| from __future__ import annotations |
| import json |
| import re |
| from typing import Any |
| COMMUNICATION_ROLE_NAMES = ['Emergency medical logistics director (MED)'] |
| GOLD = {'conclusion': 'Exists', 'numeric_result': 2450} |
| POSITIVE_CONCLUSION = 'Exists' |
| NEGATIVE_CONCLUSION = 'Does not exist' |
| NUMERIC_PRECISION = 0 |
| PUBLIC_ROLES = [{'role_name': 'Flight operations officer (FLT)', 'responsibility': 'Maintains flight corridor terrain clearance logs and rotorcraft lift ratings', 'role_abbreviation': 'FLT'}, {'role_name': 'Avionics and optical sensor specialist (AVS)', 'responsibility': 'Maintains electro-optical sensor deployment records and smoke penetration data', 'role_abbreviation': 'AVS'}, {'role_name': 'Aviation airspace coordinator (REG)', 'responsibility': 'Maintains temporary flight restrictions and airspace deconfliction approvals', 'role_abbreviation': 'REG'}, {'role_name': 'Mountain meteorology specialist (MET)', 'responsibility': 'Maintains alpine wind profiling, valley turbulence, and gust telemetry', 'role_abbreviation': 'MET'}, {'role_name': 'Rescue flight dispatch coordinator (DISP)', 'responsibility': 'Maintains dedicated air-traffic control frequencies and radio communication channels', 'role_abbreviation': 'DISP'}, {'role_name': 'Emergency medical logistics director (MED)', 'responsibility': 'Maintains required medical gear configurations and airborne intensive-care weight loads', 'role_abbreviation': 'MED'}, {'role_name': 'Ground staging area liaison (STG)', 'responsibility': 'Maintains casualty receiving zones and perimeter field triage records', 'role_abbreviation': 'STG'}] |
| ROLE_KEY_PREFIXES = {'Flight operations officer (FLT)': 'FLT-', 'Avionics and optical sensor specialist (AVS)': 'AVS-', 'Aviation airspace coordinator (REG)': 'REG-', 'Mountain meteorology specialist (MET)': 'MET-', 'Rescue flight dispatch coordinator (DISP)': 'DISP-', 'Emergency medical logistics director (MED)': 'MED-', 'Ground staging area liaison (STG)': 'STG-'} |
| ROLE_VALID_KEYS = {'Flight operations officer (FLT)': ['FLT-TERRAIN-MGU', 'FLT-TERRAIN-97R', 'FLT-TERRAIN-WSK', 'FLT-REFUEL-L4M', 'FLT-REFUEL-9KB', 'FLT-REFUEL-UQ1'], 'Avionics and optical sensor specialist (AVS)': ['AVS-OPTICS-ZHJ', 'AVS-OPTICS-WJB', 'AVS-OPTICS-O27', 'AVS-CALIB-Y84', 'AVS-CALIB-P0P', 'AVS-CALIB-20W'], 'Aviation airspace coordinator (REG)': ['REG-AIRSPACE-YJX', 'REG-AIRSPACE-DKV', 'REG-AIRSPACE-P4L', 'REG-NOTAM-UG0', 'REG-NOTAM-X0T', 'REG-NOTAM-DTH'], 'Mountain meteorology specialist (MET)': ['MET-WIND-560', 'MET-WIND-5J8', 'MET-WIND-LU7', 'MET-THERM-UTH', 'MET-THERM-MNK', 'MET-THERM-8CT'], 'Rescue flight dispatch coordinator (DISP)': ['DISP-RADIO-IFQ', 'DISP-RADIO-9GT', 'DISP-RADIO-W3W', 'DISP-RELAY-6NO', 'DISP-RELAY-G72', 'DISP-RELAY-FVH'], 'Emergency medical logistics director (MED)': ['MED-OXY-HZ1', 'MED-OXY-5OB', 'MED-OXY-W2Y', 'MED-EQUIP-DNX', 'MED-EQUIP-7PQ', 'MED-EQUIP-T7U'], 'Ground staging area liaison (STG)': ['STG-TRIAGE-C0R', 'STG-TRIAGE-QPY', 'STG-TRIAGE-KOT', 'STG-CREW-EBK', 'STG-CREW-WDA', 'STG-CREW-3U7']} |
| RECORD_REPLY_KEYS = {'FLT-TERRAIN-97R': ['FLT-TERRAIN-97R'], 'FLT-TERRAIN-WSK': ['FLT-TERRAIN-WSK'], 'FLT-TERRAIN-MGU': ['FLT-TERRAIN-MGU'], 'AVS-OPTICS-WJB': ['AVS-OPTICS-WJB'], 'AVS-OPTICS-O27': ['AVS-OPTICS-O27'], 'AVS-OPTICS-ZHJ': ['AVS-OPTICS-ZHJ'], 'REG-AIRSPACE-P4L': ['REG-AIRSPACE-P4L'], 'REG-AIRSPACE-DKV': ['REG-AIRSPACE-DKV'], 'REG-AIRSPACE-YJX': ['REG-AIRSPACE-YJX'], 'MET-WIND-5J8': ['MET-WIND-5J8'], 'MET-WIND-LU7': ['MET-WIND-LU7'], 'MET-WIND-560': ['MET-WIND-560'], 'DISP-RADIO-9GT': ['DISP-RADIO-9GT'], 'DISP-RADIO-IFQ': ['DISP-RADIO-IFQ'], 'DISP-RADIO-W3W': ['DISP-RADIO-W3W'], 'MED-EQUIP-DNX': ['MED-EQUIP-DNX'], 'MED-EQUIP-7PQ': ['MED-EQUIP-7PQ'], 'MED-EQUIP-T7U': ['MED-EQUIP-T7U'], 'STG-TRIAGE-KOT': ['STG-TRIAGE-KOT'], 'STG-TRIAGE-C0R': ['STG-TRIAGE-C0R'], 'STG-TRIAGE-QPY': ['STG-TRIAGE-QPY'], 'AVS-CALIB-P0P': ['AVS-CALIB-P0P'], 'AVS-CALIB-20W': ['AVS-CALIB-20W'], 'AVS-CALIB-Y84': ['AVS-CALIB-Y84'], 'REG-NOTAM-UG0': ['REG-NOTAM-UG0'], 'REG-NOTAM-DTH': ['REG-NOTAM-DTH'], 'REG-NOTAM-X0T': ['REG-NOTAM-X0T'], 'MET-THERM-UTH': ['MET-THERM-UTH'], 'MET-THERM-8CT': ['MET-THERM-8CT'], 'MET-THERM-MNK': ['MET-THERM-MNK'], 'DISP-RELAY-FVH': ['DISP-RELAY-FVH'], 'DISP-RELAY-G72': ['DISP-RELAY-G72'], 'DISP-RELAY-6NO': ['DISP-RELAY-6NO'], 'MED-OXY-HZ1': ['MED-OXY-HZ1'], 'MED-OXY-5OB': ['MED-OXY-5OB'], 'MED-OXY-W2Y': ['MED-OXY-W2Y'], 'STG-CREW-WDA': ['STG-CREW-WDA'], 'STG-CREW-EBK': ['STG-CREW-EBK'], 'STG-CREW-3U7': ['STG-CREW-3U7'], 'FLT-REFUEL-UQ1': ['FLT-REFUEL-UQ1'], 'FLT-REFUEL-9KB': ['FLT-REFUEL-9KB'], 'FLT-REFUEL-L4M': ['FLT-REFUEL-L4M']} |
| REQUIRED_KEYS = ['AVS-OPTICS-WJB', 'DISP-RADIO-9GT', 'FLT-REFUEL-UQ1', 'FLT-TERRAIN-WSK', 'MED-EQUIP-DNX', 'MET-WIND-5J8', 'REG-AIRSPACE-P4L', 'REG-AIRSPACE-YJX'] |
| DISTRACTOR_KEYS = ['STG-CREW-3U7', 'STG-CREW-EBK', 'STG-CREW-WDA', 'STG-TRIAGE-C0R', 'STG-TRIAGE-KOT', 'STG-TRIAGE-QPY'] |
| 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': 'Flight operations officer (FLT)', 'target_key': 'FLT-TERRAIN-97R', 'alternate_key': 'FLT-REFUEL-UQ1', 'target_context': 'Sichuan', 'alternate_context': 'Yunnan'} |
| INCOMPLETE_INJECTION = {'record_id': 'rec_avionics_sensor_A', 'role_id': 'avionics_sensor', 'candidate_id': 'A', 'key': 'AVS-OPTICS-WJB', 'boolean_predicate_id': 'A_flir_sensor_deployed', 'boolean_label': 'Forward-looking infrared sensor deployment', 'hidden_predicate_id': 'A_flir_visibility_penetration', 'threshold_label': 'Forward-looking infrared visibility penetration', 'threshold_aliases': ['FLIR visibility penetration'], 'threshold_value': 850, 'threshold_unit': 'm', 'partial_record_text': 'AVS-OPTICS-WJB: Pinnacle Pass Corridor.Pinnacle Pass Corridor: Forward-looking infrared sensor deployment = true .Source: Avionics payload inventory report; Scope: Rotorcraft airframe assigned to the corridor; Measurement: Active mounting and operational readiness of the thermal imaging turret.', 'alternative_outputs': [{'conclusion': 'Does not exist', 'numeric_result': None, 'qualified_candidate_id': None}], 'disclosure_mode': 'upper_bound', 'disclosure_version': 'truthful_bounds_v3_domain_bounds', 'disclosure_bounds': {'lower': None, 'upper': 3000}, 'coarse_statement': 'Forward-looking infrared visibility penetration is at most 3000 m.', 'role_name': 'Avionics and optical sensor specialist (AVS)', 'candidate_name': 'Pinnacle Pass Corridor', 'other_candidate_names': ['Timberline Valley Route', 'Glacier Ridge Passage'], 'context_value': None, 'other_context_values': [], 'language': 'en'} |
| INCOMPLETE_FOLLOWUP_RECORDS = [{'key': 'FLT-TERRAIN-97R', 'role': 'Flight operations officer (FLT)', 'candidate': 'Pinnacle Pass Corridor', 'context': 'Sichuan', 'fields': [{'id': 'terrain_radar_cleared', 'label': 'Terrain collision radar clearance', 'aliases': ['Terrain radar clearance']}, {'id': 'gross_lift_capacity', 'label': 'Gross rotorcraft cargo lift capacity', 'aliases': ['Gross cargo lift capacity']}]}, {'key': 'FLT-TERRAIN-WSK', 'role': 'Flight operations officer (FLT)', 'candidate': 'Timberline Valley Route', 'context': 'Sichuan', 'fields': [{'id': 'terrain_radar_cleared', 'label': 'Terrain collision radar clearance', 'aliases': ['Terrain radar clearance']}, {'id': 'gross_lift_capacity', 'label': 'Gross rotorcraft cargo lift capacity', 'aliases': ['Gross cargo lift capacity']}]}, {'key': 'FLT-TERRAIN-MGU', 'role': 'Flight operations officer (FLT)', 'candidate': 'Glacier Ridge Passage', 'context': 'Sichuan', 'fields': [{'id': 'terrain_radar_cleared', 'label': 'Terrain collision radar clearance', 'aliases': ['Terrain radar clearance']}, {'id': 'gross_lift_capacity', 'label': 'Gross rotorcraft cargo lift capacity', 'aliases': ['Gross cargo lift capacity']}]}, {'key': 'AVS-OPTICS-WJB', 'role': 'Avionics and optical sensor specialist (AVS)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'flir_sensor_deployed', 'label': 'Forward-looking infrared sensor deployment', 'aliases': ['FLIR sensor deployment']}, {'id': 'flir_visibility_penetration', 'label': 'Forward-looking infrared visibility penetration', 'aliases': ['FLIR visibility penetration']}]}, {'key': 'AVS-OPTICS-O27', 'role': 'Avionics and optical sensor specialist (AVS)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'flir_sensor_deployed', 'label': 'Forward-looking infrared sensor deployment', 'aliases': ['FLIR sensor deployment']}, {'id': 'flir_visibility_penetration', 'label': 'Forward-looking infrared visibility penetration', 'aliases': ['FLIR visibility penetration']}]}, {'key': 'AVS-OPTICS-ZHJ', 'role': 'Avionics and optical sensor specialist (AVS)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'flir_sensor_deployed', 'label': 'Forward-looking infrared sensor deployment', 'aliases': ['FLIR sensor deployment']}, {'id': 'flir_visibility_penetration', 'label': 'Forward-looking infrared visibility penetration', 'aliases': ['FLIR visibility penetration']}]}, {'key': 'REG-AIRSPACE-P4L', 'role': 'Aviation airspace coordinator (REG)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'flight_restriction_cleared', 'label': 'Civil aviation flight restriction clearance', 'aliases': ['Temporary flight restriction clearance']}]}, {'key': 'REG-AIRSPACE-DKV', 'role': 'Aviation airspace coordinator (REG)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'flight_restriction_cleared', 'label': 'Civil aviation flight restriction clearance', 'aliases': ['Temporary flight restriction clearance']}]}, {'key': 'REG-AIRSPACE-YJX', 'role': 'Aviation airspace coordinator (REG)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'flight_restriction_cleared', 'label': 'Civil aviation flight restriction clearance', 'aliases': ['Temporary flight restriction clearance']}]}, {'key': 'MET-WIND-5J8', 'role': 'Mountain meteorology specialist (MET)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'max_crosswind_gust', 'label': 'Maximum valley crosswind gust speed', 'aliases': ['Valley crosswind gust speed']}]}, {'key': 'MET-WIND-LU7', 'role': 'Mountain meteorology specialist (MET)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'max_crosswind_gust', 'label': 'Maximum valley crosswind gust speed', 'aliases': ['Valley crosswind gust speed']}]}, {'key': 'MET-WIND-560', 'role': 'Mountain meteorology specialist (MET)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'max_crosswind_gust', 'label': 'Maximum valley crosswind gust speed', 'aliases': ['Valley crosswind gust speed']}]}, {'key': 'DISP-RADIO-9GT', 'role': 'Rescue flight dispatch coordinator (DISP)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'atc_frequency_assigned', 'label': 'Dedicated air-traffic control frequency assignment', 'aliases': ['Dedicated ATC frequency assignment']}]}, {'key': 'DISP-RADIO-IFQ', 'role': 'Rescue flight dispatch coordinator (DISP)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'atc_frequency_assigned', 'label': 'Dedicated air-traffic control frequency assignment', 'aliases': ['Dedicated ATC frequency assignment']}]}, {'key': 'DISP-RADIO-W3W', 'role': 'Rescue flight dispatch coordinator (DISP)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'atc_frequency_assigned', 'label': 'Dedicated air-traffic control frequency assignment', 'aliases': ['Dedicated ATC frequency assignment']}]}, {'key': 'MED-EQUIP-DNX', 'role': 'Emergency medical logistics director (MED)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'medical_equipment_weight', 'label': 'Mandatory survival and medical equipment weight', 'aliases': ['Mandatory medical gear weight']}]}, {'key': 'MED-EQUIP-7PQ', 'role': 'Emergency medical logistics director (MED)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'medical_equipment_weight', 'label': 'Mandatory survival and medical equipment weight', 'aliases': ['Mandatory medical gear weight']}]}, {'key': 'MED-EQUIP-T7U', 'role': 'Emergency medical logistics director (MED)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'medical_equipment_weight', 'label': 'Mandatory survival and medical equipment weight', 'aliases': ['Mandatory medical gear weight']}]}, {'key': 'STG-TRIAGE-KOT', 'role': 'Ground staging area liaison (STG)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'casualty_staging_bays', 'label': 'Ground casualty staging bay count', 'aliases': ['Staging bay capacity']}]}, {'key': 'STG-TRIAGE-C0R', 'role': 'Ground staging area liaison (STG)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'casualty_staging_bays', 'label': 'Ground casualty staging bay count', 'aliases': ['Staging bay capacity']}]}, {'key': 'STG-TRIAGE-QPY', 'role': 'Ground staging area liaison (STG)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'casualty_staging_bays', 'label': 'Ground casualty staging bay count', 'aliases': ['Staging bay capacity']}]}, {'key': 'AVS-CALIB-P0P', 'role': 'Avionics and optical sensor specialist (AVS)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'supplemental_avionics_sensor', 'label': 'Sensor calibration age', 'aliases': []}]}, {'key': 'AVS-CALIB-20W', 'role': 'Avionics and optical sensor specialist (AVS)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'supplemental_avionics_sensor', 'label': 'Sensor calibration age', 'aliases': []}]}, {'key': 'AVS-CALIB-Y84', 'role': 'Avionics and optical sensor specialist (AVS)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'supplemental_avionics_sensor', 'label': 'Sensor calibration age', 'aliases': []}]}, {'key': 'REG-NOTAM-UG0', 'role': 'Aviation airspace coordinator (REG)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'supplemental_airspace_regulator', 'label': 'Active notice count', 'aliases': []}]}, {'key': 'REG-NOTAM-DTH', 'role': 'Aviation airspace coordinator (REG)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'supplemental_airspace_regulator', 'label': 'Active notice count', 'aliases': []}]}, {'key': 'REG-NOTAM-X0T', 'role': 'Aviation airspace coordinator (REG)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'supplemental_airspace_regulator', 'label': 'Active notice count', 'aliases': []}]}, {'key': 'MET-THERM-UTH', 'role': 'Mountain meteorology specialist (MET)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'supplemental_mountain_meteorology', 'label': 'Base air temperature', 'aliases': []}]}, {'key': 'MET-THERM-8CT', 'role': 'Mountain meteorology specialist (MET)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'supplemental_mountain_meteorology', 'label': 'Base air temperature', 'aliases': []}]}, {'key': 'MET-THERM-MNK', 'role': 'Mountain meteorology specialist (MET)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'supplemental_mountain_meteorology', 'label': 'Base air temperature', 'aliases': []}]}, {'key': 'DISP-RELAY-FVH', 'role': 'Rescue flight dispatch coordinator (DISP)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'supplemental_rescue_dispatch', 'label': 'Operational repeater count', 'aliases': []}]}, {'key': 'DISP-RELAY-G72', 'role': 'Rescue flight dispatch coordinator (DISP)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'supplemental_rescue_dispatch', 'label': 'Operational repeater count', 'aliases': []}]}, {'key': 'DISP-RELAY-6NO', 'role': 'Rescue flight dispatch coordinator (DISP)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'supplemental_rescue_dispatch', 'label': 'Operational repeater count', 'aliases': []}]}, {'key': 'MED-OXY-HZ1', 'role': 'Emergency medical logistics director (MED)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'supplemental_medical_logistics', 'label': 'Portable medical oxygen cylinders', 'aliases': []}]}, {'key': 'MED-OXY-5OB', 'role': 'Emergency medical logistics director (MED)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'supplemental_medical_logistics', 'label': 'Portable medical oxygen cylinders', 'aliases': []}]}, {'key': 'MED-OXY-W2Y', 'role': 'Emergency medical logistics director (MED)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'supplemental_medical_logistics', 'label': 'Portable medical oxygen cylinders', 'aliases': []}]}, {'key': 'STG-CREW-WDA', 'role': 'Ground staging area liaison (STG)', 'candidate': 'Pinnacle Pass Corridor', 'context': '', 'fields': [{'id': 'supplemental_ground_staging', 'label': 'Rostered trauma nurses', 'aliases': []}]}, {'key': 'STG-CREW-EBK', 'role': 'Ground staging area liaison (STG)', 'candidate': 'Timberline Valley Route', 'context': '', 'fields': [{'id': 'supplemental_ground_staging', 'label': 'Rostered trauma nurses', 'aliases': []}]}, {'key': 'STG-CREW-3U7', 'role': 'Ground staging area liaison (STG)', 'candidate': 'Glacier Ridge Passage', 'context': '', 'fields': [{'id': 'supplemental_ground_staging', 'label': 'Rostered trauma nurses', 'aliases': []}]}, {'key': 'FLT-REFUEL-UQ1', 'role': 'Flight operations officer (FLT)', 'candidate': 'Pinnacle Pass Corridor', 'context': 'Yunnan', 'fields': [{'id': 'terrain_radar_cleared', 'label': 'Terrain collision radar clearance', 'aliases': ['Terrain radar clearance']}, {'id': 'gross_lift_capacity', 'label': 'Gross rotorcraft cargo lift capacity', 'aliases': ['Gross cargo lift capacity']}]}, {'key': 'FLT-REFUEL-9KB', 'role': 'Flight operations officer (FLT)', 'candidate': 'Timberline Valley Route', 'context': 'Yunnan', 'fields': [{'id': 'terrain_radar_cleared', 'label': 'Terrain collision radar clearance', 'aliases': ['Terrain radar clearance']}, {'id': 'gross_lift_capacity', 'label': 'Gross rotorcraft cargo lift capacity', 'aliases': ['Gross cargo lift capacity']}]}, {'key': 'FLT-REFUEL-L4M', 'role': 'Flight operations officer (FLT)', 'candidate': 'Glacier Ridge Passage', 'context': 'Yunnan', 'fields': [{'id': 'terrain_radar_cleared', 'label': 'Terrain collision radar clearance', 'aliases': ['Terrain radar clearance']}, {'id': 'gross_lift_capacity', 'label': 'Gross rotorcraft cargo lift capacity', 'aliases': ['Gross cargo lift capacity']}]}] |
| 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 there exists a unique evacuation flight corridor that satisfies all of the following operational standards: terrain collision radar clearance in the Sichuan operational sector is confirmed; forward-looking infrared sensor deployment is active and forward-looking infrared visibility penetration is at least 850 m; civil aviation flight restriction clearance is granted; maximum valley crosswind gust speed is at most 25 kn; and dedicated air-traffic control frequency assignment is secured. If such a corridor exists, state "Exists" and calculate its effective evacuation payload capacity, defined as gross rotorcraft cargo lift capacity in the Sichuan operational sector minus mandatory survival and medical equipment weight, expressed in kg. Otherwise, state "Does not exist".\n\nNumeric calculation rule: Compute only for the unique eligible candidate: [Gross rotorcraft cargo lift capacity] - [Mandatory survival and medical equipment weight]. Result unit: kg. Round only the final result to 0 decimal places (ties to even). If no candidate qualifies, numeric_result is null.\n\nCandidates and records to verify:\n- Glacier Ridge Passage：FLT-TERRAIN-MGU、AVS-OPTICS-ZHJ、REG-AIRSPACE-YJX、MET-WIND-560、DISP-RADIO-W3W、MED-EQUIP-T7U、STG-TRIAGE-QPY\n- Timberline Valley Route：FLT-TERRAIN-WSK、AVS-OPTICS-O27、REG-AIRSPACE-DKV、MET-WIND-LU7、DISP-RADIO-IFQ、MED-EQUIP-7PQ、STG-TRIAGE-C0R\n- Pinnacle Pass Corridor：FLT-REFUEL-UQ1、AVS-OPTICS-WJB、REG-AIRSPACE-P4L、MET-WIND-5J8、DISP-RADIO-9GT、MED-EQUIP-DNX、STG-TRIAGE-KOT' |
| TASK_CONTEXT = 'During emergency wildland fire operations, incident commanders assess potential air evacuation flight corridors to ensure rotorcraft can safely extricate trapped personnel through mountainous terrain under severe smoke and thermal updrafts. Operational evaluation requires verifying navigation safety bounds, avionics and sensor readiness, regulatory airspace clearances, and localized weather limits to establish flight viability.' |
|
|
| """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': 'Flight operations officer (FLT)', 'target_key': 'FLT-TERRAIN-97R', 'alternate_key': 'FLT-REFUEL-UQ1', 'target_context': 'Sichuan', 'alternate_context': 'Yunnan', 'candidate': 'Pinnacle Pass Corridor', 'other_candidates': ['Timberline Valley Route', 'Glacier Ridge Passage'], 'numeric': [{'topic': 'gross_lift_capacity', 'value': 3100, 'label': 'Gross rotorcraft cargo lift capacity', 'aliases': ['Gross cargo lift capacity'], 'unit': 'kg'}], 'boolean': [], 'language': 'en', 'facts': [{'topic': 'terrain_radar_cleared', 'label': 'Terrain collision radar clearance', 'aliases': ['Terrain radar clearance']}, {'topic': 'flir_sensor_deployed', 'label': 'Forward-looking infrared sensor deployment', 'aliases': ['FLIR sensor deployment']}, {'topic': 'flir_visibility_penetration', 'label': 'Forward-looking infrared visibility penetration', 'aliases': ['FLIR visibility penetration']}, {'topic': 'flight_restriction_cleared', 'label': 'Civil aviation flight restriction clearance', 'aliases': ['Temporary flight restriction clearance']}, {'topic': 'max_crosswind_gust', 'label': 'Maximum valley crosswind gust speed', 'aliases': ['Valley crosswind gust speed']}, {'topic': 'atc_frequency_assigned', 'label': 'Dedicated air-traffic control frequency assignment', 'aliases': ['Dedicated ATC frequency assignment']}, {'topic': 'gross_lift_capacity', 'label': 'Gross rotorcraft cargo lift capacity', 'aliases': ['Gross cargo lift capacity']}, {'topic': 'medical_equipment_weight', 'label': 'Mandatory survival and medical equipment weight', 'aliases': ['Mandatory medical gear weight']}]} |
| """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() |
|
|
