'FSC-SHIFT-UHI': ['FSC-SHIFT-UHI'], 'FSC-SHIFT-T02': ['FSC-SHIFT-T02'], 'FSC-SHIFT-QK4': ['FSC-SHIFT-QK4'], 'PIL-MEDIA-18J': ['PIL-MEDIA-18J'], 'PIL-MEDIA-USE': ['PIL-MEDIA-USE'], 'PIL-MEDIA-AYU': ['PIL-MEDIA-AYU'], 'HES-ATEX-4OD': ['HES-ATEX-4OD']} |
| REQUIRED_KEYS = ['CFM-MICR-4P6', 'CFM-MICR-9ZF', 'DLL-SPEC-8L8', 'ECD-CUST-6PZ', 'ECD-CUST-U4F', 'FSC-TIME-0XS', 'HES-ATEX-4OD', 'RIA-PEER-JLK'] |
| DISTRACTOR_KEYS = ['PIL-COMM-AAX', 'PIL-COMM-OG9', 'PIL-COMM-THZ', 'PIL-MEDIA-18J', 'PIL-MEDIA-AYU', 'PIL-MEDIA-USE'] |
| SETTING = 'mixed' |
|
|
| def runtime_defaults() -> dict[str, Any]: |
| return { |
| "setting": SETTING, |
| "communication_role_names": list(COMMUNICATION_ROLE_NAMES), |
| "neglect_required_followups": 1, |
| "finalize_ideal_round": 4, |
| "max_turns": 7, |
| } |
|
|
| FINALIZE_IDEAL_ROUND = runtime_defaults()["finalize_ideal_round"] |
| DEFAULT_MAX_TURNS = runtime_defaults()["max_turns"] |
| ERROR_INJECTION = {'enabled': True, 'role_name': 'Hazardous Environments Safety Inspector (HES)', 'target_key': 'HES-ATEX-LI5', 'alternate_key': 'HES-ATEX-4OD', 'target_context': 'offshore production environment', 'alternate_context': 'onshore test environment'} |
| INCOMPLETE_INJECTION = {'record_id': 'rec_metallurgical_forensics_A', 'role_id': 'metallurgical_forensics', 'candidate_id': 'A', 'key': 'CFM-MICR-9ZF', 'boolean_predicate_id': 'A_sem_capability_enabled', 'boolean_label': 'Scanning electron microscopy capability enabled', 'hidden_predicate_id': 'A_electron_beam_spot_size', 'threshold_label': 'Electron beam spot resolution', 'threshold_aliases': ['Microscopy spot resolution', 'SEM beam spot diameter'], 'threshold_value': 3.0, 'threshold_unit': 'nm', 'partial_record_text': 'CFM-MICR-9ZF: Micro-computed tomography and coupon metallurgical protocol.Micro-computed tomography and coupon metallurgical protocol: Scanning electron microscopy capability enabled = true .Source: Laboratory instrument readiness audit; Scope: Designated failure analysis scanning suite; Measurement: Formal operational validation status of field-emission scanning electron microscope.', 'alternative_outputs': [{'conclusion': 'Does not exist', 'numeric_result': None, 'qualified_candidate_id': None}], 'role_name': 'Chief Forensic Metallurgist (CFM)', 'candidate_name': 'Micro-computed tomography and coupon metallurgical protocol', 'other_candidate_names': ['Full-scale hydrodynamic spool burst simulation protocol', 'In-situ automated ultrasonic backscatter mapping protocol'], 'context_value': None, 'other_context_values': [], 'language': 'en'} |
| INCOMPLETE_FOLLOWUP_RECORDS = [{'key': 'CFM-MICR-9ZF', 'role': 'Chief Forensic Metallurgist (CFM)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'sem_capability_enabled', 'label': 'Scanning electron microscopy capability enabled', 'aliases': ['SEM capability enablement', 'Electron microscopy readiness']}, {'id': 'electron_beam_spot_size', 'label': 'Electron beam spot resolution', 'aliases': ['Microscopy spot resolution', 'SEM beam spot diameter']}]}, {'key': 'CFM-MICR-4P6', 'role': 'Chief Forensic Metallurgist (CFM)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'sem_capability_enabled', 'label': 'Scanning electron microscopy capability enabled', 'aliases': ['SEM capability enablement', 'Electron microscopy readiness']}, {'id': 'electron_beam_spot_size', 'label': 'Electron beam spot resolution', 'aliases': ['Microscopy spot resolution', 'SEM beam spot diameter']}]}, {'key': 'CFM-MICR-TJV', 'role': 'Chief Forensic Metallurgist (CFM)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'sem_capability_enabled', 'label': 'Scanning electron microscopy capability enabled', 'aliases': ['SEM capability enablement', 'Electron microscopy readiness']}, {'id': 'electron_beam_spot_size', 'label': 'Electron beam spot resolution', 'aliases': ['Microscopy spot resolution', 'SEM beam spot diameter']}]}, {'key': 'ECD-CUST-U4F', 'role': 'Evidence Custody Director (ECD)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'biometric_custody_logged', 'label': 'Biometric chain-of-custody logging verified', 'aliases': ['Biometric custody verification', 'Chain-of-custody logging']}]}, {'key': 'ECD-CUST-MKJ', 'role': 'Evidence Custody Director (ECD)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'biometric_custody_logged', 'label': 'Biometric chain-of-custody logging verified', 'aliases': ['Biometric custody verification', 'Chain-of-custody logging']}]}, {'key': 'ECD-CUST-6PZ', 'role': 'Evidence Custody Director (ECD)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'biometric_custody_logged', 'label': 'Biometric chain-of-custody logging verified', 'aliases': ['Biometric custody verification', 'Chain-of-custody logging']}]}, {'key': 'HES-ATEX-LI5', 'role': 'Hazardous Environments Safety Inspector (HES)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': 'offshore production environment', 'fields': [{'id': 'zone1_containment_certified', 'label': 'Zone-1 explosive atmosphere containment certified', 'aliases': ['Zone-1 ATEX containment clearance', 'Explosive atmosphere containment certification']}]}, {'key': 'HES-ATEX-ZXJ', 'role': 'Hazardous Environments Safety Inspector (HES)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': 'offshore production environment', 'fields': [{'id': 'zone1_containment_certified', 'label': 'Zone-1 explosive atmosphere containment certified', 'aliases': ['Zone-1 ATEX containment clearance', 'Explosive atmosphere containment certification']}]}, {'key': 'HES-ATEX-HKN', 'role': 'Hazardous Environments Safety Inspector (HES)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': 'offshore production environment', 'fields': [{'id': 'zone1_containment_certified', 'label': 'Zone-1 explosive atmosphere containment certified', 'aliases': ['Zone-1 ATEX containment clearance', 'Explosive atmosphere containment certification']}]}, {'key': 'RIA-PEER-JLK', 'role': 'Regulatory Integrity Auditor (RIA)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'peer_review_cleared', 'label': 'Independent technical peer review cleared', 'aliases': ['Technical peer review clearance', 'Independent review body signoff']}]}, {'key': 'RIA-PEER-AMF', 'role': 'Regulatory Integrity Auditor (RIA)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'peer_review_cleared', 'label': 'Independent technical peer review cleared', 'aliases': ['Technical peer review clearance', 'Independent review body signoff']}]}, {'key': 'RIA-PEER-D59', 'role': 'Regulatory Integrity Auditor (RIA)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'peer_review_cleared', 'label': 'Independent technical peer review cleared', 'aliases': ['Technical peer review clearance', 'Independent review body signoff']}]}, {'key': 'DLL-SPEC-8L8', 'role': 'Diagnostic Laboratory Lead (DLL)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'gross_specimen_capacity', 'label': 'Gross diagnostic specimen capacity', 'aliases': ['Total specimen capacity', 'Diagnostic specimen throughput volume']}]}, {'key': 'DLL-SPEC-2D0', 'role': 'Diagnostic Laboratory Lead (DLL)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'gross_specimen_capacity', 'label': 'Gross diagnostic specimen capacity', 'aliases': ['Total specimen capacity', 'Diagnostic specimen throughput volume']}]}, {'key': 'DLL-SPEC-ZWK', 'role': 'Diagnostic Laboratory Lead (DLL)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'gross_specimen_capacity', 'label': 'Gross diagnostic specimen capacity', 'aliases': ['Total specimen capacity', 'Diagnostic specimen throughput volume']}]}, {'key': 'FSC-TIME-0XS', 'role': 'Forensic Scheduling Controller (FSC)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'allocated_run_time', 'label': 'Allocated laboratory run time', 'aliases': ['Testing campaign run duration', 'Allocated laboratory duration']}]}, {'key': 'FSC-TIME-E4T', 'role': 'Forensic Scheduling Controller (FSC)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'allocated_run_time', 'label': 'Allocated laboratory run time', 'aliases': ['Testing campaign run duration', 'Allocated laboratory duration']}]}, {'key': 'FSC-TIME-E06', 'role': 'Forensic Scheduling Controller (FSC)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'allocated_run_time', 'label': 'Allocated laboratory run time', 'aliases': ['Testing campaign run duration', 'Allocated laboratory duration']}]}, {'key': 'PIL-COMM-OG9', 'role': 'Public Inquest Liaison (PIL)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'inquest_briefing_index', 'label': 'Public inquiry briefing readiness index', 'aliases': ['Inquest briefing index', 'Inquiry reporting score']}]}, {'key': 'PIL-COMM-THZ', 'role': 'Public Inquest Liaison (PIL)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'inquest_briefing_index', 'label': 'Public inquiry briefing readiness index', 'aliases': ['Inquest briefing index', 'Inquiry reporting score']}]}, {'key': 'PIL-COMM-AAX', 'role': 'Public Inquest Liaison (PIL)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'inquest_briefing_index', 'label': 'Public inquiry briefing readiness index', 'aliases': ['Inquest briefing index', 'Inquiry reporting score']}]}, {'key': 'CFM-DIFF-DJP', 'role': 'Chief Forensic Metallurgist (CFM)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'supplemental_metallurgical_forensics', 'label': 'Residual stress diffractometer sample throughput', 'aliases': []}]}, {'key': 'CFM-DIFF-KLW', 'role': 'Chief Forensic Metallurgist (CFM)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'supplemental_metallurgical_forensics', 'label': 'Residual stress diffractometer sample throughput', 'aliases': []}]}, {'key': 'CFM-DIFF-W8I', 'role': 'Chief Forensic Metallurgist (CFM)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'supplemental_metallurgical_forensics', 'label': 'Residual stress diffractometer sample throughput', 'aliases': []}]}, {'key': 'ECD-VAULT-TNI', 'role': 'Evidence Custody Director (ECD)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'supplemental_evidence_custody', 'label': 'Cold-vault relative humidity', 'aliases': []}]}, {'key': 'ECD-VAULT-KKN', 'role': 'Evidence Custody Director (ECD)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'supplemental_evidence_custody', 'label': 'Cold-vault relative humidity', 'aliases': []}]}, {'key': 'ECD-VAULT-QUB', 'role': 'Evidence Custody Director (ECD)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'supplemental_evidence_custody', 'label': 'Cold-vault relative humidity', 'aliases': []}]}, {'key': 'HES-VENT-OBI', 'role': 'Hazardous Environments Safety Inspector (HES)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'supplemental_hazardous_safety', 'label': 'Enclosure purge exchange rate', 'aliases': []}]}, {'key': 'HES-VENT-9O5', 'role': 'Hazardous Environments Safety Inspector (HES)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'supplemental_hazardous_safety', 'label': 'Enclosure purge exchange rate', 'aliases': []}]}, {'key': 'HES-VENT-O8F', 'role': 'Hazardous Environments Safety Inspector (HES)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'supplemental_hazardous_safety', 'label': 'Enclosure purge exchange rate', 'aliases': []}]}, {'key': 'RIA-ETHIC-4OC', 'role': 'Regulatory Integrity Auditor (RIA)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'supplemental_regulatory_integrity', 'label': 'Audit conflict disclosure completion rate', 'aliases': []}]}, {'key': 'RIA-ETHIC-XBM', 'role': 'Regulatory Integrity Auditor (RIA)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'supplemental_regulatory_integrity', 'label': 'Audit conflict disclosure completion rate', 'aliases': []}]}, {'key': 'RIA-ETHIC-YST', 'role': 'Regulatory Integrity Auditor (RIA)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'supplemental_regulatory_integrity', 'label': 'Audit conflict disclosure completion rate', 'aliases': []}]}, {'key': 'DLL-CLEAN-8YN', 'role': 'Diagnostic Laboratory Lead (DLL)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'supplemental_laboratory_operations', 'label': 'Ultrasonic wash bath temperature', 'aliases': []}]}, {'key': 'DLL-CLEAN-6KH', 'role': 'Diagnostic Laboratory Lead (DLL)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'supplemental_laboratory_operations', 'label': 'Ultrasonic wash bath temperature', 'aliases': []}]}, {'key': 'DLL-CLEAN-L53', 'role': 'Diagnostic Laboratory Lead (DLL)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'supplemental_laboratory_operations', 'label': 'Ultrasonic wash bath temperature', 'aliases': []}]}, {'key': 'FSC-SHIFT-UHI', 'role': 'Forensic Scheduling Controller (FSC)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'supplemental_project_scheduling', 'label': 'Shift handover overlap duration', 'aliases': []}]}, {'key': 'FSC-SHIFT-T02', 'role': 'Forensic Scheduling Controller (FSC)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'supplemental_project_scheduling', 'label': 'Shift handover overlap duration', 'aliases': []}]}, {'key': 'FSC-SHIFT-QK4', 'role': 'Forensic Scheduling Controller (FSC)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'supplemental_project_scheduling', 'label': 'Shift handover overlap duration', 'aliases': []}]}, {'key': 'PIL-MEDIA-18J', 'role': 'Public Inquest Liaison (PIL)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': '', 'fields': [{'id': 'supplemental_inquest_liaison', 'label': 'Press release turnaround window', 'aliases': []}]}, {'key': 'PIL-MEDIA-USE', 'role': 'Public Inquest Liaison (PIL)', 'candidate': 'Full-scale hydrodynamic spool burst simulation protocol', 'context': '', 'fields': [{'id': 'supplemental_inquest_liaison', 'label': 'Press release turnaround window', 'aliases': []}]}, {'key': 'PIL-MEDIA-AYU', 'role': 'Public Inquest Liaison (PIL)', 'candidate': 'In-situ automated ultrasonic backscatter mapping protocol', 'context': '', 'fields': [{'id': 'supplemental_inquest_liaison', 'label': 'Press release turnaround window', 'aliases': []}]}, {'key': 'HES-ATEX-4OD', 'role': 'Hazardous Environments Safety Inspector (HES)', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'context': 'onshore test environment', 'fields': [{'id': 'zone1_containment_certified', 'label': 'Zone-1 explosive atmosphere containment certified', 'aliases': ['Zone-1 ATEX containment clearance', 'Explosive atmosphere containment certification']}]}] |
| KEY_PATTERN = re.compile(r"^[A-Z]+-[A-Z]+-[A-Z0-9]{3}$") |
| REPLY_KEY_PATTERN = re.compile(r"(?<![A-Z0-9])[A-Z]+-[A-Z]+-[A-Z0-9]{3}(?![A-Z0-9])", re.IGNORECASE) |
| ROLE_REPLY_MAX_ATTEMPTS = 3 |
| TASK_QUERY = 'Determine whether exactly one forensic investigation protocol satisfies all screening requirements: accredited scanning electron microscopy capability is enabled and electron beam spot resolution is at most 3.5 nm, biometric chain-of-custody logging is verified, Zone-1 explosive atmosphere containment is certified for the offshore production environment, and independent technical peer review clearance is granted. If exactly one forensic investigation protocol meets these criteria, report whether such a candidate Exists or Does not exist, and compute its analytical throughput rate by dividing gross diagnostic specimen capacity by allocated laboratory run time in specimens/d.\n\nNumeric calculation rule: Compute only for the unique eligible candidate: [Gross diagnostic specimen capacity] / [Allocated laboratory run time] (direct ratio; do not multiply by 100; denominator must not be zero). Result unit: specimens/d. Round only the final result to 2 decimal places (ties to even). If no candidate qualifies, numeric_result is null.\n\nCandidates and records to verify:\n- In-situ automated ultrasonic backscatter mapping protocol：CFM-MICR-TJV、ECD-CUST-6PZ、HES-ATEX-HKN、RIA-PEER-D59、DLL-SPEC-ZWK、FSC-TIME-E06、PIL-COMM-AAX\n- Full-scale hydrodynamic spool burst simulation protocol：CFM-MICR-4P6、ECD-CUST-MKJ、HES-ATEX-ZXJ、RIA-PEER-AMF、DLL-SPEC-2D0、FSC-TIME-E4T、PIL-COMM-THZ\n- Micro-computed tomography and coupon metallurgical protocol：CFM-MICR-9ZF、ECD-CUST-U4F、HES-ATEX-4OD、RIA-PEER-JLK、DLL-SPEC-8L8、FSC-TIME-0XS、PIL-COMM-OG9' |
| TASK_CONTEXT = 'Following an offshore subsea pipeline failure, forensic engineering teams are evaluating candidate failure investigation protocols to ensure rigorous diagnostic capabilities, strict chain-of-custody, and hazardous environment readiness. Protocols are assessed across laboratory analytical standards, evidence security procedures, explosion containment safety certifications, and independent verification reviews to select an authorized investigation campaign.' |
|
|
| """Parse one model message without treating trailing generation noise as an action.""" |
| import json |
| import re |
| from typing import Any |
|
|
|
|
| def parse_json_message(content: Any, *, trace: dict[str, Any] | None = None) -> dict[str, Any]: |
| if trace is not None: |
| trace.clear() |
| trace.update(policy="first_complete_object", raw_response=content, recovered=False) |
| if not isinstance(content, str): |
| raise ValueError("model response must be text") |
| text = content.strip().lstrip("\ufeff").strip() |
| |
| try: |
| value, end = json.JSONDecoder().raw_decode(text) |
| start = 0 |
| except json.JSONDecodeError: |
| if text.startswith(("{", "[", '"')): |
| raise |
| opening = re.search(r"[\{\[]", text) |
| if opening is None: |
| raise |
| start = opening.start() |
| value, consumed = json.JSONDecoder().raw_decode(text[start:]) |
| end = start + consumed |
| if not isinstance(value, dict): |
| raise ValueError("model response must be a JSON object") |
| prefix, suffix = text[:start], text[end:] |
| if trace is not None: |
| trace.update(recovered=bool(prefix.strip() or suffix.strip()), |
| ignored_prefix=prefix, ignored_suffix=suffix) |
| return value |
|
|
|
|
| def expand_question_keys(questions): |
| expanded = [] |
| for question in questions: |
| if not isinstance(question, dict): |
| expanded.append(question) |
| continue |
| text = question.get("question") |
| keys = list(dict.fromkeys(REPLY_KEY_PATTERN.findall(text))) if isinstance(text, str) else [] |
| for key in keys or [None]: |
| expanded.append(dict(question, key=key)) |
| return expanded |
|
|
|
|
| def validate_question(question, *, role_name, semantic_enabled=False): |
| if not isinstance(question, dict) or not isinstance(question.get("question"), str) or not question["question"].strip(): |
| raise ValueError("question must contain natural-language text") |
| if role_name not in ROLE_KEY_PREFIXES: |
| raise ValueError("unknown Role") |
| key = question.get("key") |
| authorized = semantic_enabled if key is None else ( |
| isinstance(key, str) and key in ROLE_VALID_KEYS[role_name] and key in question["question"]) |
| return {"authorized": bool(authorized)} |
|
|
| def recovery_key_match(answer: str) -> str | None: |
| target = ERROR_INJECTION.get("target_key") |
| if not target: |
| return None |
| def contains(key: str) -> bool: |
| return bool(re.search(r"(?<![A-Z0-9-])" + re.escape(key) + r"(?![A-Z0-9-])", answer, re.IGNORECASE)) |
| if contains(target): |
| return "full_key" |
| parts = target.split("-") |
| alias = parts[0] + "-" + parts[-1] |
| matches = [key for key in ROLE_VALID_KEYS.get(ERROR_INJECTION.get("role_name"), []) |
| if key.split("-")[0] + "-" + key.split("-")[-1] == alias] |
| if matches == [target] and contains(alias): |
| return "abbreviated_key" |
| return None |
|
|
| def _key_communication_metrics(transcript: list[dict[str, Any]]) -> dict[str, Any]: |
| key_query_rounds: dict[str, int] = {} |
| key_query_counts: dict[str, int] = {} |
| queried_roles: set[str] = set() |
| mismatch_round: int | None = None |
| recovery_query_sent = False |
| recovery_query_turn: int | None = None |
| target_key_returned = False |
| recovery_turn: int | None = None |
| recovery_method: str | None = None |
| recovery_reference: str | None = None |
| target_key_query_turn: int | None = None |
| finalized_before_recovery = False |
| for event in transcript: |
| turn = int(event.get("turn", len(transcript))) |
| action = event.get("main_agent_action", {}) |
| if ( |
| ERROR_INJECTION.get("enabled") |
| and action.get("action") == "finalize" |
| and not target_key_returned |
| ): |
| finalized_before_recovery = True |
| if action.get("action") != "ask_role_agents": continue |
| for answer in event.get("role_answers", []): |
| role_name = answer.get("role_agent") |
| if role_name: queried_roles.add(role_name) |
| if ( |
| ERROR_INJECTION.get("enabled") and mismatch_round is not None and turn > mismatch_round |
| and role_name == ERROR_INJECTION.get("role_name") |
| and answer.get("question", {}).get("key") == ERROR_INJECTION.get("target_key") |
| and "role_trace" in answer |
| and recovery_key_match(str(answer.get("answer", ""))) is not None |
| ): |
| target_key_query_turn = target_key_query_turn or turn |
| if ( |
| ERROR_INJECTION.get("enabled") |
| and role_name == ERROR_INJECTION.get("role_name") |
| and answer.get("question", {}).get("key") == ERROR_INJECTION.get("alternate_key") |
| and "role_trace" in answer |
| ): |
| mismatch_round = mismatch_round or turn |
| for message in action.get("messages", []): |
| for question in message.get("questions", []): |
| key = question.get("key") |
| if ( |
| ERROR_INJECTION.get("enabled") |
| and resolve_role_name(message.get("role_agent")) == ERROR_INJECTION.get("role_name") |
| and (key is None or key == ERROR_INJECTION.get("target_key")) |
| and mismatch_round is not None |
| and turn > mismatch_round |
| ): |
| recovery_query_sent = True |
| recovery_query_turn = recovery_query_turn or turn |
| if not isinstance(key, str) or not KEY_PATTERN.fullmatch(key): continue |
| key_query_rounds.setdefault(key, turn) |
| key_query_counts[key] = key_query_counts.get(key, 0) + 1 |
| if recovery_query_sent and not target_key_returned and mismatch_round is not None and turn > mismatch_round: |
| for answer in event.get("role_answers", []): |
| question = answer.get("question", {}) |
| if ( |
| answer.get("role_agent") == ERROR_INJECTION.get("role_name") |
| and (question.get("key") is None or question.get("key") == ERROR_INJECTION.get("target_key")) |
| and "role_trace" in answer |
| and recovery_key_match(str(answer.get("answer", ""))) is not None |
| ): |
| target_key_returned = True |
| recovery_reference = recovery_key_match(str(answer.get("answer", ""))) |
| recovery_turn = turn |
| recovery_method = "keyless_query" if question.get("key") is None else "target_key_query" |
| break |
| queried = set(key_query_counts) |
| required = set(REQUIRED_KEYS) |
| relevant = queried & required |
| return { |
| "required_key_recall":len(relevant)/len(required) if required else 1.0, |
| "required_key_precision":len(relevant)/len(queried) if queried else 0.0, |
| "key_query_rounds":key_query_rounds, |
| "key_query_counts":key_query_counts, |
| "duplicate_key_queries":sum(max(0, count-1) for count in key_query_counts.values()), |
| "distractor_key_queries":sum(key_query_counts.get(key, 0) for key in DISTRACTOR_KEYS), |
| "distractor_key_query_rate":sum(key_query_counts.get(key, 0) for key in DISTRACTOR_KEYS)/sum(key_query_counts.values()) if key_query_counts else 0.0, |
| "turns_to_finalize":len(transcript), |
| "mismatch_key_queried":mismatch_round is not None, |
| "mismatch_key_turn":mismatch_round, |
| "recovery_query_sent":recovery_query_sent, |
| "recovery_query_turn":recovery_query_turn, |
| "target_key_returned":target_key_returned, |
| "recovery_turn":recovery_turn, |
| "recovery_method":recovery_method, |
| "target_key_query_turn":target_key_query_turn, |
| "recovery_key_match":recovery_reference, |
| "recovery_metrics_version":"keyed_and_keyless_v3", |
| "recovery_latency":recovery_turn - mismatch_round if recovery_turn is not None and mismatch_round is not None else None, |
| "finalized_before_recovery":finalized_before_recovery, |
| } |
|
|
| def main_system_prompt() -> str: |
| role_text = "\n".join(f"- {item['role_name']}: {item['responsibility']}" for item in PUBLIC_ROLES) |
| ask_example = json.dumps({"action":"ask_role_agents","messages":[{"role_agent":"Role name","questions":[{"id":"Q1","question":"Please provide the record for ABC-DATA-A1B using the supplied key."}]}]}, ensure_ascii=False) |
| final_example = json.dumps({"action":"finalize","conclusion":"Exactly: " + POSITIVE_CONCLUSION + " or " + NEGATIVE_CONCLUSION,"numeric_result":None}, ensure_ascii=False).replace('"numeric_result": null', '"numeric_result": null/number') |
| communication_protocol = ('You may output {"action":"wait"} to advance one logical turn. An empty observation {"observations":[]} means no new visible messages; it does not imply receipt, processing or any future reply.' if SETTING in {'missed', 'mixed'} else '') |
| followup = ('For keyless follow-ups, name the candidate in full and the metric explicitly, or request that candidate\'s complete records. A unique previously queried record can be checked directly; otherwise supply a full key and metric when asked to clarify. Explicit directory requests return the complete directory.' if SETTING == 'incomplete' else |
| 'Authorized keyless follow-ups may name the candidate and metric or request complete records. A role may retrieve facts, request clarification or return a directory. If given a directory, select a valid key to obtain facts.' if SETTING == 'mixed' else |
| 'Authorized keyless questions return that role\'s complete directory. Choose a key from the directory to obtain concrete facts.') |
| return "\n\n".join([ |
| 'You are the Main Agent. Integrate information from independent role agents and make the final decision. Communicate in English.', |
| 'You cannot see private role information directly. Obtain it only by explicitly messaging the role agents.', |
| 'Key protocol:\n1. Candidate names and record keys are supplied in the task. Initial access requires a valid key; authorized later follow-ups are the exception.\n' |
| '2. Keys use ROLE-BUSINESS-XXX: role abbreviation, business abbreviation and three random alphanumeric characters. Use complete supplied keys; do not invent them.\n' |
| '3. Send each key to its owning business role. Use valid keys supplied by roles for subsequent checks.\n' |
| '4. Do not output a separate key field. Put the complete key in question text; the system extracts it and checks ownership. Separate different keys into individual questions, preferably one record per question.\n' |
| '5. After a successful keyed query to a role, keyless natural-language follow-ups to that role are authorized starting from the next turn.\n6. ' + followup, |
| 'Available independent role agents:\n\n' + role_text, |
| 'To ask questions, output only JSON in this form:\n\n' + ask_example, |
| *([communication_protocol] if communication_protocol else []), |
| 'Final answer format (output only valid JSON):\n\n' + final_example, |
| 'Use exactly one of these conclusion labels: ' + POSITIVE_CONCLUSION + ' or ' + NEGATIVE_CONCLUSION + '. ' |
| 'Do not append a candidate name, explanation or other text. null/number means choose one, not literal syntax. ' |
| 'For a positive conclusion provide the computed number; for a negative conclusion use null. Do not include units or explanations in numeric_result.', |
| ]) |
|
|
|
|
| ERROR_RECOVERY_PROFILE = {'role_name': 'Hazardous Environments Safety Inspector (HES)', 'target_key': 'HES-ATEX-LI5', 'alternate_key': 'HES-ATEX-4OD', 'target_context': 'offshore production environment', 'alternate_context': 'onshore test environment', 'candidate': 'Micro-computed tomography and coupon metallurgical protocol', 'other_candidates': ['Full-scale hydrodynamic spool burst simulation protocol', 'In-situ automated ultrasonic backscatter mapping protocol'], 'numeric': [], 'boolean': [{'topic': 'zone1_containment_certified', 'value': True, 'label': 'Zone-1 explosive atmosphere containment certified', 'aliases': ['Zone-1 ATEX containment clearance', 'Explosive atmosphere containment certification'], 'unit': ''}], 'language': 'en', 'facts': [{'topic': 'sem_capability_enabled', 'label': 'Scanning electron microscopy capability enabled', 'aliases': ['SEM capability enablement', 'Electron microscopy readiness']}, {'topic': 'electron_beam_spot_size', 'label': 'Electron beam spot resolution', 'aliases': ['Microscopy spot resolution', 'SEM beam spot diameter']}, {'topic': 'biometric_custody_logged', 'label': 'Biometric chain-of-custody logging verified', 'aliases': ['Biometric custody verification', 'Chain-of-custody logging']}, {'topic': 'zone1_containment_certified', 'label': 'Zone-1 explosive atmosphere containment certified', 'aliases': ['Zone-1 ATEX containment clearance', 'Explosive atmosphere containment certification']}, {'topic': 'peer_review_cleared', 'label': 'Independent technical peer review cleared', 'aliases': ['Technical peer review clearance', 'Independent review body signoff']}, {'topic': 'gross_specimen_capacity', 'label': 'Gross diagnostic specimen capacity', 'aliases': ['Total specimen capacity', 'Diagnostic specimen throughput volume']}, {'topic': 'allocated_run_time', 'label': 'Allocated laboratory run time', 'aliases': ['Testing campaign run duration', 'Allocated laboratory duration']}]} |
| """Conservative English evidence rules shared by standalone generated runtimes. |
| |
| This is attribution of explicit statements, not arbitrary semantic entailment. |
| Unsupported or conflicting statements remain unknown; no model calls are made. |
| """ |
| import re |
| from decimal import Decimal |
|
|
| _RECOVERY_NUMBER = r'[+-]?(?:\d+(?:\.\d+)?|\.\d+)(?:[eE][+-]?\d+)?' |
| _RECOVERY_UNCERTAIN = r"\b(?:unknown|uncertain|unclear|may(?!\s+\d{1,4}\b)|might|could|should|would|must|expected|possibly|whether|cannot|unable|unconfirmed|approximately|about|around|roughly|estimated|not confirmed|not known|not established)\b" |
| _RECOVERY_NEGATIVE = r'\b(?:not|no|never|false|invalid|blocked|failed|denied|unavailable|unapproved|revoked|inactive|disabled|noncompliant)\b' |
|
|
|
|
| def evidence_text(text): |
| text = text.replace('’', "'").replace('−', '-').replace('‑', '-') |
| text = re.sub(r"\bcan't\b", 'cannot', text, flags=re.I) |
| text = re.sub(r"n't\b", ' not', text, flags=re.I) |
| text = re.sub(r'(?<![\w.,])[-+]?\d{1,3}(?:,\d{3})+(?:\.\d+)?(?![\d,]|\.\d)', |
| lambda m: m[0].replace(',', ''), text) |
| return text |
|
|
|
|
| def evidence_phrase(text, phrase): |
| if not phrase: |
| return False |
| return bool(re.search(r'(?<!\w)' + re.escape(phrase).replace(r'\ ', r'\s+') + r'(?!\w)', text, re.I)) |
|
|
|
|
| def evidence_nonasserted(text): |
| """Questions, hypotheses and unconfirmed claims are not returned facts.""" |
| return bool('?' in text or re.search(_RECOVERY_UNCERTAIN, text, re.I) |
| or re.search(r'\b(?:if|unless|assuming|suppose|supposing|hypothetically)\b', text, re.I) |
| or re.search(r'\b(?:do|does|did|has|have|had)\s+not\s+(?:confirm|verify|establish)(?:ed)?\b', text, re.I)) |
|
|
|
|
| def evidence_clauses(text): |
| |
| sentences = re.split(r'[。\n]|\.(?!\d)|(?<=[!?])\s+', text) |
| clauses = [] |
| for sentence in sentences: |
| if evidence_nonasserted(sentence): |
| clauses.append(sentence) |
| else: |
| |
| predicate = r'\b(?:is|are|was|were|has|have|had|holds?|received|maintains?|equals?)\b|[:=]' |
| parts = re.split(r'([;；]|,?\s+\b(?:but|and)\b\s+(?![+-]?\d))', sentence, flags=re.I) |
| current = parts[0] |
| for separator, following in zip(parts[1::2], parts[2::2]): |
| continuation = re.match(r'(?:it|its|this|that|(?:the\s+)?status)\b', following, re.I) |
| split = not continuation and (not re.search(r'\band\b', separator, re.I) or |
| re.search(predicate, current, re.I) and re.search(predicate, following, re.I)) |
| if split: |
| clauses.append(current) |
| current = following |
| else: |
| current += separator + following |
| clauses.append(current) |
| return clauses |
|
|
|
|
| def evidence_scope(answer, question, profile): |
| for target in [profile.get('candidate', ''), profile.get('target_context', '')]: |
| if target and re.search(r'\b(?:not|outside|excluding|except)(?:\s+(?:for|the))?\s+' + re.escape(target) + r'(?!\w)', answer, re.I): |
| return 'not_returned' |
| candidates = profile.get('other_candidates', []) |
| contexts = profile.get('other_contexts', [profile.get('alternate_context', '')]) |
| if any(evidence_phrase(answer, c) for c in [*candidates, *contexts] if c): |
| target_also_present = evidence_phrase(answer, profile.get('candidate', '')) and ( |
| not profile.get('target_context') or evidence_phrase(answer, profile['target_context'])) |
| return 'unknown' if target_also_present else 'not_returned' |
| combined = question.get('question', '') + '\n' + answer |
| target_key = profile.get('target_key', '') |
| if target_key and (question.get('key') == target_key or evidence_phrase(combined, target_key)): |
| return 'eligible' |
| if evidence_phrase(combined, profile.get('candidate', '')) and ( |
| not profile.get('target_context') or evidence_phrase(combined, profile['target_context'])): |
| return 'eligible' |
| return 'unknown' |
|
|
|
|
| def evidence_record_text(answer, profile): |
| """Isolate an explicitly keyed record in a multi-record reply. |
| |
| A directory can contain facts as well as keys. Only the target key's |
| segment is eligible; neighboring records never supply its evidence. |
| Unstructured multi-candidate prose remains conservatively unscored. |
| """ |
| keys = list(re.finditer(r'(?<!\w)[A-Z]{2,10}-[A-Z]{2,10}-[A-Z0-9]{3}(?!\w)', answer)) |
| target = profile.get('target_key') |
| if not target or not any(m[0] == target for m in keys): |
| return answer |
| pieces = [] |
| for i, match in enumerate(keys): |
| if match[0] == target: |
| end = keys[i+1].start() if i+1 < len(keys) else len(answer) |
| pieces.append(answer[match.start():end]) |
| return '\n'.join(pieces) |
|
|
|
|
| def evidence_key_request_only(answer): |
| """An explicit request for a key supplies no factual recovery evidence.""" |
| return bool(re.fullmatch(r'\s*(?:please\s+(?:provide|supply)|I\s+need)\s+(?:a\s+)?valid\s+key\b[^\n.;]*[.!]?\s*', answer, re.I)) |
|
|
|
|
| def evidence_unit_pattern(unit): |
| |
| |
| parts = re.split(r'\s*/\s*|\s+per\s+', unit) |
| if len(parts) == 2 and all(parts): |
| return evidence_unit_pattern(parts[0]) + r'\s*(?:/|(?i:per))\s*' + evidence_unit_pattern(parts[1]) |
| groups = [('h', 'hour', 'hours'), ('min', 'minute', 'minutes'), ('s', 'second', 'seconds'), |
| ('kg', 'kilogram', 'kilograms'), ('m', 'meter', 'meters', 'metre', 'metres'), |
| ('d', 'day', 'days'), ('L', 'liter', 'liters', 'litre', 'litres'), |
| ('%', 'percent', 'per cent'), ('instances', 'instance'), ('items', 'item'), |
| ('people', 'person', 'persons'), ('days', 'day')] |
| for group in groups: |
| if unit in group: |
| canonical, *words = group |
| if canonical in {'instances', 'items', 'people', 'days'}: |
| words.append(canonical) |
| canonical = None |
| parts = ([re.escape(canonical)] if canonical else []) + ['(?i:' + re.escape(w) + ')' for w in words] |
| if unit == '%': parts.append('％') |
| return '(?:' + '|'.join(sorted(parts, key=len, reverse=True)) + ')' |
| if re.fullmatch(r'[a-z]+(?:-[a-z]+)+', unit): |
| prefix, suffix = unit.rsplit('-', 1) |
| return re.escape(prefix + '-') + evidence_unit_pattern(suffix) |
| if re.fullmatch(r'[a-z]{3,}', unit) and unit not in {'mol', 'rad', 'kat', 'lux'}: |
| singular = unit |
| if unit.endswith('ies'): |
| singular = unit[:-3] + 'y' |
| elif re.search(r'(?:ch|sh|ss|x|z)es$', unit): |
| singular = unit[:-2] |
| elif unit.endswith('s') and not unit.endswith(('ss', 'us', 'is')): |
| singular = unit[:-1] |
| plural = (singular[:-1] + 'ies' if re.search(r'[^aeiou]y$', singular) else |
| singular + 'es' if re.search(r'(?:s|x|z|ch|sh)$', singular) else singular + 's') |
| return '(?i:' + '|'.join(re.escape(w) for w in sorted({unit, singular, plural}, key=len, reverse=True)) + ')' |
| return re.escape(unit) |
|
|
|
|
| def evidence_measurements(text, unit): |
| text = evidence_text(text) |
| text = re.sub(r'(?<=\d)[-–—](?=\d)', ' to ', text) |
| suffix = (r'(?:\s+(?:1|(?i:dimensionless)))?' if unit == '1' |
| else r'\s*' + evidence_unit_pattern(unit)) |
| pattern = r'(?<![A-Za-z0-9_.,+\-])(' + _RECOVERY_NUMBER + r')' + suffix + r'(?![A-Za-z0-9_/%]|\.\d|,\d)' |
| return text, list(re.finditer(pattern, text)) |
|
|
|
|
| def evidence_exact_measurement(text, match): |
| before, after = text[max(0, match.start()-60):match.start()], text[match.end():match.end()+45] |
| before = re.sub(r'\bamounts?\s+to\s*$', 'equals ', before, flags=re.I) |
| if evidence_nonasserted(text): |
| return False |
| if re.search(r'(?:\b(?:under|over|below|above|less than|more than|at most|at least|up to|between|to|and|not|no more than|no less than)|不超过|不低于|小于等于|大于等于|小于|大于|至少|至多|最多|最少|低于|高于|介于|至|到|[<>≤≥～~–—])\s*$', before, re.I): |
| return False |
| if re.search(r'\d\s*[-–—]\s*$', before): |
| return False |
| if re.match(r'\s*(?:\b(?:maximum|minimum|max|min)\b|\bor (?:less|more|lower|higher)\b|\b(?:to|through)\s+[+-]?\d|[-–—~～]\s*\d)', after, re.I): |
| return False |
| return True |
|
|
|
|
| def english_exact_value(answer, value, unit): |
| for clause in evidence_clauses(evidence_text(answer)): |
| text, matches = evidence_measurements(clause, unit) |
| if any(Decimal(m[1]) == Decimal(str(value)) and evidence_exact_measurement(text, m) for m in matches): |
| return True |
| return False |
|
|
|
|
| def numeric_evidence(answer, question, profile, fact): |
| if evidence_key_request_only(answer): |
| return 'not_returned' |
| answer = evidence_text(evidence_record_text(answer, profile)) |
| scope = evidence_scope(answer, question, profile) |
| if scope != 'eligible': |
| return scope |
| labels = [fact.get('label', ''), *fact.get('aliases', [])] |
| states = set() |
| for clause in metric_assertions(answer, profile, fact): |
| |
| if not any(evidence_phrase(clause, label) for label in labels if label): |
| continue |
| if evidence_nonasserted(clause) or re.search(r'\b(?:catalog|directory)\b', clause, re.I): |
| continue |
| |
| others = profile.get('facts', profile.get('numeric', []) + profile.get('boolean', [])) |
| if any(other.get('topic') != fact.get('topic') and any(evidence_phrase(clause, label) for label in |
| [other.get('label', ''), *other.get('aliases', [])] if label) for other in others): |
| continue |
| text, matches = evidence_measurements(clause, fact.get('unit', '')) |
| |
| unit = fact.get('unit', '') |
| if re.fullmatch(r'[a-z]{3,}', unit): |
| for label in labels: |
| if re.search(r'\b' + evidence_unit_pattern(unit) + r'$', label): |
| pattern = r'(?<![\w.,+-])(' + _RECOVERY_NUMBER + r')\s+' + re.escape(label).replace(r'\ ', r'\s+') + r'(?!\w)' |
| matches += list(re.finditer(pattern, text, re.I)) |
| for m in matches: |
| if evidence_exact_measurement(text, m) and not re.search(_RECOVERY_NEGATIVE, text[:m.start()], re.I): |
| states.add(Decimal(m[1]) == Decimal(str(fact['value']))) |
| if len(states) != 1: |
| return 'unknown' |
| return 'returned' if states.pop() else 'not_returned' |
|
|
|
|
| def _evidence_predicate(text): |
| text = re.sub(r'\b(?:is|are|was|were|has|have|had|been|the|a|an|already)\b', ' ', text.casefold()) |
| return re.sub(r'\s+', ' ', text).strip(' .:') |
|
|
|
|
| _BOOLEAN_PREDICATE = r'\b(?:enabled|issued|approved|authorized|isolated|certified|insured|assigned|cleared|present|valid|clear|ready|open|active|completed|available|installed|registered|signed|granted|confirmed|verified|operational|compliant|meets|passed|released|equipped|connected|running|accepted)\b' |
|
|
| |
| _POSSESSION = r'(?:has|have|had|holds?|received|obtained|secured|possesses|provides|provided|maintains|maintained|achieved|satisfies|demonstrates|demonstrated|confirms?|confirmed|passed|equipped\s+with)' |
| _ACQUISITION = r'(?:obtained|received|secured|achieved|maintained|held|provided)' |
|
|
|
|
| def protected_metrics(text, profile, fact): |
| """Mask longest metric spans before any punctuation/conjunction splitting. |
| |
| A full label owns overlapping aliases. Preserve punctuation inside labels |
| (including Oxford commas) and never infer a metric from its gold value. |
| """ |
| facts = [fact, *profile.get('facts', profile.get('numeric', []) + profile.get('boolean', []))] |
| labels = set() |
| for item in facts: |
| for label in [item.get('label', ''), *item.get('aliases', [])]: |
| if label: |
| labels.add(label) |
| labels.update(_boolean_labels(label)) |
| if not labels: |
| return text, {} |
| pattern = r'(?<!\w)(?:' + '|'.join(re.escape(s).replace(r'\ ', r'\s+') |
| for s in sorted(labels, key=len, reverse=True)) + r')(?!\w)' |
| spans = {} |
| def mask(match): |
| token = f'ZZMETRIC{len(spans)}ZZ' |
| spans[token] = match[0] |
| return token |
| return re.sub(pattern, mask, text, flags=re.I), spans |
|
|
|
|
| def restore_metrics(text, spans): |
| return re.sub(r'ZZMETRIC\d+ZZ', lambda m: spans.get(m[0], m[0]), text) |
|
|
|
|
| def metric_assertions(answer, profile, fact): |
| """Bind metrics before splitting prose; leave ambiguous joint lists unknown. |
| |
| Split between anchored metrics only after a completed assertion, using the |
| last connector before the next metric. This supports values before labels, |
| natural verbs and possessive subjects without splitting noun phrases. |
| Sentence-level uncertainty remains attached to all its claims. |
| """ |
| masked, spans = protected_metrics(answer, profile, fact) |
| for sentence in re.split(r'[。\n]|\.(?!\d)|(?<=[!?])\s+', masked): |
| if evidence_nonasserted(sentence): |
| yield restore_metrics(sentence, spans) |
| continue |
| anchors = list(re.finditer(r'ZZMETRIC\d+ZZ', sentence)) |
| cuts = [] |
| for left, right in zip(anchors, anchors[1:]): |
| gap = sentence[left.end():right.start()] |
| separators = list(re.finditer(r'[;；]|,\s*|,?\s+\b(?:and|but|while|whereas)\b\s+', gap, re.I)) |
| if not separators: |
| continue |
| sep = separators[-1] |
| prior = sentence[cuts[-1] if cuts else 0:left.end()+sep.start()] |
| assertion = re.search(r'\b(?:is|are|was|were|has|have|had|measured|equals?|reached|achieves)\b|[:=]|(?<![A-Z])\d', |
| re.sub(r'ZZMETRIC\d+ZZ', '', prior), re.I) |
| if assertion or re.search(r'\b' + _POSSESSION + r'\b', prior, re.I): |
| cuts.append(left.end()+sep.end()) |
| start = 0 |
| for end in [*cuts, len(sentence)]: |
| for clause in evidence_clauses(sentence[start:end]): |
| yield restore_metrics(clause, spans) |
| start = end |
|
|
|
|
| def _boolean_labels(label): |
| """Allow a participle before or after its noun phrase, preserving its words.""" |
| phrase = _evidence_predicate(label) |
| variants = [phrase] |
| words = phrase.split() |
| if len(words) > 1 and re.fullmatch(_BOOLEAN_PREDICATE, words[-1]): |
| variants.append(' '.join([words[-1], *words[:-1]])) |
| if words[-1] == 'valid': |
| variants.append(' '.join(['valid', 'unrestricted', *words[:-1]])) |
| return variants |
|
|
|
|
| def _boolean_clauses(answer, profile): |
| for clause in metric_assertions(answer, profile, profile): |
| if evidence_nonasserted(clause): |
| yield clause |
| continue |
| |
| masked, spans = protected_metrics(clause, profile, profile) |
| for part in re.split( |
| r',(?!\s*(?:but|and|is|are|was|were|has|have|had|which|that|it|its|status|not|no|false|true|yes|confirmed)\b)\s*', |
| masked, flags=re.I): |
| yield restore_metrics(part, spans) |
|
|
|
|
| def boolean_evidence(answer, question, profile): |
| if evidence_key_request_only(answer): |
| return 'not_returned' |
| answer = evidence_text(evidence_record_text(answer, profile)) |
| scope = evidence_scope(answer, question, profile) |
| if scope != 'eligible': |
| return scope |
| |
| for adjective, noun in [('approved', 'approval'), ('authorized', 'authorization')]: |
| label = profile['label'] |
| if label.casefold().endswith(' ' + adjective): |
| subject = label[:-(len(adjective)+1)] |
| pattern = r'\b(?:obtained|received|secured)\s+(?:formal\s+)?' + noun + r'\s+for\s+(?:(?:its|the)\s+)?' + re.escape(subject) |
| answer = re.sub(pattern, subject + ' is ' + adjective, answer, flags=re.I) |
| labels = [variant for label in [profile['label'], *profile.get('aliases', [])] |
| for variant in _boolean_labels(label)] |
| primary = _boolean_labels(profile['label']) |
| states = set() |
| for clause in _boolean_clauses(answer, profile): |
| plain = _evidence_predicate(clause) |
| no_negative = _evidence_predicate(re.sub(_RECOVERY_NEGATIVE, ' ', clause, flags=re.I)) |
| assertion = re.sub(r'\b(?:the|a|an|is|are|was|were|been|already)\b', ' ', |
| re.sub(_RECOVERY_NEGATIVE, ' ', clause, flags=re.I), flags=re.I) |
| assertion = re.sub(r'\s+', ' ', assertion).strip() |
| if not any(evidence_phrase(plain, _evidence_predicate(label)) or evidence_phrase(no_negative, _evidence_predicate(label)) for label in labels if label): |
| continue |
| if evidence_nonasserted(clause) or re.search(r'\b(?:catalog|directory)\b', clause, re.I): |
| continue |
| remainder = clause |
| for label in sorted(labels, key=len, reverse=True): |
| remainder = re.sub(re.escape(label), '', remainder, flags=re.I) |
| negative = bool(re.search(_RECOVERY_NEGATIVE, remainder, re.I)) |
| explicit = bool(re.search(r'\b(?:true|yes|false|no)\b', remainder, re.I)) |
| label_match = any(evidence_phrase(no_negative, label) for label in primary) |
| affirmative = label_match and bool(re.search('(?:' + _BOOLEAN_PREDICATE + r')\s*$', profile['label'], re.I)) |
| |
| for label in primary: |
| escaped = re.escape(label).replace(r'\ ', r'\s+') |
| affirmative |= bool(re.search(r'\b' + _POSSESSION + r'\s+(?:that\s+)?(?:(?:valid|active|formal|operational|continuous|dedicated)\s+)*' + escaped + r'(?!\w)', assertion, re.I)) |
| affirmative |= bool(re.search(r'(?<!\w)' + escaped + r'\s+(?:' + _BOOLEAN_PREDICATE + '|' + _ACQUISITION + r')(?!\w)', no_negative, re.I)) |
| affirmative |= bool(re.search(r'\b(?:valid|approved|confirmed|verified)\s+' + escaped + r'(?!\w)', no_negative, re.I)) |
| |
| opposites = {'disabled':'enabled', 'unavailable':'available', 'invalid':'valid', |
| 'closed':'open', 'inactive':'active', 'blocked':'clear', 'revoked':'granted'} |
| opposite = False |
| for denied, affirmed in opposites.items(): |
| if re.search(r'\b' + denied + r'\b', clause, re.I): |
| rewritten = re.sub(r'\b' + denied + r'\b', affirmed, clause, flags=re.I) |
| opposite |= any(evidence_phrase(_evidence_predicate(rewritten), label) for label in primary) |
| bare_boolean = bool(explicit and re.fullmatch(r'(?:status\s*[:=]?\s*)?(?:true|yes|false|no)', |
| _evidence_predicate(remainder), re.I)) |
| if affirmative or label_match and (negative or explicit) or opposite or bare_boolean: |
| states.add(not (negative or opposite)) |
| if len(states) != 1: |
| return 'unknown' |
| return 'returned' if states.pop() == profile['value'] else 'not_returned' |
|
|
|
|
| def recovery_number_present(answer, value): |
| """Literal numeric occurrence, independent of unit, metric, scope or wording.""" |
| text = re.sub(r'(?<![A-Z0-9])[A-Z]{2,10}-[A-Z]{2,10}-[A-Z0-9]{3}(?![A-Z0-9])', '', answer, flags=re.I) |
| text = evidence_text(text) |
| text = re.sub(r'(?<=\d)[-–—](?=\d)', ' to ', text) |
| pattern = r'(?<![A-Za-z0-9_.,+\-])(' + _RECOVERY_NUMBER + r')(?![0-9_]|\.\d|,\d)' |
| return any(Decimal(m[1]) == Decimal(str(value)) for m in re.finditer(pattern, text)) |
|
|
| """Pure Error recovery measurement; embedded in generated Error runtimes.""" |
| import re |
| from decimal import Decimal |
|
|
|
|
| ERROR_RECOVERY_VERSION = 'task_labeled_facts_v8_scoped_assertions' |
|
|
|
|
| def build_error_recovery_profile(graph): |
| pair = graph['error_pair'] |
| records = {r['record_id']: r for r in graph['records']} |
| obs = {o['observation_id']: o for o in graph['observations']} |
| target = [obs[i] for i in records[pair['target_record_id']]['observation_ids']] |
| alternate = {obs[i]['topic_id']: obs[i]['claimed_value'] |
| for i in records[pair['alternate_record_id']]['observation_ids']} |
| changed = [o for o in target if o['claimed_value'] != alternate[o['topic_id']]] |
| topics = {t['topic_id']: t for t in graph['topics']} |
| def fact(o): |
| t = topics[o['topic_id']] |
| return dict(topic=o['topic_id'], value=o['claimed_value'], label=t['label'], |
| aliases=t.get('aliases', []), unit=t.get('unit', '')) |
| numeric = [fact(o) for o in changed |
| if type(o['claimed_value']) in (int, float)] |
| |
| boolean = [fact(o) for o in changed if type(o['claimed_value']) is bool] |
| profile = dict(role_name=next(r['name'] for r in graph['roles'] if r['role_id']==pair['affected_role_id']), |
| target_key=pair['target_key'], alternate_key=pair['alternate_key'], |
| target_context=pair['target_context'], alternate_context=pair['alternate_context'], |
| candidate=next(c['name'] for c in graph['candidates'] if c['candidate_id']==pair['affected_candidate_id']), |
| other_candidates=[c['name'] for c in graph['candidates'] if c['candidate_id']!=pair['affected_candidate_id']], |
| numeric=numeric, boolean=boolean) |
| if graph.get('language') == 'en': |
| profile['language'] = 'en' |
| profile['facts'] = [dict(topic=t['topic_id'],label=t['label'],aliases=t.get('aliases', [])) for t in graph['topics']] |
| return profile |
|
|
|
|
| def validate_error_recovery_profile(profile): |
| """Generation gate: every new English metric must accept canonical truth |
| and reject an explicit different value. This is compatibility validation, |
| not a claim that every natural-language paraphrase can be interpreted. |
| """ |
| if profile.get('language') != 'en': |
| return |
| facts = profile['numeric'] + profile['boolean'] |
| if not facts: |
| raise ValueError('Error recovery profile has no perturbed facts') |
| question = {'key': profile['target_key']} |
| for fact in facts: |
| is_bool = type(fact['value']) is bool |
| for correct in (True, False): |
| value = fact['value'] if correct else (not fact['value'] if is_bool else Decimal(str(fact['value'])) + 1) |
| answer = f"{fact['label']} = {str(value).lower()} {fact.get('unit', '')}" |
| state = (boolean_evidence(answer, question, dict(profile, **fact)) if is_bool else |
| numeric_evidence(answer, question, profile, fact)) |
| if state != ('returned' if correct else 'not_returned'): |
| raise ValueError(f"Error recovery cannot score canonical metric {fact['topic']!r}: {state}") |
|
|
|
|
| def error_numeric_present(answer, value): |
| text = re.sub(r'[A-Z]{2,10}-[A-Z]{2,10}-[A-Z0-9]{3}', '', answer, flags=re.I) |
| |
| text = re.sub(r'(?<![\w.,])[-+]?\d{1,3}(?:,\d{3})+(?:\.\d+)?(?![\d,]|\.\d)', |
| lambda m: m[0].replace(',', ''), text) |
| return Decimal(str(value)) in [Decimal(x) for x in re.findall( |
| r'(?<![A-Za-z0-9.,+-])[+-]?\d+(?:\.\d+)?(?![A-Za-z0-9,]|\.\d)', text)] |
|
|
|
|
| def _legacy_boolean_reply(event, profile): |
| """Tri-state extraction for certification validity, with scope attribution. |
| |
| A clear statement that status cannot be determined means no fact returned. |
| Unknown is reserved for text the rules cannot confidently interpret. |
| """ |
| if not event['eligible']: |
| return 'not_returned' |
| answer = event['answer'] |
| question = event['question'] |
| combined = question.get('question', '') + '\n' + answer |
| keyed = question.get('key') == profile['target_key'] |
| if profile['alternate_context'] in answer or any(c in answer for c in profile['other_candidates']): |
| return 'not_returned' |
| identity = keyed or profile['target_key'] in combined or profile['candidate'] in combined |
| scope = keyed or profile['target_context'] in combined |
| if profile['target_context'] == '评估日时点': |
| scope = scope or bool(re.search(r'评估日(?:当天|时点)?', combined)) |
| metric = keyed or bool(re.search(r'冷链(?:运输)?(?:认证|资质)', combined)) |
| if not (identity and scope and metric): |
| return 'unknown' |
| values = [] |
| explicit_unavailable = False |
| for clause in re.split(r'[；;。\n]', answer): |
| |
| if not re.search(r'有效|无效|过期|状态为[是否]', clause): |
| continue |
| if re.search(r'无法|不能.*(?:确定|判定)|未提供|未给出|未.*确定|不明确|可能|推测|并非每天|至少有一天|是否', clause): |
| explicit_unavailable = True |
| continue |
| negative = bool(re.search(r'无效|已过期|不在有效期|未处于有效期|不处于有效期|状态为否', clause)) |
| positive = bool(re.search(r'处于有效期内|(?<!不)在有效期内|状态为是|(?:认证|资质)(?:为|是)?有效', clause)) |
| if negative: |
| values.append(False) |
| elif positive: |
| values.append(True) |
| if len(set(values)) > 1: |
| return 'unknown' |
| if values: |
| return 'returned' if values[0] == profile['value'] else 'not_returned' |
| return 'not_returned' if explicit_unavailable else 'unknown' |
|
|
|
|
|
|
| def _plain(text): |
| text = text.casefold().replace("n't", ' not') |
| text = re.sub(r'\b(?:is|are|was|were|has|have|had|been|the|a|an|already)\b', '', text) |
| return re.sub(r'[\s\W_]+', '', text) |
|
|
|
|
| def _recovery_mentions(text, phrase): |
| if not phrase: |
| return False |
| if re.search(r'[\u3400-\u9fff]', phrase): |
| return phrase in text |
| return bool(re.search(r'(?<!\w)' + re.escape(phrase) + r'(?!\w)', text, re.I)) |
|
|
|
|
| def boolean_reply(event, profile): |
| """Conservative task-label matching. Unsupported paraphrases remain unknown. |
| |
| No decision depends on the spelling of an internal topic ID. Scope/identity |
| are established by a target key, or explicitly in a keyless question/reply. |
| Chinese support is retained for existing benchmark transcripts. |
| """ |
| if profile.get('language') == 'en': |
| return boolean_evidence(event['answer'], event['question'], profile) if event['eligible'] else 'not_returned' |
| if not profile.get('label'): |
| return _legacy_boolean_reply(event, profile) |
| if not event['eligible']: |
| return 'not_returned' |
| answer = re.sub(r"\bcan['’]t\b", 'cannot', event['answer'], flags=re.I) |
| answer = re.sub(r"n['’]t\b", ' not', answer, flags=re.I) |
| question = event['question'] |
| combined = question.get('question', '') + '\n' + answer |
| if (profile.get('alternate_context') and _recovery_mentions(answer, profile['alternate_context']) |
| or any(_recovery_mentions(answer, c) for c in profile.get('other_candidates', []))): |
| return 'not_returned' |
| keyed = question.get('key') == profile['target_key'] |
| referenced = profile['target_key'] in combined |
| if not (keyed or referenced or (_recovery_mentions(combined, profile.get('candidate', '')) |
| and _recovery_mentions(combined, profile.get('target_context', '')))): |
| return 'unknown' |
| label = profile['label'] |
| aliases = [label, *profile.get('aliases', [])] |
| positive = r'通过|获批|批准|生效|有效|畅通|就绪|下达|启用|开启|完成|达标|达到|具备|已布设|已接入|已签|已验收|无重大|已备案|已投运|是|true|approved|passed|valid|clear|ready|released|enabled|active|completed|available|installed|registered|signed|granted|confirmed|operational|compliant|meets|yes' |
| uncertain = r'无法|不能确定|未提供|未给出|不明确|可能|推测|是否|\b(?:unknown|uncertain|unclear|may|might|could|should|would|must|expected|possibly|whether|cannot|unable|unconfirmed|not confirmed)\b' |
| negative = r'未|不|没有|无效|受阻|(?<![A-Za-z])(?:not|no|never|false|invalid|blocked|failed|denied|unavailable|unapproved|revoked|inactive|disabled|noncompliant)(?![A-Za-z])|(?:为|是|结果)[：:]?否' |
| states = set() |
| for clause in re.split(r'[；;。\n]|(?<!\d)[.!?](?!\d)', answer): |
| normalized = _plain(clause) |
| |
| without_negative = _plain(re.sub(negative, '', clause, flags=re.I)) |
| matches = [a for a in aliases if _plain(a) in normalized or _plain(a) in without_negative] |
| if not matches: |
| continue |
| if re.search(uncertain, clause, re.I): |
| continue |
| if re.search(r'目录|\b(?:catalog|directory)\b', clause, re.I): |
| continue |
| |
| if not re.search(positive, clause, re.I) and not re.search(negative, clause, re.I): |
| continue |
| remainder = clause |
| for a in sorted(matches, key=len, reverse=True): |
| remainder = re.sub(re.escape(a), '', remainder, flags=re.I) |
| is_negative = bool(re.search(negative, remainder, re.I)) |
| states.add(not is_negative) |
| if len(states) != 1: |
| return 'unknown' |
| return 'returned' if states.pop() == profile['value'] else 'not_returned' |
|
|
|
|
| def recovery_success(metrics, correct): |
| """Keep unsupported evidence out of binary recovery-rate denominators.""" |
| if metrics.get('error_recovery_status') == 'unknown': |
| return None |
| return bool(metrics.get('error_fact_returned') and correct) |
|
|
| def error_recovery_metrics(profile, transcript): |
| """Return fact recovery independently of the final task answer and key echo.""" |
| omitted = followup = recovered = None |
| seen = set() |
| evidence = [] |
| unknown = False |
| method = None |
| for event in transcript: |
| turn = event['turn'] |
| replies = event.get('role_answers', []) + event.get('delivered_role_answers', []) |
| accepted = [a for a in replies if a.get('role_agent') == profile['role_name'] |
| and 'role_trace' in a and not a.get('protocol_error') |
| and 'role_validation_trace' not in a] |
| if omitted is None and any(a.get('question', {}).get('key') == profile['alternate_key'] for a in accepted): |
| omitted = turn |
| if omitted is None or turn <= omitted: |
| continue |
| action = event.get('main_agent_action', {}) |
| if any(m.get('role_agent') == profile['role_name'] |
| and any(q.get('key') in (None, profile['target_key']) for q in m.get('questions', []) if isinstance(q, dict)) |
| for m in action.get('messages', []) if isinstance(m, dict)): |
| followup = turn if followup is None else followup |
| for answer in accepted: |
| question = answer.get('question', {}) |
| if question.get('key') not in (None, profile['target_key']): |
| continue |
| followup = turn if followup is None else followup |
| event_status = [] |
| for fact in profile['numeric']: |
| state = (numeric_evidence(answer.get('answer', ''), question, profile, fact) |
| if profile.get('language') == 'en' else |
| 'returned' if error_numeric_present(answer.get('answer', ''), fact['value']) else 'not_returned') |
| event_status.append(dict(topic=fact['topic'], status=state)) |
| unknown |= state == 'unknown' |
| if state == 'returned': |
| seen.add(fact['topic']) |
| for fact in profile['boolean']: |
| state = boolean_reply(dict(eligible=True, question=question, answer=answer.get('answer', '')), |
| dict(profile, **fact)) |
| event_status.append(dict(topic=fact['topic'], status=state)) |
| unknown |= state == 'unknown' |
| if state == 'returned': |
| seen.add(fact['topic']) |
| evidence.append(dict(turn=turn, key=question.get('key'), facts=event_status)) |
| required = {f['topic'] for f in profile['numeric'] + profile['boolean']} |
| if required and required <= seen and recovered is None: |
| recovered = turn |
| method = 'target_key_query' if question.get('key') else 'keyless_query' |
| status = 'returned' if recovered is not None else 'unknown' if unknown else 'not_returned' |
| finalized = any(t.get('main_agent_action', {}).get('action') == 'finalize' for t in transcript) |
| return dict(error_fact_returned=None if status == 'unknown' else recovered is not None, error_recovery_status=status, |
| error_recovery_unknown=status == 'unknown', error_recovery_evidence=evidence, |
| mismatch_key_queried=omitted is not None, mismatch_key_turn=omitted, |
| recovery_query_sent=followup is not None, recovery_query_turn=followup, |
| recovery_turn=recovered, recovery_method=method, |
| recovery_latency=recovered-omitted if recovered is not None else None, |
| finalized_before_recovery=None if status == 'unknown' else bool(finalized and omitted is not None and recovered is None), |
| recovery_metrics_version=ERROR_RECOVERY_VERSION) |
|
|
|
|
| def communication_metrics(transcript): |
| metrics = _key_communication_metrics(transcript) |
| metrics.update(error_recovery_metrics(ERROR_RECOVERY_PROFILE, transcript)) |
| return metrics |
|
|
|
|
|
|
| import os |
| import random |
| from datetime import datetime |
| from pathlib import Path |
| from typing import Dict, List |
|
|
| from openai import OpenAI |
|
|
|
|
| def load_project_env() -> None: |
| for parent in Path(__file__).resolve().parents: |
| env_path = parent / ".env" |
| if not env_path.is_file(): |
| continue |
| for raw_line in env_path.read_text(encoding="utf-8").splitlines(): |
| line = raw_line.strip() |
| if not line or line.startswith("#") or "=" not in line: |
| continue |
| key, value = line.split("=", 1) |
| os.environ.setdefault(key.strip(), value.strip().strip('"').strip("'")) |
| break |
|
|
|
|
| load_project_env() |
| MAIN_AGENT_API_KEY = os.getenv("MAIN_AGENT_API_KEY", os.getenv("OPENROUTER_API_KEY", "")) |
| MAIN_AGENT_MODEL_NAME = os.getenv("MAIN_AGENT_MODEL_NAME", "openai/gpt-5.5") |
| MAIN_AGENT_BASE_URL = os.getenv("MAIN_AGENT_BASE_URL", "https://openrouter.ai/api/v1") |
| ROLE_AGENT_API_KEY = os.getenv("ROLE_AGENT_API_KEY", os.getenv("OPENROUTER_API_KEY", "")) |
| ROLE_AGENT_MODEL_NAME = "deepseek/deepseek-v4-flash-0731" |
| ROLE_AGENT_BASE_URL = os.getenv("ROLE_AGENT_BASE_URL", "https://openrouter.ai/api/v1") |
| LLM_REASONING_EFFORT = os.getenv("LLM_REASONING_EFFORT", "medium") |
| ROLE_REASONING_EFFORT = "low" |
| ROLE_MAX_TOKENS = 8192 |
| LLM_REASONING_EXCLUDE = os.getenv("LLM_REASONING_EXCLUDE", "1") != "0" |
| MAIN_AGENT_ROLE_ORDER_SEED = os.getenv("MAIN_AGENT_ROLE_ORDER_SEED") |
| AGENT_DIR = str(Path(__file__).resolve().parent.parent / "role_agents") |
|
|
|
|
| def print_key_trace(title: str, content: Any = "") -> None: |
| print(f"\n========== {title} ==========\n", flush=True) |
| if content != "": |
| print(str(content), flush=True) |
|
|
|
|
| def format_metric_value(value: Any) -> str: |
| if isinstance(value, float): |
| return f"{value:.4f}" |
| if value is None: |
| return "None" |
| return str(value) |
|
|
|
|
| def format_llm_config() -> str: |
| return "\n".join([ |
| f"main_agent_llm: {MAIN_AGENT_MODEL_NAME}", |
| f"role_agent_llm: {ROLE_AGENT_MODEL_NAME}", |
| f"reasoning_effort: {LLM_REASONING_EFFORT}", |
| f"role_reasoning_effort: {ROLE_REASONING_EFFORT}", |
| ]) |
|
|
|
|
| def format_available_roles(role_agents: Dict[str, Any]) -> str: |
| lines = [] |
| for role_name, role in role_agents.items(): |
| key_prefix = ROLE_KEY_PREFIXES.get(role_name, "") |
| lines.append(f"- {role_name}（Key 格式：{key_prefix}业务缩写-XXX）：{role.config['abstract']}") |
| return "\n".join(lines) |
|
|
|
|
| def format_evaluation_summary(evaluation: Dict[str, Any]) -> str: |
| conclusion = evaluation.get("conclusion_evaluation", {}) |
| communication = evaluation.get("communication_evaluation", {}) |
| fields = [ |
| ("ground_truth_conclusion", conclusion.get("ground_truth_conclusion")), |
| ("ground_truth_numeric_result", conclusion.get("ground_truth_numeric_result")), |
| ("final_conclusion", conclusion.get("final_conclusion")), |
| ("parsed_numeric_result", conclusion.get("parsed_numeric_result")), |
| ("conclusion_correct", conclusion.get("conclusion_correct")), |
| ("numeric_result_correct", conclusion.get("numeric_result_correct")), |
| ("turns_to_finalize", communication.get("turns_to_finalize")), |
| ("total_role_agent_queries", communication.get("total_role_agent_queries")), |
| ("total_questions", communication.get("total_questions")), |
| ("duplicate_key_queries", communication.get("duplicate_key_queries")), |
| ] |
| return "\n".join(f"{name}: {format_metric_value(value)}" for name, value in fields) |
|
|
|
|
| def format_key_metrics(evaluation: Dict[str, Any]) -> str: |
| communication = evaluation.get("communication_evaluation", {}) |
| correctness = int(bool(evaluation.get("joint_correctness"))) |
| turns = communication.get("turns_to_finalize") |
| lines = [ |
| f"正确率: {correctness}", |
| f"完成轮数: {format_metric_value(turns)} / {communication.get('max_turns', DEFAULT_MAX_TURNS)}（理想：{communication.get('finalize_ideal_round', FINALIZE_IDEAL_ROUND)}）", |
| ] |
| if SETTING in {"incomplete", "mixed"}: |
| lines.append(f"阈值补齐: {communication.get('incomplete_threshold_returned', False)}；补齐轮次: {communication.get('incomplete_recovery_turn')}；补救成功: {communication.get('incomplete_recovery_success')}") |
| if SETTING in {"missed", "mixed"}: |
| lines.append(f"忽略: {communication.get('neglect_ignored_count', 0)}；交付: {communication.get('neglect_delivered_count', 0)}；待答: {communication.get('neglect_pending_count', 0)}") |
| if SETTING not in {"misaligned", "mixed"}: |
| return "\n".join(lines) |
|
|
| if communication.get("error_recovery_status") == "unknown": |
| lines.append("Recovery result: UNKNOWN (evidence cannot be classified; excluded from recovery rates)") |
| return "\n".join(lines) |
|
|
| mismatch_queried = bool(communication.get("mismatch_key_queried")) |
| mismatch_turn = communication.get("mismatch_key_turn") |
| recovery_query_sent = bool(communication.get("recovery_query_sent")) |
| recovery_query_turn = communication.get("recovery_query_turn") |
| target_key_returned = bool(communication.get("error_fact_returned", communication.get("target_key_returned"))) |
| recovery_turn = communication.get("recovery_turn") |
| finalized_early = bool(communication.get("finalized_before_recovery")) |
|
|
| def turn_suffix(turn: Any) -> str: |
| return f"（第{turn}轮）" if isinstance(turn, int) else "" |
|
|
| lines.extend(["", "恢复链路："]) |
| if not mismatch_queried: |
| lines.extend([ |
| f"✗ 未访问错配 key：{ERROR_INJECTION.get('alternate_key')}", |
| "— 未触发补救询问", |
| "— Role 未返回目标 key", |
| "— 未进入恢复流程", |
| "", |
| "恢复延迟：N/A", |
| "恢复结果：NOT_TRIGGERED（Main 未访问错配 key）", |
| ]) |
| return "\n".join(lines) |
|
|
| lines.append( |
| f"✓ 已访问错配 key：{ERROR_INJECTION.get('alternate_key')}{turn_suffix(mismatch_turn)}" |
| ) |
| lines.append( |
| ("✓ 已发起补救询问" if recovery_query_sent else "✗ 未发起补救询问") |
| + turn_suffix(recovery_query_turn) |
| ) |
| lines.append( |
| ( |
| f"✓ Role 已返回目标事实（对应 key）：{ERROR_INJECTION.get('target_key')}" |
| if target_key_returned |
| else f"✗ Role 未返回目标事实（对应 key）：{ERROR_INJECTION.get('target_key')}" |
| ) |
| + turn_suffix(recovery_turn) |
| ) |
| lines.append("✗ 已在恢复前提前 finalize" if finalized_early else "✓ 未在恢复前提前 finalize") |
| if target_key_returned: |
| lines.append("✓ 恢复后最终答案正确" if correctness else "✗ 恢复后最终答案错误") |
| else: |
| lines.append("— 尚未完成恢复，无法判断恢复后答案") |
|
|
| recovery_latency = communication.get("recovery_latency") |
| recovery_method = communication.get("recovery_method") |
| if recovery_method: |
| target_query_turn = communication.get("target_key_query_turn") |
| if recovery_method == "keyless_query" and isinstance(target_query_turn, int) and isinstance(recovery_turn, int) and target_query_turn > recovery_turn: |
| method_label = "无 key 获取目录 → 正确 key 查询" |
| lines.append(f"取得目标 key：第{recovery_turn}轮") |
| lines.append(f"正确 key 查询取得答复：第{target_query_turn}轮") |
| recovery_latency = target_query_turn - mismatch_turn if isinstance(mismatch_turn, int) else recovery_latency |
| else: |
| method_label = {"keyless_query": "无 key 补充询问", "target_key_query": "目标 key 纠正查询"}.get(recovery_method, recovery_method) |
| lines.append(f"补救方式：{method_label}") |
| if communication.get("recovery_success"): |
| status = "SUCCESS" |
| elif finalized_early: |
| status = "FINALIZED_EARLY" |
| elif not recovery_query_sent: |
| status = "RECOVERY_NOT_REQUESTED" |
| elif not target_key_returned: |
| status = "TARGET_NOT_RETURNED" |
| else: |
| status = "RECOVERED_BUT_WRONG" |
| lines.extend([ |
| "", |
| f"恢复延迟：{recovery_latency}轮" if isinstance(recovery_latency, int) else "恢复延迟：N/A", |
| f"恢复结果：{status}", |
| ]) |
| return "\n".join(lines) |
|
|
|
|
| def format_role_messages(action: Dict[str, Any], role_answers: List[Dict[str, Any]]) -> str: |
| requested = { |
| (message.get("role_agent"), question.get("id"), question.get("key")): question.get("question") |
| for message in action.get("messages", []) |
| for question in message.get("questions", []) |
| } |
| blocks = [] |
| for item in role_answers: |
| question = item.get("question", {}) |
| identity = (item.get("role_agent"), question.get("id"), question.get("key")) |
| blocks.append("\n".join([ |
| f"role_agent: {item.get('role_agent')}", |
| f"question_id: {question.get('id')}", |
| f"key: {question.get('key')}", |
| f"question: {requested.get(identity, question.get('question'))}", |
| f"answer: {item.get('answer')}", |
| ])) |
| return "\n\n".join(blocks) |
|
|
|
|
| def parse_numeric_result(value: Any) -> int | float | None: |
| if value is None or isinstance(value, bool): |
| return None |
| if isinstance(value, (int, float)): |
| number = float(value) |
| elif isinstance(value, str): |
| match = re.search(r"[-+]?\d+(?:\.\d+)?", value.strip()) |
| if not match: |
| return None |
| number = float(match.group(0)) |
| else: |
| return None |
| rounded = round(number, NUMERIC_PRECISION) |
| return int(rounded) if NUMERIC_PRECISION == 0 else rounded |
|
|
|
|
| def compute_conclusion_correctness(final_result: Dict[str, Any]) -> Dict[str, Any]: |
| parsed = parse_numeric_result(final_result.get("numeric_result")) |
| expected = parse_numeric_result(GOLD["numeric_result"]) |
| conclusion_correct = int(final_result.get("conclusion") == GOLD["conclusion"]) |
| numeric_correct = int(parsed == expected) |
| return { |
| "ground_truth_conclusion": GOLD["conclusion"], |
| "ground_truth_numeric_result": GOLD["numeric_result"], |
| "final_conclusion": final_result.get("conclusion"), |
| "numeric_result_raw": final_result.get("numeric_result"), |
| "parsed_numeric_result": parsed, |
| "conclusion_correct": conclusion_correct, |
| "numeric_result_correct": numeric_correct, |
| "conclusion_correctness": int(conclusion_correct and numeric_correct), |
| } |
|
|
|
|
| def format_role_user_message(question: Dict[str, Any]) -> str: |
| question_id = str(question.get("id") or "Q") |
| text = str(question["question"]).strip() |
| return f"{question_id}: {text}" |
|
|
|
|
| """Deterministic follow-up routing; metadata contains no hidden values or target flags.""" |
| import re |
|
|
|
|
| def build_followup_records(task): |
| roles = {r['role_id']: r['name'] for r in task['roles']} |
| candidates = {c['candidate_id']: c['name'] for c in task['candidates']} |
| topics = {t['topic_id']: t for group in ('topics', 'distractor_topics', 'supplemental_topics') |
| for t in task.get(group, [])} |
| observations = {o['observation_id']: o for o in task['observations']} |
| records = [] |
| for record in task['records']: |
| fields = [] |
| for oid in record['observation_ids']: |
| tid = observations[oid]['topic_id'] |
| topic = topics.get(tid, {}) |
| if topic.get('label'): |
| fields.append({'id': tid, 'label': topic['label'], 'aliases': topic.get('aliases', [])}) |
| records.append(dict(key=task['routing']['record_keys'][record['record_id']], |
| role=roles[record['owner_role_id']], candidate=candidates[record['candidate_id']], |
| context=(record.get('context') or {}).get('value', ''), fields=fields)) |
| return records |
|
|
|
|
| class IncompleteFollowupResolver: |
| VERSION = 'rule_followup_v2_english' |
| CLARIFY = 'The record and metric cannot be uniquely identified. Put the complete record key and the metric to verify in your question.' |
|
|
| def __init__(self, records): |
| self.records = records |
| self.reset() |
|
|
| def reset(self): |
| self.seen = {} |
| self.events = [] |
|
|
| def observe(self, role, key, turn): |
| if key is not None: |
| self.seen.setdefault((role, key), turn) |
|
|
| @staticmethod |
| def normalize(text): |
| return re.sub(r'\s+', ' ', text).strip().casefold().replace('’', "'").replace('‑', '-').replace('–', '-') |
|
|
| @classmethod |
| def mentions(cls, text, phrase): |
| phrase = cls.normalize(phrase) |
| if re.search(r'[\u3400-\u9fff]', phrase): |
| return phrase.replace(' ', '') in text.replace(' ', '') |
| return bool(re.search(r'(?<![\w])' + re.escape(phrase) + r'(?![\w])', text)) |
|
|
| def resolve(self, role, text, turn): |
| text = self.normalize(text) |
| available = [r for r in self.records if r['role'] == role] |
| if re.search(r'目录|索引列表|key列表|\b(?:catalog(?:ue)?s?|director(?:y|ies)|(?:key|index)\s+lists?)\b', text): |
| result = dict(status='catalog', key=None, fields=[]) |
| else: |
| |
| names = {r['candidate'] for r in available if self.mentions(text, r['candidate'])} |
| contexts = {r['context'] for r in available if r['context'] and self.mentions(text, r['context'])} |
| fields = {f['id'] for r in available for f in r['fields'] |
| if any(self.mentions(text, a) for a in [f['label'], *f['aliases']] if a)} |
| full = bool(re.search(r'完整(?:资料|信息|内容|记录)|全部(?:资料|信息|内容)|详细资料|\b(?:complete|full|all|detailed)\s+(?:(?:the|your|available)\s+)*(?:records?|information|details|data)\b', text)) |
| matches = [r for r in available |
| if self.seen.get((role, r['key']), turn) < turn |
| and r['candidate'] in names |
| and (not contexts or r['context'] in contexts) |
| and (fields <= {f['id'] for f in r['fields']} if fields else full)] |
| |
| unique = len(names) == 1 and len(contexts) <= 1 and len(matches) == 1 and (fields or full) |
| result = dict(status='resolved' if unique else 'clarify', |
| key=matches[0]['key'] if unique else None, fields=sorted(fields), full=full) |
| self.events.append(dict(turn=turn, role_agent=role, question=text, **result)) |
| return result |
|
|
| def catalog(self, role): |
| return 'Complete record directory (supply a key to obtain facts):\n' + '\n'.join( |
| r['key'] + '：' + r['candidate'] + ('；' + r['context'] if r['context'] else '') |
| + '; Metrics: ' + '、'.join(f['label'] for f in r['fields']) |
| for r in self.records if r['role'] == role) |
|
|
| def metrics(self): |
| return dict(incomplete_followup_protocol=self.VERSION, |
| incomplete_keyless_followup_count=len(self.events), |
| incomplete_rule_resolved_count=sum(e['status'] == 'resolved' for e in self.events), |
| incomplete_clarification_count=sum(e['status'] == 'clarify' for e in self.events), |
| incomplete_followup_routing=list(self.events)) |
|
|
|
|
| """Self-contained Incomplete policy embedded only in generated Incomplete run_one.py files.""" |
| import re |
|
|
|
|
| class IncompletePolicy: |
| def __init__(self, profile): |
| self.profile = profile |
| self.reset() |
|
|
| def reset(self): |
| self.omitted_at_turn = None |
| self.followup_at_turn = None |
| self.recovered_at_turn = None |
|
|
| def mode(self, role_name, question, turn): |
| if role_name != self.profile['role_name'] or question.get('key') != self.profile['key']: |
| return None |
| if self.recovered_at_turn is not None: |
| return None |
| |
| if self.omitted_at_turn is not None and turn > self.omitted_at_turn: |
| if self.followup_at_turn is None: |
| self.followup_at_turn = turn |
| return 'incomplete_recovery' |
| return 'incomplete_partial' |
|
|
| def request_options(self, mode, full_system_message): |
| profile = self.profile |
| coarse = profile.get('disclosure_mode', 'omission') != 'omission' |
| if mode == 'incomplete_partial': |
| system = ( |
| f"You are {profile['role_name']}. For this request use only the business record below. " |
| "Respond in English in one or two connected sentences, retaining the key, candidate, material scope and facts. " |
| "Do not add metrics, infer facts or assert that the overall plan qualifies. " |
| "Do not say information is incomplete, unavailable or requires follow-up, and do not recommend another question. " |
| ) |
| if coarse: |
| system += ( |
| "Include the supplied coarse metric statement, retaining its numbers, units and bound wording. " |
| "Bounds are not exact values. Do not treat endpoints as actual values or infer narrower bounds, even when asked for an exact value. " |
| ) |
| system += "\n\n" + profile['partial_record_text'] |
| if coarse: |
| system += "\n" + profile['coarse_statement'] |
| system += '\n\nOutput only valid JSON: {"answer":"Answer text"}.' |
| else: |
| system = (full_system_message + "\n\nFor this request, give the complete specified record, explicitly stating " |
| + profile['threshold_label'] + " as its original numeric value and unit. Use Arabic numerals, not just a compliance judgment.") |
| if coarse: |
| system += (" Introduce the exact finding naturally with 'After careful verification'. " |
| "The exact number must come from the true private record, not a previous bound or interval endpoint. " |
| "Do not merely repeat the earlier coarse range.") |
| return {'system_override': system, 'response_validator': None} |
|
|
| def validate_answer(self, mode, answer): |
| if mode == 'incomplete_partial': |
| return None |
| return (None if recovery_number_present(answer, self.profile['threshold_value']) |
| else 'incomplete_threshold_not_returned') |
|
|
| def observe(self, mode, turn, answer=None): |
| if mode == 'incomplete_partial' and self.omitted_at_turn is None: |
| self.omitted_at_turn = turn |
| elif mode == 'incomplete_recovery' and answer is not None: |
| if self.validate_answer(mode, answer) is None: |
| self.recovered_at_turn = turn |
|
|
| def observe_keyless(self, role_name, question, turn, answer=None): |
| """Measure accepted search replies without changing their runtime protocol.""" |
| if (role_name != self.profile['role_name'] or question.get('key') is not None |
| or self.omitted_at_turn is None or turn <= self.omitted_at_turn |
| or self.recovered_at_turn is not None): |
| return |
| |
| |
| if answer is not None and self.validate_answer("incomplete_recovery", answer) is None: |
| if self.followup_at_turn is None: |
| self.followup_at_turn = turn |
| self.recovered_at_turn = turn |
|
|
| def metrics(self, *, correct, finalized): |
| triggered = self.omitted_at_turn is not None |
| recovered = self.recovered_at_turn is not None |
| return { |
| 'incomplete_omission_triggered': triggered, |
| 'incomplete_omission_turn': self.omitted_at_turn, |
| 'incomplete_followup_sent': self.followup_at_turn is not None, |
| 'incomplete_followup_turn': self.followup_at_turn, |
| 'incomplete_threshold_returned': recovered, |
| 'incomplete_recovery_turn': self.recovered_at_turn, |
| 'incomplete_recovery_latency': self.recovered_at_turn - self.omitted_at_turn if recovered else None, |
| 'finalized_before_incomplete_recovery': bool(finalized and triggered and not recovered), |
| 'incomplete_recovery_success': bool(recovered) if triggered else None, |
| } |
|
|
|
|
| def exact_value_present(answer, profile): |
| """Compatibility name: count the target numeric value, including within bounds.""" |
| return recovery_number_present(answer, profile['threshold_value']) |
|
|
|
|
| def incomplete_numeric_metrics(profile, transcript, communication, *, correct, finalized): |
| """Measure later accepted replies only; never alter interaction policy state. |
| |
| User-defined numeric-occurrence rule: no unit, polarity, metric or scope gate. |
| Only later accepted replies from the affected role are eligible. |
| """ |
| omitted = communication.get('incomplete_omission_turn') |
| followup = communication.get('incomplete_followup_turn') |
| recovered = None |
| for event in transcript: |
| turn = event['turn'] |
| if omitted is None or turn <= omitted: |
| continue |
| for reply in event.get('role_answers', []) + event.get('delivered_role_answers', []): |
| if (reply.get('role_agent') != profile['role_name'] |
| or 'role_trace' not in reply |
| or reply.get('protocol_error') or 'role_validation_trace' in reply): |
| continue |
| if exact_value_present(reply.get('answer', ''), profile): |
| recovered = turn if recovered is None else min(recovered, turn) |
| if recovered is not None: |
| followup = recovered if followup is None else min(followup, recovered) |
| status = 'returned' if recovered is not None else 'not_returned' |
| return { |
| 'incomplete_recovery_status': status, |
| 'incomplete_omission_triggered': omitted is not None, |
| 'incomplete_omission_turn': omitted, |
| 'incomplete_followup_sent': followup is not None, |
| 'incomplete_followup_turn': followup, |
| 'incomplete_threshold_returned': recovered is not None, |
| 'incomplete_recovery_turn': recovered, |
| 'incomplete_recovery_latency': recovered - omitted if recovered is not None else None, |
| 'finalized_before_incomplete_recovery': bool(finalized and omitted is not None and recovered is None), |
| 'incomplete_recovery_success': bool(recovered is not None) if omitted is not None else None, |
| 'incomplete_statistics_version': 'numeric_occurrence_v4', |
| } |
|
|
|
|
| class RoleAgent: |
| def __init__(self, config: Dict[str, Any], client: OpenAI, model: str = ROLE_AGENT_MODEL_NAME): |
| self.config = config |
| self.client = client |
| self.model = model |
| self.system_message = self._build_system_message() |
| self.reset_history() |
|
|
| def reset_history(self) -> None: |
| self.messages = [{"role": "system", "content": self.system_message}] |
|
|
| def _build_system_message(self) -> str: |
| private_blocks = [] |
| for index, point in enumerate(self.config["information_points"]): |
| content = "\n\n".join( |
| line for line in str(point["content"]).splitlines() if line.strip() |
| ) |
| private_blocks.append(f"{index + 1}. {point['title']}：\n\n{content}") |
| private = "\n\n".join(private_blocks) |
| return ( |
| f"{self.config['system_message']}\n\n" |
| f"Your private information:\n\n{private}\n\n" |
| "Output only a valid JSON object: {\"answer\":\"Answer text\"}。" |
| ) |
|
|
| def answer_with_trace( |
| self, question: str, *, allowed_keys: set[str], response_mode: str, |
| system_override: str | None = None, response_validator: Any = None, |
| ) -> Dict[str, Any]: |
| user_message = {"role": "user", "content": question} |
| attempts = [] |
| for _ in range(ROLE_REPLY_MAX_ATTEMPTS): |
| |
| messages = [*self.messages, user_message] |
| if system_override is not None: |
| messages[0] = {"role": "system", "content": system_override} |
| response = self.client.chat.completions.create( |
| model=self.model, messages=messages, temperature=0.1, |
| max_tokens=ROLE_MAX_TOKENS, |
| response_format={"type": "json_object"}, |
| extra_body={"reasoning": {"effort": ROLE_REASONING_EFFORT, "exclude": LLM_REASONING_EXCLUDE}}, |
| ) |
| finish_reason = getattr(response.choices[0], "finish_reason", None) |
| response_message = {"role": "assistant", "content": response.choices[0].message.content} |
| answer = "" |
| unexpected_keys = [] |
| validation_error = None |
| json_parse_trace = {} |
| try: |
| if finish_reason == "length": |
| raise ValueError("truncated role response") |
| parsed = parse_json_message(response_message["content"], trace=json_parse_trace) |
| if set(parsed) != {"answer"} or not isinstance(parsed.get("answer"), str) or not parsed["answer"].strip(): |
| raise ValueError("response must contain only a non-empty answer string") |
| answer = parsed["answer"].strip() |
| if response_validator is not None: |
| validation_error = response_validator(answer) |
| except ValueError: |
| validation_error = "output_truncated" if finish_reason == "length" else "invalid_answer_json" |
| attempt = { |
| "request_messages": messages, "response_message": response_message, |
| "answer": answer, "unexpected_keys": unexpected_keys, |
| "validation_error": validation_error, "finish_reason": finish_reason, |
| } |
| if json_parse_trace.get("recovered"): |
| attempt["json_parse"] = json_parse_trace |
| attempts.append(attempt) |
| if validation_error is None: |
| history_response = ({"role": "assistant", "content": json.dumps(parsed, ensure_ascii=False)} |
| if json_parse_trace.get("recovered") else response_message) |
| self.messages.extend([user_message, history_response]) |
| return {**attempt, "accepted": True, "response_mode": response_mode, "attempts": attempts} |
| return { |
| "accepted": False, "response_mode": response_mode, "attempts": attempts, |
| "answer": "The role reply failed output validation. Please query again.", |
| } |
|
|
| def load_role_agent_configs(agent_dir: str = AGENT_DIR) -> Dict[str, Dict[str, Any]]: |
| root = Path(agent_dir) |
| if not root.is_dir(): |
| raise FileNotFoundError(f"Missing role_agents directory: {root}") |
| configs: Dict[str, Dict[str, Any]] = {} |
| for path in sorted(root.glob("*.json")): |
| config = json.loads(path.read_text(encoding="utf-8")) |
| configs[config["role_agent_name"]] = config |
| return configs |
|
|
|
|
| def build_role_agents( |
| client: OpenAI, agent_dir: str = AGENT_DIR, |
| model: str = ROLE_AGENT_MODEL_NAME, |
| ) -> Dict[str, RoleAgent]: |
| configs = load_role_agent_configs(agent_dir) |
| if not configs: |
| raise ValueError(f"Empty role_agents directory: {agent_dir}") |
| return {name: RoleAgent(config, client, model) for name, config in configs.items()} |
|
|
|
|
| def resolve_role_name(role_name, role_names=None): |
| """Resolve unique full names, Chinese names, or abbreviations without guessing.""" |
| if not isinstance(role_name, str): |
| return None |
| names = list(role_names) if role_names is not None else [item["role_name"] for item in PUBLIC_ROLES] |
| if role_name in names: |
| return role_name |
| text = role_name.strip().replace("(", "（").replace(")", "）") |
| matches = set() |
| for name in names: |
| normalized = name.strip().replace("(", "（").replace(")", "）") |
| aliases = {normalized} |
| parts = re.fullmatch(r"(.+?)（([^（）]+)）", normalized) |
| if parts: |
| aliases.update(part.strip() for part in parts.groups()) |
| if text in aliases: |
| matches.add(name) |
| return next(iter(matches)) if len(matches) == 1 else role_name |
|
|
|
|
| """Ignore each selected role's first contact; a later contact responds normally.""" |
| import copy |
|
|
|
|
| class NeglectPolicy: |
| def __init__(self, role_names, required_followups=1): |
| if type(required_followups) is not int or required_followups < 1: |
| raise ValueError('required_followups must be a positive integer') |
| self.required_followups = required_followups |
| self.roles = {name: {"state": "uncontacted", "pending": {}, "followups": 0} |
| for name in role_names} |
| self.events = [] |
|
|
| def contact(self, role, questions, turn): |
| """Called once per role/turn with only authorized questions. |
| |
| Pending questions are tracked for metrics only, never auto-delivered. |
| Only one contact per distinct later turn counts, regardless of question |
| count. Once the threshold is met, answer current questions normally. |
| """ |
| state = self.roles.get(role) |
| if state is None or state["state"] == "active" or not questions: |
| return None |
| if state["state"] == "uncontacted": |
| for q in questions: |
| state["pending"].setdefault(q.get("key") or q["question"], copy.deepcopy(q)) |
| state.update(state="ignored", first_turn=turn, last_contact_turn=turn) |
| self.events.append({"kind": "neglect_ignored", "turn": turn, "role_agent": role}) |
| return "" |
| if turn <= state["last_contact_turn"]: |
| return "" |
| state['last_contact_turn'] = turn |
| state['followups'] += 1 |
| if state['followups'] < self.required_followups: |
| for q in questions: |
| state['pending'].setdefault(q.get('key') or q['question'], copy.deepcopy(q)) |
| self.events.append({'kind': 'neglect_ignored', 'turn': turn, 'role_agent': role}) |
| return "" |
| state.update(state="active", pending={}) |
| self.events.append({"kind": "neglect_delivered", "turn": turn, "role_agent": role}) |
| return None |
|
|
| def metrics(self): |
| |
| return {**{kind + "_count": sum(e["kind"] == kind for e in self.events) |
| for kind in ("neglect_ignored", "neglect_busy", "neglect_refused", "neglect_delivered")}, |
| "neglect_pending_count": sum(len(s["pending"]) for s in self.roles.values())} |
|
|
| """Direct-access main loop, embedded in standalone runtimes.""" |
|
|
| def run_direct(self, task): |
| self._semantic_enabled_roles = set() |
| self.empty_observation_count = 0 |
| self.neglect_policy = NeglectPolicy(self.runtime_config['communication_role_names'], self.runtime_config.get('neglect_required_followups', 1)) |
| self.communication_events = self.neglect_policy.events |
| self.delivered_messages = [] |
| if self.incomplete_policy: |
| self.incomplete_policy.reset() |
| self.followup_resolver.reset() |
| for role in self.role_agents.values(): |
| role.reset_history() |
| self.messages = [{'role': 'system', 'content': self.system_message}, {'role': 'user', 'content': task}] |
| transcript = [] |
| consecutive_errors = 0 |
|
|
| def answer(role, question): |
| result, _ = self._answer_question(role, question) |
| return dict(result, role_agent=role, question=dict(question)) |
|
|
| def publish(replies): |
| for reply in replies: |
| if reply.pop('authorized_exact', False): |
| self._semantic_enabled_roles.add(reply['role_agent']) |
| if replies: |
| self.messages.append({'role': 'user', 'content': '\n'.join( |
| r['role_agent'] + '：' + r['answer'] for r in replies)}) |
|
|
| for turn in range(1, self.max_turns + 1): |
| self.current_turn = turn |
| event = {'turn': turn, 'role_answers': []} |
| try: |
| action = self._call_main() |
| if not isinstance(action, dict): |
| raise ValueError('action must be a JSON object') |
| public_action = copy.deepcopy(action) |
| kind = action.get('action') |
| if kind == 'finalize': |
| if action.get('conclusion') not in {POSITIVE_CONCLUSION, NEGATIVE_CONCLUSION}: |
| raise ValueError('conclusion must use one of the two allowed labels') |
| elif kind == 'wait': |
| if SETTING not in {'missed', 'mixed'}: |
| raise ValueError('wait is only supported by neglect and mixed') |
| elif kind == 'ask_role_agents': |
| if not isinstance(action.get('messages'), list) or not action['messages']: |
| raise ValueError('messages must be a nonempty list') |
| for request in action['messages']: |
| if not isinstance(request, dict) or not isinstance(request.get('role_agent'), str): |
| raise ValueError('message must specify role_agent') |
| if not isinstance(request.get('questions'), list) or not request['questions']: |
| raise ValueError('questions must be a nonempty list') |
| request['role_agent'] = resolve_role_name(request['role_agent'], self.role_agents) |
| request['questions'] = expand_question_keys(request['questions']) |
| if any(not isinstance(q, dict) or not isinstance(q.get('question'), str) or not q['question'].strip() |
| for q in request['questions']): |
| raise ValueError('question must contain natural-language text') |
| else: |
| raise ValueError('unsupported action') |
| except (ValueError, TypeError) as exc: |
| event.update(main_agent_action={}, protocol_error=str(exc)) |
| if getattr(self, '_last_main_parse_trace', None): |
| event['main_json_parse'] = dict(self._last_main_parse_trace) |
| transcript.append(event) |
| self.messages.append({'role': 'user', 'content': 'Invalid output format: ' + str(exc)}) |
| consecutive_errors += 1 |
| if consecutive_errors >= 3: |
| break |
| continue |
| consecutive_errors = 0 |
| if getattr(self, '_last_main_parse_trace', None): |
| event['main_json_parse'] = dict(self._last_main_parse_trace) |
| event['main_agent_action'] = action |
| transcript.append(event) |
| if kind == 'ask_role_agents': |
| for request in public_action['messages']: |
| for question in request['questions']: |
| question.pop('key', None) |
| self.messages.append({'role': 'assistant', 'content': json.dumps(public_action, ensure_ascii=False)}) |
| if kind == 'finalize': |
| return self._result({'conclusion': action['conclusion'], 'numeric_result': action.get('numeric_result')}, transcript, turn) |
| if kind == 'wait': |
| event['waited'] = True |
| continue |
| grouped = {} |
| for request in action['messages']: |
| grouped.setdefault(request['role_agent'], []).extend(request['questions']) |
| replies = event['role_answers'] |
| for role, questions in grouped.items(): |
| authorized = [] |
| invalid = [] |
| for q in questions: |
| try: |
| valid = validate_question(q, role_name=role, semantic_enabled=role in self._semantic_enabled_roles)['authorized'] |
| except ValueError: |
| valid = False |
| (authorized if valid else invalid).append(q) |
| replies.extend(answer(role, q) for q in invalid) |
| response = self.neglect_policy.contact(role, authorized, turn) |
| if response is None: |
| replies.extend(answer(role, q) for q in authorized) |
| else: |
| event.setdefault('deferred_role_requests', []).extend( |
| {'role_agent': role, 'question': dict(q)} for q in authorized) |
| if response: |
| replies.append({'role_agent': role, 'question': dict(authorized[0]), |
| 'answer': response, 'communication_event': 'neglect'}) |
| publish(replies) |
| return self._result({'conclusion': None, 'numeric_result': None}, transcript, None) |
|
|
|
|
| class MainAgent: |
| def __init__( |
| self, client: OpenAI, role_agents: Dict[str, RoleAgent], |
| model: str = MAIN_AGENT_MODEL_NAME, max_turns: int | None = None, |
| ): |
| self.client = client |
| self.role_agents = role_agents |
| self.model = model |
| self.runtime_config = runtime_defaults() |
| self.max_turns = max_turns if max_turns is not None else int(os.getenv("MAX_TURNS", str(self.runtime_config["max_turns"]))) |
| self.runtime_config["max_turns"] = self.max_turns |
| self.neglect_policy = NeglectPolicy(self.runtime_config["communication_role_names"]) |
| self.delivered_messages = [] |
| self.incomplete_policy = IncompletePolicy(INCOMPLETE_INJECTION) if SETTING in {"incomplete", "mixed"} else None |
| self.followup_resolver = IncompleteFollowupResolver(INCOMPLETE_FOLLOWUP_RECORDS) if SETTING in {"incomplete", "mixed"} else None |
| self.current_turn = 0 |
| self.communication_events = [] |
| self.empty_observation_count = 0 |
| self.messages: List[Dict[str, str]] = [] |
| self.system_message = self._build_system_message() |
| self._semantic_enabled_roles: set[str] = set() |
|
|
| def _build_system_message(self) -> str: |
| if MAIN_AGENT_ROLE_ORDER_SEED is None: |
| return main_system_prompt() |
| roles = list(PUBLIC_ROLES) |
| random.Random(MAIN_AGENT_ROLE_ORDER_SEED).shuffle(roles) |
| role_text = "\n".join( |
| f"- {item['role_name']}: {item['responsibility']}" |
| for item in roles |
| ) |
| return main_system_prompt().replace( |
| "\n".join( |
| f"- {item['role_name']}: {item['responsibility']}" |
| for item in PUBLIC_ROLES |
| ), |
| role_text, |
| ) |
|
|
| def _call_main(self) -> Dict[str, Any]: |
| if SETTING in {"missed", "mixed"} and self.messages[-1]["role"] == "assistant": |
| self.messages.append({"role": "user", "content": '{"observations":[]}'}) |
| self.empty_observation_count += 1 |
| response = self.client.chat.completions.create( |
| model=self.model, |
| messages=self.messages, |
| temperature=0.1, |
| response_format={"type": "json_object"}, |
| extra_body={"reasoning": {"effort": LLM_REASONING_EFFORT, "exclude": LLM_REASONING_EXCLUDE}}, |
| ) |
| self._last_main_parse_trace = {} |
| return parse_json_message(response.choices[0].message.content, trace=self._last_main_parse_trace) |
|
|
| def _answer_question( |
| self, role_name: str, question: Dict[str, Any], |
| ) -> tuple[Dict[str, Any], bool]: |
| role_name = resolve_role_name(role_name, self.role_agents) |
| role = self.role_agents.get(role_name) |
| if role is None: |
| return {"answer": f"Error: unknown role {role_name}。"}, False |
| try: |
| normalized = validate_question( |
| question, role_name=role_name, |
| semantic_enabled=role_name in self._semantic_enabled_roles, |
| ) |
| except ValueError as error: |
| return {"answer": f"Invalid query: {error}"}, False |
| if not normalized["authorized"]: |
| return {"answer": "The key is invalid, belongs to another role, or keyless follow-up is not yet authorized. Include a complete key for this role in question text, without a separate key field."}, False |
| key = question.get("key") |
| role_question = dict(question) |
| binding = None |
| if (self.followup_resolver and key is None |
| and (SETTING != "mixed" or role_name == INCOMPLETE_INJECTION["role_name"])): |
| binding = self.followup_resolver.resolve(role_name, question['question'], self.current_turn) |
| if binding['status'] != 'resolved': |
| answer = (self.followup_resolver.catalog(role_name) if binding['status'] == 'catalog' |
| else self.followup_resolver.CLARIFY) |
| return {"answer": answer, "followup_binding": binding}, False |
| key = binding['key'] |
| |
| role_question = dict(question, key=key, question=question['question'] + ' (System-linked record: ' + key + ')') |
| allowed_keys = set(RECORD_REPLY_KEYS[key] if key is not None else ROLE_VALID_KEYS[role_name]) |
| unrelated_field = False |
| if binding and key == INCOMPLETE_INJECTION['key']: |
| hidden_topic = INCOMPLETE_INJECTION['hidden_predicate_id'].removeprefix(INCOMPLETE_INJECTION['candidate_id'] + '_') |
| unrelated_field = not binding['full'] and hidden_topic not in binding['fields'] and self.incomplete_policy.recovered_at_turn is None |
| disclosure_mode = ('incomplete_partial' if unrelated_field else self.incomplete_policy.mode(role_name, role_question, self.current_turn)) if self.incomplete_policy else None |
| if binding: |
| binding['recovery_requested'] = disclosure_mode == 'incomplete_recovery' |
| options = self.incomplete_policy.request_options(disclosure_mode, role.system_message) if disclosure_mode else {} |
| trace = role.answer_with_trace( |
| format_role_user_message(role_question), allowed_keys=allowed_keys, |
| response_mode=disclosure_mode or ("exact" if key is not None else "search"), |
| **options, |
| ) |
| if not trace["accepted"]: |
| return { |
| "answer": trace["answer"], "role_validation_trace": trace, |
| "protocol_error": "role_reply_scope_violation", |
| **({"followup_binding": binding} if binding else {}), |
| }, False |
| if disclosure_mode: |
| self.incomplete_policy.observe(disclosure_mode, self.current_turn, trace["answer"]) |
| if self.followup_resolver: |
| self.followup_resolver.observe(role_name, key, self.current_turn) |
| return { |
| "answer": trace["answer"], "role_trace": trace, |
| "authorized_exact": question.get("key") is not None, |
| **({"followup_binding": binding} if binding else {}), |
| }, False |
|
|
| def _communication_evaluation( |
| self, transcript: List[Dict[str, Any]], final_turn: int | None, |
| ) -> Dict[str, Any]: |
| metrics = communication_metrics(transcript) |
| total_questions = sum( |
| len(message.get("questions", [])) |
| for event in transcript |
| for message in event.get("main_agent_action", {}).get("messages", []) |
| ) |
| ideal = max(1, self.runtime_config["finalize_ideal_round"]) |
| turns = final_turn if final_turn is not None else len(transcript) |
| return { |
| **metrics, |
| "total_role_agent_queries": total_questions, |
| "total_questions": total_questions, |
| "finalize_ideal_round": self.runtime_config["finalize_ideal_round"], |
| "max_turns": self.max_turns, |
| "wait_turns": sum(bool(event.get("waited")) for event in transcript), |
| "empty_observation_count": self.empty_observation_count, |
| **self.neglect_policy.metrics(), |
| "delivered_reply_count": len(self.delivered_messages), |
| "pending_reply_count": self.neglect_policy.metrics()["neglect_pending_count"], |
| "efficiency": min(1.0, ideal / turns) if final_turn else 0.0, |
| } |
|
|
| def _result( |
| self, final: Dict[str, Any], transcript: List[Dict[str, Any]], final_turn: int | None, |
| ) -> Dict[str, Any]: |
| conclusion = compute_conclusion_correctness(final) |
| communication = self._communication_evaluation(transcript, final_turn) |
| communication["recovery_success"] = ( |
| recovery_success(communication, conclusion["conclusion_correctness"] == 1) |
| if ERROR_INJECTION.get("enabled") else None |
| ) |
| if self.incomplete_policy: |
| communication.update(self.followup_resolver.metrics()) |
| communication.update(self.incomplete_policy.metrics( |
| correct=conclusion["conclusion_correctness"] == 1, finalized=final_turn is not None, |
| )) |
| communication.update(incomplete_numeric_metrics( |
| INCOMPLETE_INJECTION, transcript, communication, |
| correct=conclusion["conclusion_correctness"] == 1, finalized=final_turn is not None, |
| )) |
| return { |
| "final_result": final, |
| "evaluation": { |
| "conclusion_correct": conclusion["conclusion_correct"], |
| "numeric_result_correct": conclusion["numeric_result_correct"], |
| "joint_correctness": conclusion["conclusion_correctness"], |
| "conclusion_evaluation": conclusion, |
| "communication_evaluation": communication, |
| }, |
| "transcript": transcript, |
| "runtime_config": dict(self.runtime_config), |
| "collaboration_state": { |
| "neglect_roles": self.neglect_policy.roles, |
| "delivered_messages": list(self.delivered_messages), |
| "communication_events": list(self.communication_events), |
| }, |
| } |
|
|
| run = run_direct |
|
|
|
|
| def build_task() -> str: |
| return TASK_CONTEXT.strip() + "\n\n" + TASK_QUERY.strip() |
|
|
|
|
| def create_timestamped_run_dir( |
| root: str | Path, prefix: str, started: datetime | None = None, |
| ) -> Path: |
| root_path = Path(root) |
| root_path.mkdir(parents=True, exist_ok=True) |
| timestamp = (started or datetime.now()).strftime("%m%d_%H%M") |
| base_name = f"{prefix}_{timestamp}" |
| suffix = 1 |
| while True: |
| name = base_name if suffix == 1 else f"{base_name}_{suffix:02d}" |
| candidate = root_path / name |
| try: |
| candidate.mkdir(exist_ok=False) |
| return candidate |
| except FileExistsError: |
| suffix += 1 |
|
|
|
|
| def save_single_run_result( |
| result: Dict[str, Any], output_root: str | Path | None = None, |
| ) -> Path: |
| logs_root = ( |
| Path(output_root) |
| if output_root is not None |
| else Path(__file__).resolve().parent.parent / "logs" |
| ) |
| run_dir = create_timestamped_run_dir(logs_root, "single_run") |
| result_path = run_dir / "result.json" |
| result_path.write_text( |
| json.dumps(result, ensure_ascii=False, indent=2) + "\n", |
| encoding="utf-8", |
| ) |
| return result_path |
|
|
|
|
| def main() -> None: |
| main_client = OpenAI(base_url=MAIN_AGENT_BASE_URL, api_key=MAIN_AGENT_API_KEY) |
| role_client = OpenAI(base_url=ROLE_AGENT_BASE_URL, api_key=ROLE_AGENT_API_KEY) |
| role_agents = build_role_agents(role_client) |
| agent = MainAgent(main_client, role_agents) |
| task = build_task() |
| print_key_trace("LLM CONFIG", format_llm_config()) |
| print_key_trace("AVAILABLE ROLES", format_available_roles(role_agents)) |
| print_key_trace("TASK QUERY", task) |
| result = agent.run(task) |
| result_path = save_single_run_result(result) |
| summary_title = "MISALIGNED RECOVERY SUMMARY" if SETTING in {"misaligned", "mixed"} else "TASK SUMMARY" |
| print_key_trace(summary_title, format_key_metrics(result["evaluation"])) |
| print_key_trace("RESULT SAVED", result_path) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
