Datasets:

iNLP-Lab
/

UnCoBench

from __future__ import annotations
import json
import re
from typing import Any
COMMUNICATION_ROLE_NAMES = []
GOLD = {'conclusion': 'Select', 'numeric_result': 84.0}
POSITIVE_CONCLUSION = 'Select'
NEGATIVE_CONCLUSION = 'Do not select'
NUMERIC_PRECISION = 1
PUBLIC_ROLES = [{'role_name': 'Medical assessment lead (MAL)', 'responsibility': 'Maintains specialty board verification records and clinical simulation assessment results', 'role_abbreviation': 'MAL'}, {'role_name': 'Aero-medical credentialing lead (AMCL)', 'responsibility': 'Maintains aviation medical licenses and logged transport flight hour documentation', 'role_abbreviation': 'AMCL'}, {'role_name': 'Professional conduct reviewer (PCR)', 'responsibility': 'Maintains state licensing board disciplinary history and malpractice claim records', 'role_abbreviation': 'PCR'}, {'role_name': 'Hospital executive committee liaison (HECL)', 'responsibility': 'Maintains executive governance sponsorship and institutional endorsement records', 'role_abbreviation': 'HECL'}, {'role_name': 'Executive leadership evaluator (ELE)', 'responsibility': 'Maintains structured leadership panel evaluations and crisis team management scores', 'role_abbreviation': 'ELE'}, {'role_name': 'Medical staff talent analyst (MSTA)', 'responsibility': 'Maintains external peer references and professional reputation survey indices', 'role_abbreviation': 'MSTA'}]
ROLE_KEY_PREFIXES = {'Medical assessment lead (MAL)': 'MAL-', 'Aero-medical credentialing lead (AMCL)': 'AMCL-', 'Professional conduct reviewer (PCR)': 'PCR-', 'Hospital executive committee liaison (HECL)': 'HECL-', 'Executive leadership evaluator (ELE)': 'ELE-', 'Medical staff talent analyst (MSTA)': 'MSTA-'}
ROLE_VALID_KEYS = {'Medical assessment lead (MAL)': ['MAL-PROC-7QW', 'MAL-PROC-Z9L', 'MAL-PROC-8UD', 'MAL-CLIN-1OD', 'MAL-CLIN-M9B', 'MAL-CLIN-QQG'], 'Aero-medical credentialing lead (AMCL)': ['AMCL-AERO-BN7', 'AMCL-AERO-OGQ', 'AMCL-AERO-SMC', 'AMCL-ALT-MZM', 'AMCL-ALT-KX6', 'AMCL-ALT-1OY'], 'Professional conduct reviewer (PCR)': ['PCR-ETHIC-C3W', 'PCR-ETHIC-KPX', 'PCR-ETHIC-Z24', 'PCR-DISC-GPM', 'PCR-DISC-9LM', 'PCR-DISC-2AY'], 'Hospital executive committee liaison (HECL)': ['HECL-GRANT-OP1', 'HECL-GRANT-7YW', 'HECL-GRANT-RMS', 'HECL-SPON-3GR', 'HECL-SPON-HC0', 'HECL-SPON-66T'], 'Executive leadership evaluator (ELE)': ['ELE-MENT-1L3', 'ELE-MENT-DF7', 'ELE-MENT-L7L', 'ELE-LEAD-MBU', 'ELE-LEAD-U00', 'ELE-LEAD-D9U'], 'Medical staff talent analyst (MSTA)': ['MSTA-PUB-P38', 'MSTA-PUB-SFI', 'MSTA-PUB-ZZA', 'MSTA-REP-X4U', 'MSTA-REP-XQQ', 'MSTA-REP-491']}
RECORD_REPLY_KEYS = {'MAL-CLIN-M9B': ['MAL-CLIN-M9B'], 'MAL-CLIN-QQG': ['MAL-CLIN-QQG'], 'MAL-CLIN-1OD': ['MAL-CLIN-1OD'], 'AMCL-AERO-BN7': ['AMCL-AERO-BN7'], 'AMCL-AERO-SMC': ['AMCL-AERO-SMC'], 'AMCL-AERO-OGQ': ['AMCL-AERO-OGQ'], 'PCR-DISC-2AY': ['PCR-DISC-2AY'], 'PCR-DISC-GPM': ['PCR-DISC-GPM'], 'PCR-DISC-9LM': ['PCR-DISC-9LM'], 'HECL-SPON-66T': ['HECL-SPON-66T'], 'HECL-SPON-3GR': ['HECL-SPON-3GR'], 'HECL-SPON-HC0': ['HECL-SPON-HC0'], 'ELE-LEAD-U00': ['ELE-LEAD-U00'], 'ELE-LEAD-MBU': ['ELE-LEAD-MBU'], 'ELE-LEAD-D9U': ['ELE-LEAD-D9U'], 'MSTA-REP-XQQ': ['MSTA-REP-XQQ'], 'MSTA-REP-491': ['MSTA-REP-491'], 'MSTA-REP-X4U': ['MSTA-REP-X4U'], 'MAL-PROC-7QW': ['MAL-PROC-7QW'], 'MAL-PROC-Z9L': ['MAL-PROC-Z9L'], 'MAL-PROC-8UD': ['MAL-PROC-8UD'], 'AMCL-ALT-KX6': ['AMCL-ALT-KX6'], 'AMCL-ALT-1OY': ['AMCL-ALT-1OY'], 'AMCL-ALT-MZM': ['AMCL-ALT-MZM'], 'HECL-GRANT-OP1': ['HECL-GRANT-OP1'], 'HECL-GRANT-RMS': ['HECL-GRANT-RMS'], 'HECL-GRANT-7YW': ['HECL-GRANT-7YW'], 'ELE-MENT-L7L': ['ELE-MENT-L7L'], 'ELE-MENT-1L3': ['ELE-MENT-1L3'], 'ELE-MENT-DF7': ['ELE-MENT-DF7'], 'MSTA-PUB-ZZA': ['MSTA-PUB-ZZA'], 'MSTA-PUB-SFI': ['MSTA-PUB-SFI'], 'MSTA-PUB-P38': ['MSTA-PUB-P38'], 'PCR-ETHIC-KPX': ['PCR-ETHIC-KPX'], 'PCR-ETHIC-C3W': ['PCR-ETHIC-C3W'], 'PCR-ETHIC-Z24': ['PCR-ETHIC-Z24']}
REQUIRED_KEYS = ['AMCL-AERO-BN7', 'AMCL-AERO-SMC', 'ELE-LEAD-U00', 'HECL-SPON-66T', 'MAL-CLIN-1OD', 'MAL-CLIN-M9B', 'PCR-DISC-2AY']
DISTRACTOR_KEYS = ['MSTA-PUB-P38', 'MSTA-PUB-SFI', 'MSTA-PUB-ZZA', 'MSTA-REP-491', 'MSTA-REP-X4U', 'MSTA-REP-XQQ']
SETTING = 'incomplete'

def runtime_defaults() -> dict[str, Any]:
    return {
        "setting": SETTING,
        "communication_role_names": list(COMMUNICATION_ROLE_NAMES),
        "finalize_ideal_round": 3,
        "max_turns": 6,
    }

FINALIZE_IDEAL_ROUND = runtime_defaults()["finalize_ideal_round"]
DEFAULT_MAX_TURNS = runtime_defaults()["max_turns"]
ERROR_INJECTION = {'enabled': False, 'role_name': 'Professional conduct reviewer (PCR)', 'target_key': 'PCR-DISC-2AY', 'alternate_key': 'PCR-ETHIC-KPX', 'target_context': 'final adjudicated board sanctions', 'alternate_context': 'final adjudications and pending inquiries'}
INCOMPLETE_INJECTION = {'record_id': 'rec_credentialing_officer_A', 'role_id': 'credentialing_officer', 'candidate_id': 'A', 'key': 'AMCL-AERO-BN7', 'boolean_predicate_id': 'A_flight_credential_active', 'boolean_label': 'Active flight physician credential', 'hidden_predicate_id': 'A_flight_experience_hours', 'threshold_label': 'Critical care transport flight hours', 'threshold_aliases': ['Logged transport flight hours', 'Critical care flight experience'], 'threshold_value': 1350, 'threshold_unit': 'h', 'partial_record_text': 'AMCL-AERO-BN7: Dr. Fiona Gallagher.Dr. Fiona Gallagher: Active flight physician credential = true .Source: Federal Aviation Flight Surgeon credentialing system; Scope: Rotor-wing and fixed-wing medical crew authorization; Measurement: Active authorization switch status on aero-medical flight roster.', 'alternative_outputs': [{'conclusion': 'Do not select', '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': 'Critical care transport flight hours is at most 3000 h.', 'role_name': 'Aero-medical credentialing lead (AMCL)', 'candidate_name': 'Dr. Fiona Gallagher', 'other_candidate_names': ['Dr. Marcus Vance', 'Dr. Priya Sundaram'], 'context_value': None, 'other_context_values': [], 'language': 'en'}
INCOMPLETE_FOLLOWUP_RECORDS = [{'key': 'MAL-CLIN-M9B', 'role': 'Medical assessment lead (MAL)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'board_certified', 'label': 'Board certification verified', 'aliases': ['Specialty board verification', 'Board certified status']}, {'id': 'simulation_score', 'label': 'Clinical simulation assessment score', 'aliases': ['Clinical simulation score', 'Simulation assessment rating']}]}, {'key': 'MAL-CLIN-QQG', 'role': 'Medical assessment lead (MAL)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'board_certified', 'label': 'Board certification verified', 'aliases': ['Specialty board verification', 'Board certified status']}, {'id': 'simulation_score', 'label': 'Clinical simulation assessment score', 'aliases': ['Clinical simulation score', 'Simulation assessment rating']}]}, {'key': 'MAL-CLIN-1OD', 'role': 'Medical assessment lead (MAL)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'board_certified', 'label': 'Board certification verified', 'aliases': ['Specialty board verification', 'Board certified status']}, {'id': 'simulation_score', 'label': 'Clinical simulation assessment score', 'aliases': ['Clinical simulation score', 'Simulation assessment rating']}]}, {'key': 'AMCL-AERO-BN7', 'role': 'Aero-medical credentialing lead (AMCL)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'flight_credential_active', 'label': 'Active flight physician credential', 'aliases': ['Aero-medical credential status', 'Flight physician credential active']}, {'id': 'flight_experience_hours', 'label': 'Critical care transport flight hours', 'aliases': ['Logged transport flight hours', 'Critical care flight experience']}]}, {'key': 'AMCL-AERO-SMC', 'role': 'Aero-medical credentialing lead (AMCL)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'flight_credential_active', 'label': 'Active flight physician credential', 'aliases': ['Aero-medical credential status', 'Flight physician credential active']}, {'id': 'flight_experience_hours', 'label': 'Critical care transport flight hours', 'aliases': ['Logged transport flight hours', 'Critical care flight experience']}]}, {'key': 'AMCL-AERO-OGQ', 'role': 'Aero-medical credentialing lead (AMCL)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'flight_credential_active', 'label': 'Active flight physician credential', 'aliases': ['Aero-medical credential status', 'Flight physician credential active']}, {'id': 'flight_experience_hours', 'label': 'Critical care transport flight hours', 'aliases': ['Logged transport flight hours', 'Critical care flight experience']}]}, {'key': 'PCR-DISC-2AY', 'role': 'Professional conduct reviewer (PCR)', 'candidate': 'Dr. Fiona Gallagher', 'context': 'final adjudicated board sanctions', 'fields': [{'id': 'clean_disciplinary_record', 'label': 'Clean disciplinary history verified', 'aliases': ['Disciplinary clearance', 'Absence of board disciplinary action']}]}, {'key': 'PCR-DISC-GPM', 'role': 'Professional conduct reviewer (PCR)', 'candidate': 'Dr. Marcus Vance', 'context': 'final adjudicated board sanctions', 'fields': [{'id': 'clean_disciplinary_record', 'label': 'Clean disciplinary history verified', 'aliases': ['Disciplinary clearance', 'Absence of board disciplinary action']}]}, {'key': 'PCR-DISC-9LM', 'role': 'Professional conduct reviewer (PCR)', 'candidate': 'Dr. Priya Sundaram', 'context': 'final adjudicated board sanctions', 'fields': [{'id': 'clean_disciplinary_record', 'label': 'Clean disciplinary history verified', 'aliases': ['Disciplinary clearance', 'Absence of board disciplinary action']}]}, {'key': 'HECL-SPON-66T', 'role': 'Hospital executive committee liaison (HECL)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'executive_sponsorship', 'label': 'Executive committee sponsorship approved', 'aliases': ['Executive sponsorship approval', 'Hospital committee endorsement']}]}, {'key': 'HECL-SPON-3GR', 'role': 'Hospital executive committee liaison (HECL)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'executive_sponsorship', 'label': 'Executive committee sponsorship approved', 'aliases': ['Executive sponsorship approval', 'Hospital committee endorsement']}]}, {'key': 'HECL-SPON-HC0', 'role': 'Hospital executive committee liaison (HECL)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'executive_sponsorship', 'label': 'Executive committee sponsorship approved', 'aliases': ['Executive sponsorship approval', 'Hospital committee endorsement']}]}, {'key': 'ELE-LEAD-U00', 'role': 'Executive leadership evaluator (ELE)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'leadership_score', 'label': 'Executive leadership assessment score', 'aliases': ['Executive leadership score', 'Leadership evaluation rating']}]}, {'key': 'ELE-LEAD-MBU', 'role': 'Executive leadership evaluator (ELE)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'leadership_score', 'label': 'Executive leadership assessment score', 'aliases': ['Executive leadership score', 'Leadership evaluation rating']}]}, {'key': 'ELE-LEAD-D9U', 'role': 'Executive leadership evaluator (ELE)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'leadership_score', 'label': 'Executive leadership assessment score', 'aliases': ['Executive leadership score', 'Leadership evaluation rating']}]}, {'key': 'MSTA-REP-XQQ', 'role': 'Medical staff talent analyst (MSTA)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'peer_endorsement_index', 'label': 'Peer endorsement index', 'aliases': ['Peer recommendation index', 'Professional endorsement score']}]}, {'key': 'MSTA-REP-491', 'role': 'Medical staff talent analyst (MSTA)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'peer_endorsement_index', 'label': 'Peer endorsement index', 'aliases': ['Peer recommendation index', 'Professional endorsement score']}]}, {'key': 'MSTA-REP-X4U', 'role': 'Medical staff talent analyst (MSTA)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'peer_endorsement_index', 'label': 'Peer endorsement index', 'aliases': ['Peer recommendation index', 'Professional endorsement score']}]}, {'key': 'MAL-PROC-7QW', 'role': 'Medical assessment lead (MAL)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'supplemental_clinical_evaluator', 'label': 'First-pass intubation success rate', 'aliases': []}]}, {'key': 'MAL-PROC-Z9L', 'role': 'Medical assessment lead (MAL)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'supplemental_clinical_evaluator', 'label': 'First-pass intubation success rate', 'aliases': []}]}, {'key': 'MAL-PROC-8UD', 'role': 'Medical assessment lead (MAL)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'supplemental_clinical_evaluator', 'label': 'First-pass intubation success rate', 'aliases': []}]}, {'key': 'AMCL-ALT-KX6', 'role': 'Aero-medical credentialing lead (AMCL)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'supplemental_credentialing_officer', 'label': 'Hypobaric chamber currency duration', 'aliases': []}]}, {'key': 'AMCL-ALT-1OY', 'role': 'Aero-medical credentialing lead (AMCL)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'supplemental_credentialing_officer', 'label': 'Hypobaric chamber currency duration', 'aliases': []}]}, {'key': 'AMCL-ALT-MZM', 'role': 'Aero-medical credentialing lead (AMCL)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'supplemental_credentialing_officer', 'label': 'Hypobaric chamber currency duration', 'aliases': []}]}, {'key': 'HECL-GRANT-OP1', 'role': 'Hospital executive committee liaison (HECL)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'supplemental_governance_lead', 'label': 'Secured transport medicine grant funding', 'aliases': []}]}, {'key': 'HECL-GRANT-RMS', 'role': 'Hospital executive committee liaison (HECL)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'supplemental_governance_lead', 'label': 'Secured transport medicine grant funding', 'aliases': []}]}, {'key': 'HECL-GRANT-7YW', 'role': 'Hospital executive committee liaison (HECL)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'supplemental_governance_lead', 'label': 'Secured transport medicine grant funding', 'aliases': []}]}, {'key': 'ELE-MENT-L7L', 'role': 'Executive leadership evaluator (ELE)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'supplemental_leadership_assessor', 'label': 'Supervised EMS fellows', 'aliases': []}]}, {'key': 'ELE-MENT-1L3', 'role': 'Executive leadership evaluator (ELE)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'supplemental_leadership_assessor', 'label': 'Supervised EMS fellows', 'aliases': []}]}, {'key': 'ELE-MENT-DF7', 'role': 'Executive leadership evaluator (ELE)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'supplemental_leadership_assessor', 'label': 'Supervised EMS fellows', 'aliases': []}]}, {'key': 'MSTA-PUB-ZZA', 'role': 'Medical staff talent analyst (MSTA)', 'candidate': 'Dr. Fiona Gallagher', 'context': '', 'fields': [{'id': 'supplemental_talent_analytics', 'label': 'Indexed trauma publications', 'aliases': []}]}, {'key': 'MSTA-PUB-SFI', 'role': 'Medical staff talent analyst (MSTA)', 'candidate': 'Dr. Marcus Vance', 'context': '', 'fields': [{'id': 'supplemental_talent_analytics', 'label': 'Indexed trauma publications', 'aliases': []}]}, {'key': 'MSTA-PUB-P38', 'role': 'Medical staff talent analyst (MSTA)', 'candidate': 'Dr. Priya Sundaram', 'context': '', 'fields': [{'id': 'supplemental_talent_analytics', 'label': 'Indexed trauma publications', 'aliases': []}]}, {'key': 'PCR-ETHIC-KPX', 'role': 'Professional conduct reviewer (PCR)', 'candidate': 'Dr. Fiona Gallagher', 'context': 'final adjudications and pending inquiries', 'fields': [{'id': 'clean_disciplinary_record', 'label': 'Clean disciplinary history verified', 'aliases': ['Disciplinary clearance', 'Absence of board disciplinary action']}]}, {'key': 'PCR-ETHIC-C3W', 'role': 'Professional conduct reviewer (PCR)', 'candidate': 'Dr. Marcus Vance', 'context': 'final adjudications and pending inquiries', 'fields': [{'id': 'clean_disciplinary_record', 'label': 'Clean disciplinary history verified', 'aliases': ['Disciplinary clearance', 'Absence of board disciplinary action']}]}, {'key': 'PCR-ETHIC-Z24', 'role': 'Professional conduct reviewer (PCR)', 'candidate': 'Dr. Priya Sundaram', 'context': 'final adjudications and pending inquiries', 'fields': [{'id': 'clean_disciplinary_record', 'label': 'Clean disciplinary history verified', 'aliases': ['Disciplinary clearance', 'Absence of board disciplinary action']}]}]
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 to Select or Do not select a physician candidate for the emergency air ambulance medical director position. To be eligible for selection, a candidate must meet all of the following criteria:\n- Board certification is verified.\n- The candidate holds an active flight physician credential and has logged at least 1200 h of critical care transport flight experience.\n- A clean disciplinary history is verified based on final adjudicated board sanctions.\n- Hospital executive committee sponsorship is approved.\n\nIf exactly one candidate meets all requirements, conclude Select and calculate that candidate's average assessment performance across their clinical simulation assessment score and executive leadership assessment score (in pts). If no candidate qualifies, conclude Do not select. Report the final selection conclusion and the numeric result.\n\nNumeric calculation rule: Compute only for the unique eligible candidate: ([Clinical simulation assessment score] + [Executive leadership assessment score]) / 2 (arithmetic mean). Result unit: pts. Round only the final result to 1 decimal places (ties to even). If no candidate qualifies, numeric_result is null.\n\nCandidates and records to verify:\n- Dr. Priya Sundaram：MAL-CLIN-1OD、AMCL-AERO-OGQ、PCR-DISC-9LM、HECL-SPON-HC0、ELE-LEAD-D9U、MSTA-REP-X4U\n- Dr. Fiona Gallagher：MAL-CLIN-M9B、AMCL-AERO-BN7、PCR-DISC-2AY、HECL-SPON-66T、ELE-LEAD-U00、MSTA-REP-XQQ\n- Dr. Marcus Vance：MAL-CLIN-QQG、AMCL-AERO-SMC、PCR-DISC-GPM、HECL-SPON-3GR、ELE-LEAD-MBU、MSTA-REP-491"
TASK_CONTEXT = 'This evaluation reviews physician candidates for appointment to the emergency air ambulance medical director position. The role demands verified clinical qualifications, active operational flight status, spotless regulatory records, and hospital leadership endorsement to direct high-acuity aero-medical transport services.'

"""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()
    # Parse the first complete object; never recover a nested fragment.
    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 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.',
    ])


"""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|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):
    # Preserve questions and uncertainty when splitting assertions.
    sentences = re.split(r'[。\n]|\.(?!\d)|(?<=[!?])\s+', text)
    clauses = []
    for sentence in sentences:
        if evidence_nonasserted(sentence):
            clauses.append(sentence)
        else:
            clauses.extend(re.split(r'[;；]|,?\s+\b(?:but|and)\b\s+(?![+-]?\d)', sentence, flags=re.I))
    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_unit_pattern(unit):
    # Unit names may inflect; canonical symbols remain case-sensitive.
    groups = [('h', 'hour', 'hours'), ('min', 'minute', 'minutes'), ('s', 'second', 'seconds'),
              ('kg', 'kilogram', 'kilograms'), ('m', 'meter', 'meters', 'metre', 'metres'),
              ('%', '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)) + ')'
    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]
    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):
    answer = evidence_text(answer)
    scope = evidence_scope(answer, question, profile)
    if scope != 'eligible':
        return scope
    labels = [fact.get('label', ''), *fact.get('aliases', [])]
    states = set()
    for clause in evidence_clauses(answer):
        # Bind each value to its asserted metric; nearby numbers are not evidence.
        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
        # Leave ambiguous multi-metric clauses unscored.
        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', ''))
        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(' .:')


def boolean_evidence(answer, question, profile):
    answer = evidence_text(answer)
    scope = evidence_scope(answer, question, profile)
    if scope != 'eligible':
        return scope
    labels = [profile['label'], *profile.get('aliases', [])]
    states = set()
    for clause in evidence_clauses(answer):
        plain = _evidence_predicate(clause)
        no_negative = _evidence_predicate(re.sub(_RECOVERY_NEGATIVE, ' ', clause, flags=re.I))
        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))
        predicate = r'\b(?:enabled|issued|approved|valid|clear|ready|open|active|completed|available|installed|registered|signed|granted|confirmed|operational|compliant|meets|passed|released|equipped|connected|running|accepted)\b'
        affirmative = evidence_phrase(no_negative, _evidence_predicate(profile['label'])) and bool(re.search(predicate, profile['label'], re.I))
        label_match = evidence_phrase(no_negative, _evidence_predicate(profile['label']))
        # Do not transfer negation between unrelated metrics.
        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 |= evidence_phrase(_evidence_predicate(rewritten), _evidence_predicate(profile['label']))
        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))




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:
            # Match the full role catalog before restricting to previously queried records.
            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)]
            # Ambiguous references require a key; do not infer pronouns or fuzzy matches.
            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
        # Any valid later query of the same record can recover the omitted value.
        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
        # Recovery uses numeric occurrence only, without wording, scope or unit checks.
        # Observe only accepted replies; identifiers are excluded by validate_answer.
        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):
            # Keep rejected replies in the audit trace, outside model conversation history.
            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):
        self.roles = {name: {"state": "uncontacted", "pending": {}}
                      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.
        On retry, the caller answers the 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)
            self.events.append({"kind": "neglect_ignored", "turn": turn, "role_agent": role})
            return ""
        if turn <= state["first_turn"]:
            return ""
        state.update(state="active", pending={})
        self.events.append({"kind": "neglect_delivered", "turn": turn, "role_agent": role})
        return None

    def metrics(self):
        # Keep legacy counters at zero for compatible result readers.
        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.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']
            # Resolve keys only for role requests; preserve the main question in transcripts.
            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()