# -*- coding: utf-8 -*-
"""R3-Free headless SmartPaper generation runner.

The runner contains no Streamlit calls. It is safe to execute in a subprocess
whose lifetime is independent from Streamlit ScriptRunner reruns and browser
connections. Checkpoints are task-scoped, immutable-by-sequence JSON snapshots.
"""
from __future__ import annotations

import hashlib
import inspect
import json
import logging
import math
import os
import queue
import re
import threading
import time
from concurrent.futures import Future
from datetime import datetime, timezone, timedelta
from pathlib import Path
from typing import Callable, Any

from config import APP_CONFIG, AMJ_PROMPTS, ASQ_PROMPTS
from core.checkpoint_store import TaskCheckpointStore
from core.meta_extractor import extract_golden_metadata
from core.pipeline_agent import SSciPipelineAgent, PipelineCancelledError
from core.production_guard import build_validated_package, replace_abstract
from core.qualitative_engine import get_qualitative_engine
from core.quantitative_engine import get_quantitative_engine
from core.rag_engine import initialize_rag
from utils.file_parser import extract_text_from_files

ProgressCallback = Callable[..., None]

MIN_FINAL_REFERENCES = 50
LITERATURE_EVIDENCE_TARGET = 75
CITATION_PLAN_TARGET = 70
TRANSLATION_CONTRACT_VERSION = 'hierarchical-multilingual-v5'


def _clean_trailing_references(text, keywords=("reference", "references", "reference list", "参考文献")):
    lines = str(text or "").split("\n")
    for i in range(len(lines) - 1, max(-1, len(lines) - 100), -1):
        if any(re.match(rf"^##?\s*{kw}", lines[i], re.IGNORECASE) for kw in keywords):
            return "\n".join(lines[:i]).strip()
    return str(text or "").strip()


def _clean_markdown_headings(text):
    return re.sub(r"(?m)^#+\s+(.*)$", r"**\1**", str(text or ""))


def _canonical_request_hash(request_data: dict[str, Any]) -> str:
    material = dict(request_data or {})
    material.pop("request_hash", None)
    encoded = json.dumps(material, sort_keys=True, ensure_ascii=False, separators=(",", ":")).encode("utf-8")
    return hashlib.sha256(encoded).hexdigest()


class _OrderedTranslationWorker:
    """One daemon worker owned by the headless Executor process.

    It intentionally has no checkpoint authority.  Results are returned through
    Future objects and are checkpointed only by GenerationRunner's main thread.
    """

    def __init__(self, handler):
        self._handler = handler
        self._queue = queue.Queue()
        self._stop = threading.Event()
        # Lifecycle closure and task cancellation are deliberately distinct.
        # A graceful drain/close before the strict final translation gate must
        # not poison the translation agent with E_TASK_CANCELLED.
        self._cancel = threading.Event()
        self._thread = threading.Thread(
            target=self._run,
            name="smartpaper-translation-worker",
            daemon=True,
        )
        self._thread.start()

    @property
    def stop_event(self):
        return self._stop

    @property
    def cancel_event(self):
        return self._cancel

    def submit(self, job: dict[str, Any]) -> Future:
        future = Future()
        self._queue.put((future, dict(job)))
        return future

    def _run(self):
        while True:
            item = self._queue.get()
            try:
                if item is None:
                    return
                future, job = item
                if not future.set_running_or_notify_cancel():
                    continue
                try:
                    result = self._handler(job)
                except BaseException as error:
                    future.set_exception(error)
                else:
                    future.set_result(result)
            finally:
                self._queue.task_done()

    def close(self, *, wait: bool = False) -> None:
        if self._stop.is_set():
            if wait and self._thread.is_alive():
                self._thread.join(timeout=2.0)
            return
        # wait=True is the normal final-gate path after all queued work has
        # been collected.  Only an abortive/non-waiting shutdown signals the
        # translation agent to cancel an in-flight model call.
        if not wait:
            self._cancel.set()
        self._stop.set()
        while True:
            try:
                item = self._queue.get_nowait()
            except queue.Empty:
                break
            try:
                if item is not None:
                    future, _job = item
                    future.cancel()
            finally:
                self._queue.task_done()
        self._queue.put(None)
        if wait and self._thread.is_alive():
            self._thread.join(timeout=2.0)


class GenerationRunner:
    def __init__(
        self,
        *,
        task_dir: str | os.PathLike,
        request_data: dict[str, Any],
        execution_epoch: int,
        progress_callback: ProgressCallback | None = None,
        cancel_checker: Callable[[], bool] | None = None,
    ):
        self.task_dir = Path(task_dir)
        self.request = dict(request_data)
        self.execution_epoch = int(execution_epoch)
        self.progress_callback = progress_callback or (lambda **_: None)
        self.cancel_checker = cancel_checker or (lambda: False)
        expected_hash = str(self.request.get("request_hash") or "")
        actual_hash = _canonical_request_hash(self.request)
        if not expected_hash or expected_hash != actual_hash:
            raise RuntimeError("request_hash_mismatch")
        self.request_hash = expected_hash
        self.checkpoints = TaskCheckpointStore(self.task_dir, self.request_hash, self.execution_epoch)
        self.output_dir = self.task_dir / "outputs"
        self.output_dir.mkdir(parents=True, exist_ok=True)
        self.agent = None
        self.translation_agent = None
        self._preview_lock = threading.RLock()
        preview_token = str(self.request.get("preview_token") or "").strip()
        self.preview_token = preview_token if re.fullmatch(r"[A-Za-z0-9_-]{32,128}", preview_token) else ""
        self.preview_path = None
        self._last_preview_write = 0.0
        self._preview_english_text = ""
        self._preview_english_stage = ""
        self._preview_english_complete = False
        self._preview_target_text = ""
        self._preview_target_stage = "translation_waiting"
        self._preview_target_complete = False
        self._preview_target_english_commit_text = ""
        self._translation_worker = None
        self._translation_worker_sections = {}
        self._translation_worker_glossary = {}
        self._translation_worker_jobs = []
        self._translation_worker_collected = 0
        self._translation_worker_pending = set()
        self._translation_worker_context = {}
        # Display-only observability. These counters never decide task success,
        # checkpoint authority, quota, or translation reuse.
        self._translation_progress_sections = {}
        self._translation_progress_queued = set()
        self._translation_progress_inflight = set()
        self._translation_progress_total = 0
        self._qualitative_quote_sources = {}
        if self.preview_token:
            preview_dir = Path(__file__).resolve().parents[1] / "static" / "previews"
            preview_dir.mkdir(parents=True, exist_ok=True)
            self.preview_path = preview_dir / f"{self.preview_token}.json"
            # Best-effort cleanup of stale display-only previews; task/checkpoint
            # authority is unaffected by this cache.
            cutoff = time.time() - 24 * 3600
            for old_preview in preview_dir.glob("*.json"):
                try:
                    if old_preview.stat().st_mtime < cutoff:
                        old_preview.unlink()
                except OSError:
                    pass

    def _compute_request_counts(self) -> tuple[int, int, int]:
        """Return UI metrics: DeepSeek requests and eligible literature results."""
        agent = self.agent
        if agent is None:
            return 0, 0, 0
        metrics = getattr(agent, "source_metrics", {}) or {}
        s2_metric = metrics.get("s2", {}) if isinstance(metrics, dict) else {}
        oa_metric = metrics.get("oa", {}) if isinstance(metrics, dict) else {}
        translator_calls = int(getattr(getattr(self, "translation_agent", None), "ds_calls", 0) or 0)
        return (
            int(getattr(agent, "ds_calls", 0) or 0) + translator_calls,
            int(s2_metric.get("eligible_result_count", 0) or 0),
            int(oa_metric.get("eligible_result_count", 0) or 0),
        )

    def _progress(self, pct: int, node: str, *, checkpoint_seq=None, checkpoint_stage=None) -> None:
        ds_calls, s2_calls, oa_calls = self._compute_request_counts()
        self.progress_callback(
            progress_pct=max(0, min(100, int(pct))),
            current_node=str(node or "")[:240],
            checkpoint_seq=checkpoint_seq,
            checkpoint_stage=checkpoint_stage,
            ds_calls=ds_calls,
            s2_calls=s2_calls,
            oa_calls=oa_calls,
        )

    def _checkpoint(self, stage: str, state: dict[str, Any], pct: int) -> None:
        seq, normalized_stage = self.checkpoints.save(stage, state)
        self._progress(pct, normalized_stage, checkpoint_seq=seq, checkpoint_stage=normalized_stage)

    @staticmethod
    def _preview_visible_text(value: str) -> str:
        text = str(value or "")
        # Never expose hidden reasoning. Raw CITE placeholders are also hidden
        # from the user because they have not passed semantic citation QA yet;
        # the authoritative manuscript buffer remains untouched.
        text = re.sub(r"(?is)<think>.*?(?:</think>|$)", "", text)
        text = re.sub(r"\s*\[?CITE-\d+\]?\s*", " ", text, flags=re.I)
        text = re.sub(r"[ \t]{2,}", " ", text)
        return text.strip()

    @staticmethod
    def _build_qualitative_quote_ownership(tasks, data_metadata):
        """Freeze one and only one Findings owner for every qualitative quote."""
        evidence_by_tag = {
            str(item.get("tag") or "").strip(): dict(item)
            for item in (data_metadata or {}).get("evidence", [])
            if isinstance(item, dict) and str(item.get("tag") or "").strip()
        }
        owners = {}
        for task in tasks or []:
            if str(task.get("section_id") or "") != "findings":
                continue
            owner = str(task.get("display_name") or "").strip()
            for item in task.get("theme_evidence") or []:
                if not isinstance(item, dict):
                    continue
                tag = str(item.get("tag") or "").strip()
                if not tag:
                    continue
                if tag not in evidence_by_tag:
                    raise RuntimeError(f"Qualitative quotation ownership references unknown tag: {tag}")
                if tag in owners:
                    raise RuntimeError(
                        f"Qualitative quotation has multiple Findings owners: {tag} -> {owners[tag]!r}, {owner!r}"
                    )
                owners[tag] = owner
        missing = sorted(set(evidence_by_tag) - set(owners))
        if missing:
            raise RuntimeError(f"Qualitative quotations lack a Findings owner: {missing[:8]}")
        return owners

    @staticmethod
    def _qualitative_task_key(task):
        task = task or {}
        explicit = str(task.get("task_key") or "").strip()
        if explicit:
            return explicit
        section_id = str(task.get("section_id") or task.get("section") or "section").strip()
        stable_unit = str(task.get("theme_id") or task.get("subsection_id") or section_id).strip()
        part_index = int(task.get("part_index") or 0) + 1
        return f"{section_id}:{stable_unit}:part:{part_index:02d}"

    @classmethod
    def _build_qualitative_quote_invocation_plan(cls, tasks, data_metadata):
        """Freeze one deterministic body invocation for every retained quote.

        Logical ownership is a stable Theme object. Runtime Part chunks only
        select where the already-owned quote is rendered; they never become the
        quote owner and can never change cardinality.
        """
        metadata = data_metadata or {}
        evidence_by_tag = {
            str(item.get("tag") or "").strip(): dict(item)
            for item in metadata.get("evidence", [])
            if isinstance(item, dict) and str(item.get("tag") or "").strip()
        }
        theme_id_by_title = {}
        for index, theme in enumerate(metadata.get("themes") or [], start=1):
            if not isinstance(theme, dict):
                continue
            title = re.sub(r"\s+", " ", str(theme.get("title") or "")).strip().casefold()
            if title:
                theme_id_by_title[title] = str(theme.get("theme_id") or f"THEME-{index:02d}").strip()

        plan = {}
        task_slots = {}
        for task in tasks or []:
            if str(task.get("section_id") or "") != "findings":
                continue
            task_key = cls._qualitative_task_key(task)
            task_theme_id = str(task.get("theme_id") or "").strip()
            task_theme_title = str(task.get("theme_title") or task.get("subsection") or "").strip()
            for item in task.get("theme_evidence") or []:
                if not isinstance(item, dict):
                    continue
                tag = str(item.get("tag") or "").strip()
                if not tag:
                    continue
                evidence = evidence_by_tag.get(tag)
                if evidence is None:
                    raise RuntimeError(f"Qualitative quote plan references unknown evidence: {tag}")
                if tag in plan:
                    raise RuntimeError(f"Qualitative quote plan has duplicate render assignment: {tag}")
                owner_theme_id = str(
                    task_theme_id
                    or evidence.get("theme_id")
                    or theme_id_by_title.get(
                        re.sub(r"\s+", " ", str(evidence.get("theme") or task_theme_title)).strip().casefold()
                    )
                    or ""
                ).strip()
                if not owner_theme_id:
                    raise RuntimeError(f"Qualitative quote lacks a stable theme owner: {tag}")
                quote = str(evidence.get("quote") or "").strip()
                if not quote:
                    raise RuntimeError(f"Qualitative quote is empty: {tag}")
                quote_hash = str(evidence.get("quote_hash") or "").strip() or hashlib.sha256(
                    re.sub(r"\s+", " ", quote).strip().encode("utf-8")
                ).hexdigest()
                slot_ordinal = task_slots.get(task_key, 0) + 1
                task_slots[task_key] = slot_ordinal
                plan[tag] = {
                    "quote_id": tag,
                    "quote_hash": quote_hash,
                    "owner_theme_id": owner_theme_id,
                    "owner_theme_title": str(evidence.get("theme") or task_theme_title).strip(),
                    "render_task_key": task_key,
                    "render_part_index": int(task.get("part_index") or 0),
                    "render_part_count": int(task.get("part_count") or 1),
                    "slot_ordinal": slot_ordinal,
                    "cardinality": 1,
                }

        missing = sorted(set(evidence_by_tag) - set(plan))
        extra = sorted(set(plan) - set(evidence_by_tag))
        if missing or extra:
            raise RuntimeError(
                f"Qualitative quote invocation plan mismatch: missing={missing[:8]} extra={extra[:8]}"
            )
        cls._validate_qualitative_quote_invocation_plan(plan, tasks, metadata)
        return plan

    @classmethod
    def _validate_qualitative_quote_invocation_plan(cls, plan, tasks, data_metadata):
        metadata = data_metadata or {}
        evidence_by_tag = {
            str(item.get("tag") or "").strip(): dict(item)
            for item in metadata.get("evidence", [])
            if isinstance(item, dict) and str(item.get("tag") or "").strip()
        }
        task_by_key = {cls._qualitative_task_key(task): task for task in tasks or []}
        if set(plan or {}) != set(evidence_by_tag):
            raise RuntimeError("Qualitative quote invocation plan does not match the frozen evidence ledger.")
        for tag, record in (plan or {}).items():
            if int(record.get("cardinality") or 0) != 1:
                raise RuntimeError(f"Qualitative quote cardinality must equal one: {tag}")
            owner_theme_id = str(record.get("owner_theme_id") or "").strip()
            if not owner_theme_id or "part " in owner_theme_id.casefold():
                raise RuntimeError(f"Qualitative quote owner is not a stable Theme object: {tag}")
            task_key = str(record.get("render_task_key") or "").strip()
            task = task_by_key.get(task_key)
            if not task or str(task.get("section_id") or "") != "findings":
                raise RuntimeError(f"Qualitative quote render task is invalid: {tag} -> {task_key!r}")
            task_theme_id = str(task.get("theme_id") or "").strip()
            if task_theme_id and task_theme_id != owner_theme_id:
                raise RuntimeError(f"Qualitative quote render task crossed theme ownership: {tag}")
            evidence = evidence_by_tag[tag]
            quote = str(evidence.get("quote") or "").strip()
            expected_hash = str(evidence.get("quote_hash") or "").strip() or hashlib.sha256(
                re.sub(r"\s+", " ", quote).strip().encode("utf-8")
            ).hexdigest()
            if str(record.get("quote_hash") or "") != expected_hash:
                raise RuntimeError(f"Qualitative quote hash drifted after planning: {tag}")
        return True

    @staticmethod
    def _strip_model_controlled_qualitative_quotes(text, data_metadata):
        """Remove model-emitted quote controls/literals before Executor rendering.

        This is a containment boundary, not the quote control plane: quotation
        presence and cardinality come exclusively from the frozen invocation
        plan. The model's attempted tags/literals therefore have zero authority.
        """
        value = str(text or "")
        value = re.sub(r"(?<!\w)\[?QUOTE-\d+\]?(?!\w)", "", value, flags=re.I)
        quotes = sorted(
            {
                str(item.get("quote") or "").strip()
                for item in (data_metadata or {}).get("evidence", [])
                if isinstance(item, dict) and str(item.get("quote") or "").strip()
            },
            key=len,
            reverse=True,
        )
        for quote in quotes:
            for wrapped in (f'"{quote}"', f'“{quote}”', f'«{quote}»', quote):
                value = value.replace(wrapped, "")
        value = re.sub(r"[ \t]+([,.;:!?])", r"\1", value)
        value = re.sub(r"([,;:])\s*([.!?])", r"\2", value)
        value = re.sub(r"[ \t]{2,}", " ", value)
        value = re.sub(r"\n[ \t]+", "\n", value)
        return value.strip()

    @classmethod
    def _render_planned_qualitative_quotes(cls, text, *, task, data_metadata, quote_invocation_plan):
        """Deterministically insert frozen quotations into one rendered task."""
        value = cls._strip_model_controlled_qualitative_quotes(text, data_metadata)
        task_key = cls._qualitative_task_key(task)
        scheduled = [
            dict(record)
            for record in (quote_invocation_plan or {}).values()
            if str(record.get("render_task_key") or "") == task_key
        ]
        scheduled.sort(key=lambda record: (int(record.get("slot_ordinal") or 0), str(record.get("quote_id") or "")))
        if not scheduled:
            return value

        evidence_by_tag = {
            str(item.get("tag") or "").strip(): dict(item)
            for item in (data_metadata or {}).get("evidence", [])
            if isinstance(item, dict) and str(item.get("tag") or "").strip()
        }
        blocks = [block.strip() for block in re.split(r"\n\s*\n", value) if block.strip()]
        anchor_indices = [
            index for index, block in enumerate(blocks)
            if not re.match(r"^(?:#{1,6}\s|\||[-*+]\s|>)", block)
        ]
        if not anchor_indices:
            anchor_indices = [len(blocks) - 1] if blocks else []

        insertions = {}
        for index, record in enumerate(scheduled):
            tag = str(record.get("quote_id") or "").strip()
            quote = str((evidence_by_tag.get(tag) or {}).get("quote") or "").strip()
            if not quote:
                raise RuntimeError(f"Planned qualitative quotation is unavailable at render time: {tag}")
            if anchor_indices:
                anchor_pos = min(
                    len(anchor_indices) - 1,
                    max(0, int(((index + 1) * len(anchor_indices)) / (len(scheduled) + 1))),
                )
                block_index = anchor_indices[anchor_pos]
                insertions.setdefault(block_index, []).append(f'"{quote}"')
            else:
                blocks.append(f'"{quote}"')

        rendered_blocks = []
        for index, block in enumerate(blocks):
            rendered_blocks.append(block)
            rendered_blocks.extend(insertions.get(index, []))
        rendered = "\n\n".join(rendered_blocks).strip()
        for record in scheduled:
            tag = str(record.get("quote_id") or "").strip()
            quote = str((evidence_by_tag.get(tag) or {}).get("quote") or "").strip()
            if rendered.count(quote) != 1:
                raise RuntimeError(f"Qualitative deterministic quote rendering failed: {tag}")
        return rendered

    @staticmethod
    def _validate_qualitative_quote_rendering(text, quote_invocation_plan, data_metadata):
        body_text = re.split(
            r"(?m)^#\s+Appendix(?:[:\s].*)?$", str(text or ""), maxsplit=1
        )[0]
        unresolved = sorted(set(re.findall(r"\bQUOTE-\d+\b", body_text, flags=re.I)))
        if unresolved:
            raise RuntimeError(f"Unresolved qualitative quote controls in body: {unresolved[:8]}")
        normalized_body = re.sub(r"\s+", " ", body_text).casefold()
        evidence_by_tag = {
            str(item.get("tag") or "").strip(): dict(item)
            for item in (data_metadata or {}).get("evidence", [])
            if isinstance(item, dict) and str(item.get("tag") or "").strip()
        }
        for tag, record in (quote_invocation_plan or {}).items():
            quote = re.sub(r"\s+", " ", str((evidence_by_tag.get(tag) or {}).get("quote") or "")).strip().casefold()
            expected = int(record.get("cardinality") or 0)
            actual = normalized_body.count(quote) if quote else 0
            if actual != expected:
                raise RuntimeError(
                    f"Qualitative quote invocation invariant failed: {tag} actual={actual} planned={expected}"
                )
        return True

    @staticmethod
    def _redact_qualitative_quotes(text, data_metadata):
        """Hide prior verbatim quote text from cross-unit continuity context."""
        value = str(text or "")
        quotes = sorted(
            {
                str(item.get("quote") or "").strip()
                for item in (data_metadata or {}).get("evidence", [])
                if isinstance(item, dict) and str(item.get("quote") or "").strip()
            },
            key=len,
            reverse=True,
        )
        for quote in quotes:
            value = value.replace(f'"{quote}"', "[prior qualitative quotation omitted]")
            value = value.replace(quote, "[prior qualitative quotation omitted]")
        return value

    @classmethod
    def _bind_owned_qualitative_quotes(
        cls,
        text,
        *,
        display_name,
        data_metadata,
        quote_ownership,
        prior_manuscript="",
    ):
        """Bind only the current Findings unit's owned quotation tags.

        Other manuscript units are not allowed to contain frozen quote text or
        quote tags.  A direct verbatim reproduction of an owned quote is
        normalized back to its tag before deterministic binding, preserving the
        frozen wording without letting model prose create a second copy.
        """
        value = str(text or "")
        evidence_by_tag = {
            str(item.get("tag") or "").strip(): dict(item)
            for item in (data_metadata or {}).get("evidence", [])
            if isinstance(item, dict) and str(item.get("tag") or "").strip()
        }
        allowed = {
            tag for tag, owner in (quote_ownership or {}).items()
            if str(owner) == str(display_name)
        }

        raw_tags = set(re.findall(r"\bQUOTE-\d+\b", value))
        unauthorized_tags = sorted(raw_tags - allowed)
        if unauthorized_tags:
            raise RuntimeError(
                f"Qualitative quotation tag used outside owning Findings unit {display_name!r}: {unauthorized_tags[:8]}"
            )

        # Quote text itself is also fenced.  Owned verbatim text is converted
        # back to a tag and then bound once; unowned verbatim text fails early.
        for tag, item in evidence_by_tag.items():
            quote = str(item.get("quote") or "").strip()
            if not quote or quote not in value:
                continue
            if tag not in allowed:
                raise RuntimeError(
                    f"Qualitative quotation text used outside owning Findings unit: {tag} in {display_name!r}"
                )
            value = value.replace(f'"{quote}"', tag)
            value = value.replace(quote, tag)

        # A quote already present in frozen prior prose cannot be rebound later,
        # even after resume. This makes ownership idempotent without new state.
        normalized_prior = re.sub(r"\s+", " ", str(prior_manuscript or "")).casefold()
        for tag in sorted(allowed):
            quote = str((evidence_by_tag.get(tag) or {}).get("quote") or "").strip()
            if quote and re.sub(r"\s+", " ", quote).casefold() in normalized_prior:
                raise RuntimeError(f"Qualitative quotation already used before owning unit: {tag}")

        for tag in sorted(allowed):
            tag_pattern = rf"(?<!\w)\[?{re.escape(tag)}\]?(?!\w)"
            count = len(re.findall(tag_pattern, value))
            if count > 1:
                raise RuntimeError(
                    f"Qualitative quotation repeated within owning Findings unit: {tag} count={count}"
                )
            if count == 1:
                quote = str((evidence_by_tag.get(tag) or {}).get("quote") or "").strip()
                value = re.sub(
                    tag_pattern,
                    lambda _match, frozen_quote=quote: f'"{frozen_quote}"',
                    value,
                    count=1,
                )

        # No unresolved quote tag may escape into citation QA or final output.
        unresolved = sorted(set(re.findall(r"\bQUOTE-\d+\b", value)))
        if unresolved:
            raise RuntimeError(f"Unresolved qualitative quotation tags: {unresolved[:8]}")
        return value

    @staticmethod
    def _qualitative_quote_source_map(data_metadata):
        return {
            str(item.get("tag") or "").strip(): str(item.get("quote") or "").strip()
            for item in (data_metadata or {}).get("evidence", [])
            if isinstance(item, dict)
            and str(item.get("tag") or "").strip()
            and str(item.get("quote") or "").strip()
        }

    @staticmethod
    def _qualitative_quote_ledger_identity_ok(
        record, *, target_lang: str, request_hash: str, source_quote_hashes: dict,
        task_id: str = "", translation_contract_version: str = TRANSLATION_CONTRACT_VERSION,
    ) -> bool:
        """Type-safe identity gate for task-level target-language Quote reuse.

        Missing scalar identity fields remain backward-compatible with pre-R4
        checkpoints. Present fields must be strings and match exactly. Quote
        hashes must be a mapping and match the current frozen English evidence.
        execution_epoch is deliberately not part of logical identity so a newer
        epoch can reuse the same QA-passed target rendering for the same task.
        """
        if not isinstance(record, dict) or not isinstance(source_quote_hashes, dict):
            return False

        def scalar_matches(stored, current: str) -> bool:
            if stored is None or stored == "":
                return True
            return isinstance(stored, str) and stored == str(current or "")

        stored_hashes = record.get("source_quote_hashes")
        if stored_hashes is None or stored_hashes == "":
            hashes_match = True
        else:
            hashes_match = isinstance(stored_hashes, dict) and stored_hashes == source_quote_hashes

        return (
            scalar_matches(record.get("task_id"), task_id)
            and scalar_matches(record.get("target_lang"), target_lang)
            and scalar_matches(record.get("request_hash"), request_hash)
            and scalar_matches(record.get("translation_contract_version"), translation_contract_version)
            and hashes_match
        )

    @classmethod
    def _prepare_qualitative_translation_source(cls, source, quote_sources, quote_ledger):
        """Replace every frozen qualitative quote with a typed semantic slot.

        QUOTE identity is never inferred from translated narrative surface text.
        Missing target objects are classified as pending and translated through
        the dedicated typed-object path before the surrounding narrative runs.
        """
        value = str(source or "")
        pending = []
        reused = []
        for tag, quote in sorted((quote_sources or {}).items()):
            wrapped = f'"{quote}"'
            tagged = f'{wrapped} [{tag}]'
            if tagged in value:
                value = value.replace(tagged, f"[{tag}]")
                (reused if tag in (quote_ledger or {}) else pending).append(tag)
                continue
            if wrapped not in value:
                continue
            value = value.replace(wrapped, f"[{tag}]")
            (reused if tag in (quote_ledger or {}) else pending).append(tag)
        return value, pending, reused

    @classmethod
    def _finalize_qualitative_translation_quotes(cls, translated, pending, reused, quote_ledger):
        value = str(translated or "")
        ledger = dict(quote_ledger or {})
        ordered_tags = list(dict.fromkeys(list(pending or []) + list(reused or [])))
        for tag in ordered_tags:
            rendered_quote = str(ledger.get(tag) or "").strip()
            if not rendered_quote:
                raise RuntimeError(f"Qualitative quote translation ledger missing target text: {tag}")
            pattern = rf"(?<!\w)\[?{re.escape(tag)}\]?(?!\w)"
            count = len(re.findall(pattern, value))
            if count != 1:
                raise RuntimeError(
                    f"Qualitative quote deterministic target slot injection failed: {tag} matches={count}"
                )
            value = re.sub(pattern, lambda _m, q=rendered_quote: q, value, count=1)

        unresolved = sorted(set(re.findall(r"\bQUOTE-\d+\b", value)))
        if unresolved:
            raise RuntimeError(f"Unresolved target-language qualitative quote tags: {unresolved[:8]}")
        return value, ledger

    def _translation_progress_snapshot(self) -> dict[str, int]:
        """Return non-authoritative logical target-unit progress for the UI."""
        mapping = dict(getattr(self, '_translation_progress_sections', {}) or {})
        context = getattr(self, '_translation_worker_context', {}) or {}
        tasks = context.get('adaptive_structure') or [] if isinstance(context, dict) else []
        logical_keys = []
        if tasks:
            logical_keys = ['__front_matter__'] + [
                key for key, _title in self._unique_top_level_sections(tasks)
            ]
            logical_keys = list(dict.fromkeys(key for key in logical_keys if key))
        total = int(getattr(self, '_translation_progress_total', 0) or len(logical_keys))
        if total <= 0 and logical_keys:
            total = len(logical_keys)
        qa_passed = 0
        repair_pending = 0
        inflight = set(getattr(self, '_translation_progress_inflight', set()) or set())
        for key in logical_keys:
            record = mapping.get(key) or {}
            status = str(record.get('qa_status') or '')
            if key not in inflight and status in {'passed', 'passed_with_warning'} and record.get('translated_text'):
                qa_passed += 1
            elif status == 'repair_pending':
                repair_pending += 1
        queued = len(set(getattr(self, '_translation_progress_queued', set()) or set()).intersection(logical_keys))
        in_flight = len(inflight.intersection(logical_keys))
        return {
            'qa_passed': min(qa_passed, total) if total else qa_passed,
            'repair_pending': min(repair_pending, total) if total else repair_pending,
            'queued': min(queued, total) if total else queued,
            'in_flight': min(in_flight, total) if total else in_flight,
            'total': max(0, total),
        }

    def _write_preview_payload(self, *, force: bool = False) -> None:
        with self._preview_lock:
            if self.preview_path is None:
                return
            now = time.monotonic()
            if not force and now - self._last_preview_write < 0.45:
                return
            payload = {
                "task_id": str(self.request.get("task_id") or ""),
                "request_hash": self.request_hash,
                "execution_epoch": self.execution_epoch,
                "stage": str(self._preview_english_stage or "")[:240],
                "text": self._preview_visible_text(self._preview_english_text),
                "complete": bool(self._preview_english_complete),
                "target_stage": str(self._preview_target_stage or "")[:240],
                "target_text": self._preview_visible_text(self._preview_target_text),
                "target_complete": bool(self._preview_target_complete),
                "target_english_commit_text": self._preview_visible_text(
                    self._preview_target_english_commit_text
                ),
                "target_progress": self._translation_progress_snapshot(),
                "updated_at": datetime.now(timezone.utc).isoformat(),
            }
            temporary = self.preview_path.with_suffix(".json.tmp")
            temporary.write_text(json.dumps(payload, ensure_ascii=False), encoding="utf-8")
            os.replace(temporary, self.preview_path)
            self._last_preview_write = now

    def _publish_preview(self, text: str, stage: str, *, force: bool = False, complete: bool = False) -> None:
        with self._preview_lock:
            self._preview_english_text = str(text or "")
            self._preview_english_stage = str(stage or "")
            self._preview_english_complete = bool(complete)
            self._write_preview_payload(force=force)

    def _publish_target_preview(
        self, text: str, stage: str, *, force: bool = True, complete: bool = False,
        english_commit_text: str | None = None,
    ) -> None:
        with self._preview_lock:
            self._preview_target_text = str(text or "")
            self._preview_target_stage = str(stage or "")
            self._preview_target_complete = bool(complete)
            if english_commit_text is not None:
                self._preview_target_english_commit_text = str(english_commit_text or "")
            self._write_preview_payload(force=force)

    def _stream_text(self, generator, *, preview_prefix: str | None = None, preview_stage: str = "") -> str:
        chunks = []
        for chunk in generator:
            value = str(chunk or "")
            if not value:
                continue
            chunks.append(value)
            if preview_prefix is not None:
                self._publish_preview(preview_prefix + "".join(chunks), preview_stage, force=False)
        text = "".join(chunks)
        if preview_prefix is not None:
            self._publish_preview(preview_prefix + text, preview_stage, force=True)
        return text

    @staticmethod
    def _text_hash(value: str) -> str:
        normalized = re.sub(r'\s+', ' ', str(value or '')).strip()
        return hashlib.sha256(normalized.encode('utf-8')).hexdigest()

    @staticmethod
    def _unique_top_level_sections(tasks):
        output = []
        seen = set()
        for task in tasks or []:
            key = str(task.get('section_id') or task.get('section') or '').strip()
            title = str(task.get('section') or '').strip()
            if key and title and key not in seen:
                seen.add(key); output.append((key, title))
        return output

    @staticmethod
    def _extract_top_level_section(manuscript: str, title: str) -> str:
        value = str(manuscript or '')
        match = re.search(
            rf'(?ms)^#\s+{re.escape(str(title))}\s*$\n(.*?)(?=^#\s+[^#].*?$|\Z)', value
        )
        if not match:
            return ''
        return f"# {title}\n" + match.group(1).strip()

    @staticmethod
    def _english_prefix_through_top_level(manuscript: str, title: str) -> str:
        value = str(manuscript or '')
        match = re.search(
            rf'(?ms)^#\s+{re.escape(str(title))}\s*$\n.*?(?=^#\s+[^#].*?$|\Z)', value
        )
        return value[:match.end()].strip() if match else value.strip()

    @staticmethod
    def _extract_front_matter(manuscript: str, first_section_title: str) -> str:
        value = str(manuscript or '')
        match = re.search(rf'(?m)^#\s+{re.escape(str(first_section_title))}\s*$', value)
        return value[:match.start()].strip() if match else ''

    @staticmethod
    def _replace_keywords(manuscript: str, keywords) -> str:
        value = str(manuscript or '')
        if isinstance(keywords, (list, tuple)):
            rendered = ', '.join(str(item).strip() for item in keywords if str(item).strip())
        else:
            rendered = str(keywords or '').strip()
        pattern = re.compile(r'(?ms)(^##\s+Keywords\s*$\n)(.*?)(?=^#\s+[^#].*?$|\Z)')
        matches = list(pattern.finditer(value))
        if len(matches) != 1:
            raise RuntimeError(f'keywords_heading_count_invalid:{len(matches)}')
        match = matches[0]
        return value[:match.start()] + match.group(1) + rendered + '\n\n' + value[match.end():].lstrip('\n')

    @staticmethod
    def _section_is_complete(tasks, completed_display_names, section_key: str) -> bool:
        relevant = [
            str(task.get('display_name') or '') for task in (tasks or [])
            if str(task.get('section_id') or task.get('section') or '') == str(section_key)
        ]
        done = set(str(item) for item in (completed_display_names or []))
        return bool(relevant) and all(name in done for name in relevant)

    @staticmethod
    def _glossary_version(glossary) -> str:
        encoded = json.dumps(dict(glossary or {}), ensure_ascii=False, sort_keys=True).encode('utf-8')
        return hashlib.sha256(encoded).hexdigest()[:16]

    @staticmethod
    def _build_canonical_concept_ledger(
        mode: str, data_metadata: dict, *, keywords='', adaptive_structure=None, research_logic_contract=''
    ) -> dict:
        """Freeze task-level research concept identity before body translation.

        This ledger is intentionally conservative: quantitative construct names
        come directly from frozen metadata; qualitative concept/theme labels come
        from the Evidence Ledger/outline. Lower-case hyphenated concepts used in
        the frozen research logic are added because the legacy Title-Case glossary
        discovery cannot see them. No semantic synonym merging is performed.
        """
        items = []
        seen = set()

        def add(value, kind):
            term = re.sub(r'\s+', ' ', str(value or '')).strip(' \t\r\n,;:.')
            if not term or len(term) < 3 or len(term) > 100:
                return
            words = re.findall(r"[A-Za-zÀ-ÖØ-öø-ÿА-Яа-яЁёӘәҒғҚқҢңӨөҰұҮүҺһІі][\w’'\-]*", term, flags=re.UNICODE)
            if not words or len(words) > 10:
                return
            key = re.sub(r'[–—-]+', '-', term).casefold()
            if key in seen:
                return
            seen.add(key)
            items.append({'source': term, 'kind': str(kind or 'concept')})

        meta = dict(data_metadata or {})
        if str(mode or '') == 'amj':
            constructs = meta.get('constructs') if isinstance(meta.get('constructs'), dict) else {}
            for role in ('predictor', 'mediator', 'outcome'):
                add(constructs.get(role), f'quantitative_{role}')
        else:
            for theme in (meta.get('themes') or []):
                if not isinstance(theme, dict):
                    continue
                add(theme.get('title'), 'qualitative_theme')
                for concept in (theme.get('concepts') or []):
                    add(concept, 'qualitative_concept')
            for row in (meta.get('evidence') or []):
                if isinstance(row, dict):
                    add(row.get('concept'), 'qualitative_concept')

        for task in (adaptive_structure or []):
            if not isinstance(task, dict):
                continue
            for concept in (task.get('concepts') or []):
                add(concept, 'outline_concept')

        # User keywords remain planning seeds, not canonical identities. Use
        # them only as a fallback when frozen empirical/qualitative metadata and
        # outline concepts provide no concept labels at all.
        if not items:
            for seed in re.split(r'[,;\n|]+', str(keywords or '')):
                seed = re.sub(r'\s+', ' ', seed).strip()
                if 1 <= len(seed.split()) <= 6:
                    add(seed, 'keyword_fallback')

        # Capture explicit lower-case hyphenated academic expressions only when
        # they recur or carry a research-concept cue. This closes the
        # context-awareness/context-aware blind spot without freezing generic
        # methodological adjectives such as cross-sectional by default.
        hyphenated = re.findall(r'(?<![A-Za-z])(?:[a-z][a-z]{2,}(?:-[a-z][a-z]{2,})+)(?![A-Za-z])', str(research_logic_contract or ''))
        concept_cues = {
            'awareness','trust','governance','literacy','fidelity','performance','context','capability',
            'calibration','memory','acceptance','effectiveness','efficiency','alignment','recommendation',
        }
        counts = {}
        for term in hyphenated:
            counts[term.casefold()] = counts.get(term.casefold(), 0) + 1
        for term in hyphenated:
            parts = set(term.casefold().split('-'))
            if counts.get(term.casefold(), 0) >= 2 or parts.intersection(concept_cues):
                add(term, 'logic_hyphenated_concept')

        return {'version': 'concept-ledger-v1', 'items': items}

    @staticmethod
    def _canonical_concept_writer_directive(ledger: dict) -> str:
        terms = [str(item.get('source') or '').strip() for item in (dict(ledger or {}).get('items') or []) if isinstance(item, dict)]
        terms = [term for term in terms if term]
        if not terms:
            return ''
        rendered = '; '.join(terms[:40])
        return (
            'CANONICAL CONCEPT IDENTITY: The following research labels are frozen task-level concepts: '
            + rendered
            + '. When referring to the same construct or concept, preserve its canonical English label; '
              'do not silently replace it with a near-synonym that changes concept identity. Ordinary grammatical '
              'articles, possessives, and surrounding syntax remain free. Distinct listed labels are not synonyms unless the evidence explicitly says so.'
        )

    @staticmethod
    def _translation_ledger_hash(agent, source: str) -> str:
        method = getattr(agent, 'translation_fact_ledger_hash', None)
        if callable(method):
            return str(method(source) or '')
        # Test doubles / legacy adapters still get deterministic invalidation.
        return hashlib.sha256(str(source or '').encode('utf-8')).hexdigest()

    def _translation_identity_bundle(self, agent, source: str) -> dict[str, str]:
        """Return one canonical identity domain for translation reuse.

        The frozen English source remains the manuscript authority, while the
        Translation Compiler may consume a canonicalized representation (for
        example qualitative quote literals become QUOTE semantic slots).  Cache
        identity must therefore persist both hashes and derive the private Fact
        Ledger only from the exact compiled source sent to the translator.
        """
        raw_source = str(source or '').strip()
        compiled_source, _pending, _reused = self._prepare_qualitative_translation_source(
            raw_source, getattr(self, '_qualitative_quote_sources', {}), {}
        )
        compiled_source = str(compiled_source or '').strip()
        return {
            'source_hash': self._text_hash(raw_source),
            'compiled_source': compiled_source,
            'compiled_source_hash': self._text_hash(compiled_source),
            'fact_ledger_hash': self._translation_ledger_hash(agent, compiled_source),
        }

    @staticmethod
    def _assemble_partial_translation(translation_sections, adaptive_structure, *, include_front=False) -> str:
        mapping = translation_sections if isinstance(translation_sections, dict) else {}
        parts = []
        if include_front:
            record = mapping.get('__front_matter__') or {}
            if record.get('qa_status') in {'passed', 'passed_with_warning'} and record.get('translated_text'):
                parts.append(str(record['translated_text']).strip())
        for key, _title in GenerationRunner._unique_top_level_sections(adaptive_structure):
            record = mapping.get(key) or {}
            if record.get('qa_status') in {'passed', 'passed_with_warning'} and record.get('translated_text'):
                parts.append(str(record['translated_text']).strip())
        return '\n\n'.join(part for part in parts if part).strip()

    def _start_translation_worker(
        self, agent, *, translation_sections, translation_glossary,
        adaptive_structure, target_lang,
    ) -> None:
        if self._translation_worker is not None:
            return
        self.translation_agent = agent
        self._translation_worker_sections = dict(translation_sections or {})
        self._translation_worker_glossary = dict(translation_glossary or {})
        self._translation_worker_context = {
            'adaptive_structure': list(adaptive_structure or []),
            'target_lang': str(target_lang or ''),
        }
        logical_keys = ['__front_matter__'] + [
            key for key, _title in self._unique_top_level_sections(adaptive_structure)
        ]
        logical_keys = list(dict.fromkeys(key for key in logical_keys if key))
        self._translation_progress_total = len(logical_keys)
        self._translation_progress_sections = dict(translation_sections or {})
        self._translation_progress_queued = {
            key for key in logical_keys if key in self._translation_progress_sections
        }
        self._translation_progress_inflight = set()
        self._translation_worker = _OrderedTranslationWorker(self._run_translation_job)
        self._write_preview_payload(force=True)

    def _run_translation_job(self, job: dict[str, Any]) -> dict[str, Any]:
        if self.translation_agent is None:
            raise RuntimeError('translation_worker_agent_missing')
        context = dict(self._translation_worker_context or {})
        sections, glossary = self._translate_frozen_unit(
            self.translation_agent,
            unit_key=str(job.get('unit_key') or ''),
            unit_name=str(job.get('unit_name') or ''),
            source_text=str(job.get('source_text') or ''),
            target_lang=str(context.get('target_lang') or ''),
            translation_sections=self._translation_worker_sections,
            translation_glossary=self._translation_worker_glossary,
            adaptive_structure=context.get('adaptive_structure') or [],
            include_front=True,
            defer_failures=True,
            english_commit_text=str(job.get('english_commit_text') or ''),
        )
        self._translation_worker_sections = dict(sections or {})
        self._translation_worker_glossary = dict(glossary or {})
        return {
            'unit_key': str(job.get('unit_key') or ''),
            'source_hash': str(job.get('source_hash') or ''),
            'translation_sections': dict(self._translation_worker_sections),
            'translation_glossary': dict(self._translation_worker_glossary),
        }

    def _enqueue_translation_unit(
        self, agent, *, unit_key: str, unit_name: str, source_text: str,
        translation_sections: dict, english_commit_text: str,
    ) -> bool:
        source = str(source_text or '').strip()
        if not source or self._translation_worker is None:
            return False
        identity = self._translation_identity_bundle(agent, source)
        source_hash = identity['source_hash']
        compiled_source_hash = identity['compiled_source_hash']
        fact_ledger_hash = identity['fact_ledger_hash']
        existing = dict((translation_sections or {}).get(unit_key) or {})
        if not isinstance(getattr(self, '_translation_progress_queued', None), set):
            self._translation_progress_queued = set()
        if not isinstance(getattr(self, '_translation_progress_inflight', None), set):
            self._translation_progress_inflight = set()
        # Do not replace the worker-owned display snapshot with a possibly stale
        # main-thread copy here; collection or the worker completion path updates
        # _translation_progress_sections monotonically.
        self._translation_progress_queued.add(str(unit_key))
        if (
            existing.get('qa_status') in {'passed', 'passed_with_warning'}
            and existing.get('source_hash') == source_hash
            and existing.get('compiled_source_hash') == compiled_source_hash
            and existing.get('target_lang') == str(self._translation_worker_context.get('target_lang') or '')
            and existing.get('translation_contract_version') == TRANSLATION_CONTRACT_VERSION
            and existing.get('fact_ledger_hash') == fact_ledger_hash
            and existing.get('translated_text')
        ):
            return False
        if existing and existing.get('translated_text'):
            reasons = []
            if existing.get('source_hash') != source_hash: reasons.append('english_source_hash')
            if existing.get('compiled_source_hash') != compiled_source_hash: reasons.append('compiled_source_hash')
            if existing.get('fact_ledger_hash') != fact_ledger_hash: reasons.append('fact_ledger_hash')
            if existing.get('translation_contract_version') != TRANSLATION_CONTRACT_VERSION: reasons.append('contract_version')
            if existing.get('target_lang') != str(self._translation_worker_context.get('target_lang') or ''): reasons.append('target_lang')
            if existing.get('qa_status') not in {'passed', 'passed_with_warning'}: reasons.append('qa_status')
            logging.info('Translation reuse miss | unit=%s phase=enqueue reasons=%s', unit_name, ','.join(reasons) or 'unknown')
        pending_key = (str(unit_key), source_hash, compiled_source_hash, fact_ledger_hash)
        if pending_key in self._translation_worker_pending:
            return False
        self._translation_progress_inflight.add(str(unit_key))
        future = self._translation_worker.submit({
            'unit_key': str(unit_key),
            'unit_name': str(unit_name),
            'source_text': source,
            'source_hash': source_hash,
            'compiled_source_hash': compiled_source_hash,
            'fact_ledger_hash': fact_ledger_hash,
            'english_commit_text': str(english_commit_text or ''),
        })
        self._translation_worker_pending.add(pending_key)
        self._translation_worker_jobs.append({
            'pending_key': pending_key,
            'future': future,
        })
        self._write_preview_payload(force=True)
        return True

    def _collect_translation_results(self, translation_sections, translation_glossary, *, block=False):
        sections = dict(translation_sections or {})
        glossary = dict(translation_glossary or {})
        while self._translation_worker_collected < len(self._translation_worker_jobs):
            item = self._translation_worker_jobs[self._translation_worker_collected]
            future = item['future']
            if not block and not future.done():
                break
            result = future.result()
            sections = dict(result.get('translation_sections') or sections)
            glossary = dict(result.get('translation_glossary') or glossary)
            self._translation_progress_sections = dict(sections)
            self._translation_worker_pending.discard(item['pending_key'])
            self._translation_worker_collected += 1
        return sections, glossary

    def _shutdown_translation_worker(self, *, wait=False) -> None:
        worker = self._translation_worker
        if worker is None:
            return
        worker.close(wait=bool(wait))
        self._translation_worker = None

    def _translate_frozen_unit(
        self, agent, *, unit_key: str, unit_name: str, source_text: str, target_lang: str,
        translation_sections: dict, translation_glossary: dict, adaptive_structure, include_front=False,
        defer_failures=True, english_commit_text: str | None = None,
    ):
        source = str(source_text or '').strip()
        if not source:
            return translation_sections, translation_glossary
        identity = self._translation_identity_bundle(agent, source)
        source_hash = identity['source_hash']
        compiled_source_hash = identity['compiled_source_hash']
        canonical_translation_source = identity['compiled_source']
        contract_version = TRANSLATION_CONTRACT_VERSION
        fact_ledger_hash = identity['fact_ledger_hash']
        existing = dict((translation_sections or {}).get(unit_key) or {})
        if not isinstance(getattr(self, '_translation_progress_queued', None), set):
            self._translation_progress_queued = set()
        if not isinstance(getattr(self, '_translation_progress_inflight', None), set):
            self._translation_progress_inflight = set()
        self._translation_progress_sections = dict(translation_sections or {})
        self._translation_progress_queued.add(str(unit_key))
        if (
            existing.get('qa_status') in {'passed', 'passed_with_warning'}
            and existing.get('source_hash') == source_hash
            and existing.get('compiled_source_hash') == compiled_source_hash
            and existing.get('target_lang') == target_lang
            and existing.get('translation_contract_version') == contract_version
            and existing.get('fact_ledger_hash') == fact_ledger_hash
            and existing.get('translated_text')
        ):
            self._translation_progress_inflight.discard(str(unit_key))
            return translation_sections, translation_glossary
        if existing and existing.get('translated_text'):
            reasons = []
            if existing.get('source_hash') != source_hash: reasons.append('english_source_hash')
            if existing.get('compiled_source_hash') != compiled_source_hash: reasons.append('compiled_source_hash')
            if existing.get('fact_ledger_hash') != fact_ledger_hash: reasons.append('fact_ledger_hash')
            if existing.get('translation_contract_version') != contract_version: reasons.append('contract_version')
            if existing.get('target_lang') != target_lang: reasons.append('target_lang')
            if existing.get('qa_status') not in {'passed', 'passed_with_warning'}: reasons.append('qa_status')
            logging.info('Translation reuse miss | unit=%s phase=final_or_direct reasons=%s', unit_name, ','.join(reasons) or 'unknown')
        self._translation_progress_inflight.add(str(unit_key))
        partial = self._assemble_partial_translation(translation_sections, adaptive_structure, include_front=include_front)
        self._publish_target_preview(
            partial, f'translating:{unit_name}', force=True, complete=False,
            english_commit_text=english_commit_text,
        )
        updated_sections = dict(translation_sections or {})
        raw_quote_ledger_record = updated_sections.get("__quote_ledger__")
        if raw_quote_ledger_record is not None and not isinstance(raw_quote_ledger_record, dict):
            logging.warning(
                "Qualitative quote translation ledger ignored malformed record type=%s",
                type(raw_quote_ledger_record).__name__,
            )
        quote_ledger_record = (
            dict(raw_quote_ledger_record) if isinstance(raw_quote_ledger_record, dict) else {}
        )
        current_quote_hashes = {
            tag: hashlib.sha256(re.sub(r"\s+", " ", quote).strip().encode("utf-8")).hexdigest()
            for tag, quote in getattr(self, "_qualitative_quote_sources", {}).items()
        }
        current_request_hash = str(getattr(self, "request_hash", "") or "")
        current_task_id = str(getattr(getattr(self, "task_dir", None), "name", "") or "")
        ledger_identity_ok = self._qualitative_quote_ledger_identity_ok(
            quote_ledger_record,
            task_id=current_task_id,
            target_lang=target_lang,
            request_hash=current_request_hash,
            translation_contract_version=TRANSLATION_CONTRACT_VERSION,
            source_quote_hashes=current_quote_hashes,
        )
        stored_translations = quote_ledger_record.get("translations")
        quote_ledger = (
            dict(stored_translations)
            if ledger_identity_ok and isinstance(stored_translations, dict)
            else {}
        )
        translation_source, pending_quote_tags, reused_quote_tags = self._prepare_qualitative_translation_source(
            source, getattr(self, "_qualitative_quote_sources", {}), quote_ledger
        )
        translation_source = str(translation_source or '').strip()
        if translation_source != canonical_translation_source:
            raise RuntimeError('Canonical translation source changed with Quote Ledger state.')

        def publish_translation_stage(stage):
            value = str(stage or '').strip().casefold()
            if value in {'qa', 'repairing'}:
                preview_stage = f'translation_qa:{unit_name}'
            else:
                preview_stage = f'translating:{unit_name}'
            self._publish_target_preview(
                partial, preview_stage, force=True, complete=False,
                english_commit_text=english_commit_text,
            )

        try:
            if pending_quote_tags:
                quote_translate_method = getattr(agent, 'translate_qualitative_quote_objects', None)
                if not callable(quote_translate_method):
                    raise RuntimeError('Typed qualitative quote translation entry point is unavailable.')
                quote_objects = {
                    tag: getattr(self, '_qualitative_quote_sources', {}).get(tag, '')
                    for tag in pending_quote_tags
                }
                quote_result = quote_translate_method(
                    quote_objects,
                    target_lang,
                    glossary=translation_glossary,
                    max_attempts=2,
                    stage_callback=publish_translation_stage,
                )
                returned_quotes = (quote_result or {}).get('translations')
                if not isinstance(returned_quotes, dict) or set(returned_quotes) != set(pending_quote_tags):
                    raise RuntimeError(
                        'Typed qualitative quote translation identity mismatch: '
                        f'expected={sorted(pending_quote_tags)} got={sorted(returned_quotes) if isinstance(returned_quotes, dict) else []}'
                    )
                for tag in pending_quote_tags:
                    rendered_quote = str(returned_quotes.get(tag) or '').strip()
                    if not rendered_quote:
                        raise RuntimeError(f'Typed qualitative quote translation is empty: {tag}')
                    quote_ledger[tag] = rendered_quote
                returned_glossary = (quote_result or {}).get('glossary')
                if isinstance(returned_glossary, dict):
                    translation_glossary = dict(returned_glossary)
                updated_sections['__quote_ledger__'] = {
                    'task_id': getattr(getattr(self, "task_dir", None), "name", ""),
                    'request_hash': current_request_hash,
                    'writer_execution_epoch': int(getattr(self, "execution_epoch", 0) or 0),
                    'target_lang': target_lang,
                    'translation_contract_version': TRANSLATION_CONTRACT_VERSION,
                    'source_quote_hashes': current_quote_hashes,
                    'translations': dict(quote_ledger),
                    'updated_at': datetime.now(timezone.utc).isoformat(),
                }

            translate_method = agent.translate_frozen_section
            call_kwargs = {
                'glossary': translation_glossary, 'section_name': unit_name, 'max_attempts': 2,
            }
            try:
                parameters = inspect.signature(translate_method).parameters.values()
                supports_stage_callback = any(
                    item.name == 'stage_callback' or item.kind == inspect.Parameter.VAR_KEYWORD
                    for item in parameters
                )
            except (TypeError, ValueError):
                supports_stage_callback = False
            if supports_stage_callback:
                call_kwargs['stage_callback'] = publish_translation_stage
            result = translate_method(translation_source, target_lang, **call_kwargs)
            translated_text = str(result.get('text') or '').strip()
            if pending_quote_tags or reused_quote_tags:
                translated_text, quote_ledger = self._finalize_qualitative_translation_quotes(
                    translated_text, pending_quote_tags, reused_quote_tags, quote_ledger
                )
                agent.validate_translation_alignment(source, translated_text)
                result = dict(result)
                result['text'] = translated_text
                updated_sections['__quote_ledger__'] = {
                    'task_id': getattr(getattr(self, "task_dir", None), "name", ""),
                    'request_hash': current_request_hash,
                    'writer_execution_epoch': int(getattr(self, "execution_epoch", 0) or 0),
                    'target_lang': target_lang,
                    'translation_contract_version': TRANSLATION_CONTRACT_VERSION,
                    'source_quote_hashes': current_quote_hashes,
                    'translations': dict(quote_ledger),
                    'updated_at': datetime.now(timezone.utc).isoformat(),
                }
        except PipelineCancelledError:
            raise
        except Exception as error:
            if not defer_failures:
                raise
            code_method = getattr(agent, 'translation_error_code', None)
            error_code = str(code_method(error) if callable(code_method) else 'TRANSLATION_PENDING')
            updated_sections[unit_key] = {
                'source_hash': source_hash,
                'compiled_source_hash': compiled_source_hash,
                'target_lang': target_lang,
                'glossary_version': self._glossary_version(translation_glossary),
                'translation_contract_version': contract_version,
                'fact_ledger_hash': fact_ledger_hash,
                'translated_text': '',
                'qa_status': 'repair_pending',
                'error_code': error_code,
                'attempts': int(existing.get('attempts') or 0) + 1,
                'deferred_at': datetime.now(timezone.utc).isoformat(),
            }
            logging.warning(
                'Translation deferred until final gate | unit=%s code=%s error=%s',
                unit_name, error_code, type(error).__name__,
            )
            self._translation_progress_sections = dict(updated_sections)
            self._translation_progress_inflight.discard(str(unit_key))
            self._publish_target_preview(
                partial, f'translation_repair_pending:{unit_name}', force=True, complete=False,
                english_commit_text=english_commit_text,
            )
            return updated_sections, translation_glossary
        updated_glossary = dict(result.get('glossary') or translation_glossary or {})
        result_fact_ledger_hash = str(result.get('fact_ledger_hash') or fact_ledger_hash)
        if result_fact_ledger_hash != fact_ledger_hash:
            raise RuntimeError('Translation result Fact Ledger identity differs from canonical compiled source.')
        updated_sections[unit_key] = {
            'source_hash': source_hash,
            'compiled_source_hash': compiled_source_hash,
            'target_lang': target_lang,
            'glossary_version': self._glossary_version(updated_glossary),
            'translation_contract_version': str(result.get('translation_contract_version') or contract_version),
            'fact_ledger_hash': result_fact_ledger_hash,
            'translated_text': str(result.get('text') or '').strip(),
            'qa_status': str(result.get('qa_status') or 'passed'),
            'language_adapter': str(result.get('language_adapter') or ''),
            'diagnostics': list(result.get('diagnostics') or [])[-20:],
            'attempts': int(result.get('attempts') or 1),
            'completed_at': datetime.now(timezone.utc).isoformat(),
        }
        partial = self._assemble_partial_translation(updated_sections, adaptive_structure, include_front=include_front)
        self._translation_progress_sections = dict(updated_sections)
        self._translation_progress_inflight.discard(str(unit_key))
        self._publish_target_preview(
            partial, f'translation_ready:{unit_name}', force=True, complete=False,
            english_commit_text=english_commit_text,
        )
        return updated_sections, updated_glossary

    @staticmethod
    def _bounded_target(base_target: int, accumulated_deviation: float, remaining_including_current: int, safe_chunk: int) -> int:
        base = max(80, int(base_target))
        correction = -float(accumulated_deviation) / max(1, int(remaining_including_current))
        lower = max(80, int(round(base * 0.75)))
        upper = min(int(safe_chunk * 1.5), max(lower, int(round(base * 1.25))))
        return max(lower, min(upper, int(round(base + correction))))

    @staticmethod
    def _english_count_for_target(manuscript: str) -> int:
        body = re.split(r"(?m)^#\s+References\s*$", str(manuscript or ""), maxsplit=1)[0]
        body = re.split(r"(?m)^#\s+Appendix: Data Structure and Evidence\s*$", body, maxsplit=1)[0]
        # Markdown punctuation is ignored; contractions/hyphenated terms stay one token.
        return len(re.findall(r"\b[A-Za-z0-9]+(?:[-'][A-Za-z0-9]+)*\b", body))

    def _validate_target_length(self, manuscript: str, target_words: int) -> dict[str, Any]:
        """Return a nonfatal total-length quality diagnostic.

        The user-selected target remains a planning input, not a completion gate.
        Completeness is validated separately by structure, evidence, citation,
        translation, DOCX, and ZIP checks.
        """
        quality = APP_CONFIG.get("manuscript_validation", {})
        minimum = int(target_words * float(quality.get("minimum_total_ratio", 0.97)))
        maximum = int(target_words * float(quality.get("maximum_total_ratio", 1.03)))
        short_notice = float(quality.get("total_short_notice_ratio", 0.70))
        topic_review = float(quality.get("total_topic_review_ratio", 0.50))
        actual = self._english_count_for_target(manuscript)
        ratio = (actual / max(1, int(target_words)))
        status = "normal"
        recommendation = ""
        if ratio < topic_review:
            status = "very_short"
            recommendation = (
                "First verify that generation completed normally and no section/evidence is missing. "
                "If the manuscript is complete, consider broadening the research scope or selecting a new research topic."
            )
        elif ratio < short_notice:
            status = "short"
            recommendation = (
                "If the manuscript is structurally complete, consider broadening the theoretical scope, context, "
                "research question, or research topic."
            )
        elif actual < minimum:
            status = "slightly_short"
        elif actual > maximum:
            status = "long"
        if status != "normal":
            logging.warning(
                "Manuscript length diagnostic | actual=%s | target=%s | ratio=%.3f | status=%s | nonfatal=true",
                actual, target_words, ratio, status,
            )
        return {
            "scope": "manuscript", "name": "English manuscript", "actual_words": actual,
            "target_words": int(target_words), "ratio": round(ratio, 4), "status": status,
            "recommendation": recommendation, "nonfatal": True,
        }

    @staticmethod
    def _upsert_quality_diagnostic(items: list[dict[str, Any]], diagnostic: dict[str, Any]) -> None:
        key = (str(diagnostic.get("scope") or ""), str(diagnostic.get("name") or ""))
        items[:] = [
            item for item in items
            if (str(item.get("scope") or ""), str(item.get("name") or "")) != key
        ]
        items.append(dict(diagnostic))

    def _section_word_diagnostic(self, name: str, actual_words: int, target_words: int) -> dict[str, Any]:
        validation = APP_CONFIG.get("manuscript_validation", {})
        budget = APP_CONFIG.get("word_budget", {})
        target = max(1, int(target_words))
        actual = max(0, int(actual_words))
        ratio = actual / target
        short_notice = float(validation.get("section_short_notice_ratio", 0.60))
        topic_review = float(validation.get("section_topic_review_ratio", 0.40))
        preferred_min = float(budget.get("section_min_ratio", 0.92))
        preferred_max = float(budget.get("section_max_ratio", 1.08))
        status = "normal"
        recommendation = ""
        if ratio < topic_review:
            status = "very_short"
            recommendation = (
                "First verify that this section completed normally and that evidence was available. "
                "If it is complete, consider broadening the research scope or selecting a new research topic."
            )
        elif ratio < short_notice:
            status = "short"
            recommendation = (
                "If the section is complete, consider broadening the theoretical scope, context, research question, "
                "or research topic."
            )
        elif ratio < preferred_min:
            status = "slightly_short"
        elif ratio > preferred_max:
            status = "long"
        if status != "normal":
            logging.warning(
                "Section length diagnostic | section=%s | actual=%s | target=%s | ratio=%.3f | status=%s | nonfatal=true",
                name, actual, target, ratio, status,
            )
        return {
            "scope": "section", "name": str(name), "actual_words": actual, "target_words": target,
            "ratio": round(ratio, 4), "status": status, "recommendation": recommendation, "nonfatal": True,
        }

    def _resolve_paths(self, items) -> list[str]:
        output = []
        for item in items or []:
            path = (self.task_dir / str(item)).resolve()
            if self.task_dir.resolve() not in path.parents:
                raise RuntimeError("input_path_escape")
            if path.is_file():
                output.append(str(path))
        return output

    def _run_impl(self) -> dict[str, Any]:
        req = self.request
        mode = str(req.get("mode") or "").lower()
        if mode not in {"amj", "asq"}:
            raise RuntimeError("invalid_generation_mode")
        target_words = int(req.get("target_words") or 0)
        if not APP_CONFIG["target_words_min"] <= target_words <= APP_CONFIG["target_words_max"]:
            raise RuntimeError("target_words_out_of_range")

        ds_key = str(os.environ.get("DEEPSEEK_API_KEY") or "").strip()
        s2_key = str(os.environ.get("S2_API_KEY") or "").strip()
        if not ds_key:
            raise RuntimeError("DEEPSEEK_API_KEY_missing")

        if mode == "amj":
            APP_CONFIG.update(AMJ_PROMPTS)
        else:
            APP_CONFIG.update(ASQ_PROMPTS)

        agent = SSciPipelineAgent(ds_key, s2_key)
        self.agent = agent
        agent.set_cancel_checker(self.cancel_checker)
        # DeepSeek/search activity marks the executor as genuinely alive; the
        # heartbeat thread decides separately when to renew D1's lease.
        agent.set_execution_progress_hook(lambda: self._progress_activity())
        safe_chunk_words = int(agent.chunk_words_default)
        quant_engine = get_quantitative_engine()
        qual_engine = get_qualitative_engine(agent)

        state = self.checkpoints.load_latest() or {}
        quality_diagnostics = list(state.get("quality_diagnostics") or [])
        if state:
            self._preview_english_text = str(state.get('full_draft_en') or '')
            self._preview_target_text = str(state.get('full_draft_translated') or '')
            logging.info(
                "[SP_EXECUTOR] resume checkpoint seq=%s stage=%s",
                state.get("checkpoint_seq"), state.get("checkpoint_stage"),
            )

        topic = str(req.get("topic") or "").strip()
        keywords = str(req.get("keywords") or "").strip()
        target_lang = str(req.get("target_lang") or "").strip()
        output_suffix = str(req.get("output_suffix") or "Target").strip()
        interface_language = str(req.get("interface_language") or "zh").strip()
        reference_paths = self._resolve_paths(req.get("reference_files") or [])
        data_paths = self._resolve_paths(req.get("data_files") or [])
        interview_paths = self._resolve_paths(req.get("interview_files") or [])

        base_evidence_contract = str(APP_CONFIG.get("evidence_integrity_contract") or "")

        # Restore agent state before continuing any dependent step.
        if isinstance(state.get("agent_state"), dict):
            agent.load_state(state["agent_state"])

        user_lit_context = str(state.get("user_lit_context") or "")
        user_data_md = str(state.get("user_data_md") or "")
        data_metadata = dict(state.get("data_metadata") or {})

        if not state.get("evidence_ready"):
            self._progress(3, "inputs_and_evidence")
            if reference_paths:
                user_lit_context = extract_text_from_files(reference_paths[:20])
                all_paper_texts = [extract_text_from_files([path]) for path in reference_paths[:20]]
                agent.global_memory["core_golden_metadata"] = extract_golden_metadata(agent, all_paper_texts)
            if mode == "amj":
                data_file = data_paths[0] if data_paths else None
                _, user_data_md, _, data_metadata = quant_engine.resolve_empirical_data(
                    data_file, interface_language, topic=topic, keywords=keywords, agent=agent
                )
            else:
                raw_interviews = extract_text_from_files(interview_paths[:1]) if interview_paths else ""
                user_data_md, _, data_metadata = qual_engine.resolve_qualitative_data(topic, keywords, raw_interviews)
            state.update(
                {
                    "topic": topic,
                    "keywords": keywords,
                    "target_lang": target_lang,
                    "target_words": target_words,
                    "mode": mode,
                    "interface_language": interface_language,
                    "user_lit_context": user_lit_context,
                    "user_data_md": user_data_md,
                    "data_metadata": data_metadata,
                    "agent_state": agent.export_state(),
                    "evidence_ready": True,
                }
            )
            self._checkpoint("evidence_ready", state, 6)

        self._qualitative_quote_sources = (
            self._qualitative_quote_source_map(data_metadata) if mode == "asq" else {}
        )

        # ASQ planning never needs access to verbatim quotations.  Keep the
        # research-logic/front-matter stage on a quote-free internal-evidence
        # summary; verbatim text is disclosed later only to its owning Findings
        # unit (or the appendix).
        planning_data_md = (
            qual_engine.build_scoped_evidence_md(data_metadata, include_themes=True)
            if mode == "asq"
            else user_data_md
        )

        refs_text = str(state.get("refs_text") or "")
        if not refs_text:
            self._progress(8, "S2 + OpenAlex")
            refs_text = agent.fetch_references_parallel(
                topic,
                keywords,
                min_required=LITERATURE_EVIDENCE_TARGET,
                progress_callback=lambda: self._progress(9, "S2 + OpenAlex"),
            )
            if len(agent.citation_map) < MIN_FINAL_REFERENCES:
                raise RuntimeError(
                    f"Insufficient eligible references: {len(agent.citation_map)}/{MIN_FINAL_REFERENCES}"
                )
            if user_lit_context:
                core_refs = agent.parse_user_core_literature(user_lit_context)
                if core_refs:
                    refs_text = (
                        "【USER PROVIDED CORE LITERATURE】\n" + core_refs
                        + "\n\n【RETRIEVED LITERATURE】\n" + refs_text
                    )
            state.update({"refs_text": refs_text, "agent_state": agent.export_state()})
            self._checkpoint("literature_ready", state, 11)

        rag_engine = initialize_rag([value for value in (user_lit_context, refs_text) if value])

        synthesis_result = str(state.get("synthesis_result") or "")
        if not synthesis_result:
            self._progress(12, "literature_synthesis")
            synthesis_result = self._stream_text(
                agent.stream_synthesize_literature(topic, refs_text, user_core_literature=user_lit_context)
            )
            synthesis_result = re.sub(r"<think>.*?</think>", "", synthesis_result, flags=re.DOTALL).strip()
            state.update({"synthesis_result": synthesis_result, "agent_state": agent.export_state()})
            self._checkpoint("synthesis_ready", state, 13)

        adaptive_structure = state.get("adaptive_structure")
        if not isinstance(adaptive_structure, list) or not adaptive_structure:
            self._progress(14, "research_logic")
            research_logic_response = self._stream_text(
                agent.stream_generate_research_logic(
                    topic, keywords, synthesis_result, refs_text,
                    data_evidence=planning_data_md, mode=mode,
                )
            )
            research_logic_contract = re.sub(
                r"<think>.*?</think>", "", research_logic_response, flags=re.DOTALL
            ).strip()
            if not research_logic_contract:
                raise RuntimeError("research_logic_contract_missing")
            agent.global_memory["research_logic_contract"] = research_logic_contract

            self._progress(14, "working_front_matter")
            working_front = agent.generate_working_front_matter(
                topic, keywords, research_logic_contract, synthesis_result,
                data_evidence=planning_data_md, mode=mode,
            )
            working_abstract = str(working_front.get("abstract") or "").strip()
            working_keywords = list(working_front.get("keywords") or [])
            if not working_abstract or not working_keywords:
                raise RuntimeError("working_front_matter_missing")
            agent.global_memory["working_abstract"] = working_abstract

            self._progress(14, "outline_planning")
            plan = agent.generate_outline(topic, keywords, mode=mode, data_metadata=data_metadata)
            en_topic = str(plan.get("en_topic", topic))
            structure = plan.get("structure", [])
            full_draft_en = (
                f"# {en_topic}\n\n## Abstract\n{working_abstract}\n\n"
                f"## Keywords\n{', '.join(working_keywords)}\n\n"
            )
            # Publish the Working Abstract/Keywords immediately, before JIT and
            # citation planning, so the user sees the manuscript anchor before
            # Introduction while those longer planning stages continue.
            self._publish_preview(full_draft_en, "working_front_matter", force=True)
            front_budget = max(180, int(APP_CONFIG.get("word_budget", {}).get("front_matter_words", 220)))
            body_target_words = max(1000, target_words - front_budget)
            adaptive_structure = agent.build_generation_tasks(structure, body_target_words, safe_chunk_words)
            for task in adaptive_structure:
                task["is_first"] = bool(task.get("section_first"))
            quote_invocation_plan = (
                self._build_qualitative_quote_invocation_plan(adaptive_structure, data_metadata)
                if mode == "asq" else {}
            )
            jit_texts = agent.prefetch_jit_literature(topic, keywords, adaptive_structure)
            if jit_texts:
                rag_engine._initialize_chunks([jit_texts])
            self._progress(15, "citation_planning")
            citation_plan = agent.build_claim_evidence_plan(
                adaptive_structure, topic, synthesis_result, mode=mode, target_unique=CITATION_PLAN_TARGET
            )
            canonical_concept_ledger = self._build_canonical_concept_ledger(
                mode, data_metadata, keywords=keywords, adaptive_structure=adaptive_structure,
                research_logic_contract=research_logic_contract,
            )
            planned_unique = {
                item.get("tag")
                for items in citation_plan.values()
                for item in items
                if item.get("tag")
            }
            if len(planned_unique) < MIN_FINAL_REFERENCES:
                raise RuntimeError(
                    "Insufficient claim-evidence plan before drafting: "
                    f"{len(planned_unique)}/{MIN_FINAL_REFERENCES}."
                )
            state.update(
                {
                    "adaptive_structure": adaptive_structure,
                    "quote_invocation_plan": quote_invocation_plan,
                    "citation_plan": citation_plan,
                    "research_logic_contract": research_logic_contract,
                    "working_abstract": working_abstract,
                    "working_keywords": working_keywords,
                    "front_matter_phase": "working",
                    "body_target_words": body_target_words,
                    "full_draft_en": full_draft_en,
                    "full_draft_translated": "",
                    "translation_sections": {},
                    "translation_glossary": {},
                    "canonical_concept_ledger": canonical_concept_ledger,
                    "completed_sections": [],
                    "context_tail": "",
                    "accumulated_deviation": 0.0,
                    "agent_state": agent.export_state(),
                    "outline_ready": True,
                }
            )
            self._publish_preview(full_draft_en, "outline_ready", force=True)
            self._checkpoint("outline_ready", state, 15)
        else:
            full_draft_en = str(state.get("full_draft_en") or "")
            # JIT text itself is already represented in agent citation state;
            # refs + user literature rebuild a safe RAG baseline on resume.

        completed_sections = list(state.get("completed_sections") or [])
        context_tail = str(state.get("context_tail") or "")
        accumulated_deviation = float(state.get("accumulated_deviation") or 0.0)
        research_logic_contract = str(
            state.get("research_logic_contract") or agent.global_memory.get("research_logic_contract") or ""
        )
        citation_plan = state.get("citation_plan") if isinstance(state.get("citation_plan"), dict) else {}
        translation_sections = dict(state.get('translation_sections') or {})
        translation_glossary = dict(state.get('translation_glossary') or {})
        canonical_concept_ledger = state.get('canonical_concept_ledger')
        if not isinstance(canonical_concept_ledger, dict) or not canonical_concept_ledger.get('items'):
            canonical_concept_ledger = self._build_canonical_concept_ledger(
                mode, data_metadata, keywords=keywords, adaptive_structure=adaptive_structure,
                research_logic_contract=research_logic_contract,
            )
            state['canonical_concept_ledger'] = canonical_concept_ledger
        working_abstract = str(state.get("working_abstract") or "").strip()
        working_keywords = list(state.get("working_keywords") or [])
        if working_abstract:
            agent.global_memory["working_abstract"] = working_abstract
        if self._preview_target_text == '':
            self._preview_target_text = str(state.get('full_draft_translated') or '')

        # Target-language work remains Executor-owned but no longer blocks the
        # English writer.  A dedicated single translation agent/worker keeps
        # glossary order deterministic and avoids sharing mutable writer state.
        translation_agent = SSciPipelineAgent(ds_key, s2_key)
        translation_agent.set_execution_progress_hook(lambda: self._progress_activity())
        translation_agent.set_cancel_checker(lambda: self.cancel_checker())
        seeded_glossary = translation_agent.seed_canonical_concept_glossary(
            canonical_concept_ledger, target_lang, translation_glossary
        )
        if seeded_glossary != translation_glossary:
            translation_glossary = dict(seeded_glossary)
            state['translation_glossary'] = translation_glossary
            state['canonical_concept_ledger'] = canonical_concept_ledger
            self._checkpoint('canonical_concept_ledger_ready', state, 15)
        self._start_translation_worker(
            translation_agent,
            translation_sections=translation_sections,
            translation_glossary=translation_glossary,
            adaptive_structure=adaptive_structure,
            target_lang=target_lang,
        )
        # Capture a dedicated abort Event, not the worker lifecycle stop Event.
        # The worker is gracefully closed before the strict final translation
        # gate, but the same translation agent remains valid for deferred/final
        # units.  Abortive shutdown still cancels any in-flight target call.
        translation_abort_event = self._translation_worker.cancel_event
        translation_agent.set_cancel_checker(
            lambda: self.cancel_checker() or translation_abort_event.is_set()
        )

        # Backward-compatible resume path for a pre-R3.2.8.3 checkpoint that has
        # an outline but no Working Abstract yet. Generate it once, checkpoint it,
        # and reuse it thereafter; never regenerate an unchanged anchor on resume.
        if not working_abstract and not state.get("english_finalized"):
            working_front = agent.generate_working_front_matter(
                topic, keywords, research_logic_contract, synthesis_result,
                data_evidence=planning_data_md, mode=mode,
            )
            working_abstract = str(working_front.get("abstract") or "").strip()
            working_keywords = list(working_front.get("keywords") or [])
            if not working_abstract or not working_keywords:
                raise RuntimeError("working_front_matter_missing")
            agent.global_memory["working_abstract"] = working_abstract
            full_draft_en = replace_abstract(full_draft_en, working_abstract)
            full_draft_en = self._replace_keywords(full_draft_en, working_keywords)
            state.update({
                "working_abstract": working_abstract,
                "working_keywords": working_keywords,
                "front_matter_phase": "working",
                "full_draft_en": full_draft_en,
                "agent_state": agent.export_state(),
            })
            self._publish_preview(full_draft_en, "working_front_matter", force=True)
            self._checkpoint("working_front_matter_ready", state, 15)

        # Queue the QA-frozen Working front matter.  Translation is allowed to
        # overlap later English work, but only frozen English units may enter
        # this Executor-owned ordered worker.
        if working_abstract and not state.get("english_finalized"):
            section_order_for_front = self._unique_top_level_sections(adaptive_structure)
            if section_order_for_front:
                first_section_title = section_order_for_front[0][1]
                working_front_source = self._extract_front_matter(full_draft_en, first_section_title)
                if not working_front_source and not completed_sections:
                    # Fresh tasks contain only Title/Abstract/Keywords here; the
                    # first body heading does not exist yet. Preserve legacy
                    # resume behavior when completed body sections are present.
                    working_front_source = str(full_draft_en or '').strip()
                self._enqueue_translation_unit(
                    translation_agent,
                    unit_key='__front_matter__', unit_name='Working Front Matter',
                    source_text=working_front_source,
                    translation_sections=translation_sections,
                    english_commit_text=working_front_source,
                )

        # Resume/idempotency: completed English top-level sections whose current
        # source-hash translation is absent/stale are queued exactly once.
        for section_key, section_title in self._unique_top_level_sections(adaptive_structure):
            if not self._section_is_complete(adaptive_structure, completed_sections, section_key):
                continue
            frozen_section = self._extract_top_level_section(full_draft_en, section_title)
            self._enqueue_translation_unit(
                translation_agent,
                unit_key=section_key,
                unit_name=section_title,
                source_text=frozen_section,
                translation_sections=translation_sections,
                english_commit_text=self._english_prefix_through_top_level(full_draft_en, section_title),
            )

        translation_sections, translation_glossary = self._collect_translation_results(
            translation_sections, translation_glossary, block=False
        )
        if not citation_plan:
            self._progress(15, "citation_planning")
            citation_plan = agent.build_claim_evidence_plan(
                adaptive_structure, topic, synthesis_result, mode=mode, target_unique=CITATION_PLAN_TARGET
            )
            state["citation_plan"] = citation_plan

        quote_invocation_plan = (
            dict(state.get("quote_invocation_plan") or {}) if mode == "asq" else {}
        )
        if mode == "asq":
            if not quote_invocation_plan:
                quote_invocation_plan = self._build_qualitative_quote_invocation_plan(
                    adaptive_structure, data_metadata
                )
                state["quote_invocation_plan"] = quote_invocation_plan
                self._checkpoint("quote_plan_ready", state, 15)
            self._validate_qualitative_quote_invocation_plan(
                quote_invocation_plan, adaptive_structure, data_metadata
            )

        total_steps = len(adaptive_structure)
        for sec in adaptive_structure:
            display_name = sec["display_name"]
            if display_name in completed_sections:
                continue
            if self.cancel_checker():
                raise RuntimeError("task_cancel_requested")
            remaining = sum(1 for item in adaptive_structure if item["display_name"] not in completed_sections)
            adjusted_target = self._bounded_target(
                int(sec["target_words"]), 0.0, remaining, safe_chunk_words
            )
            sec_title = sec["section"]
            subsection = sec.get("subsection")
            is_quarantined = any(
                word in sec_title.lower()
                for word in ["abstract", "keyword", "conclusion", "references", "appendix"]
            )
            planned_items = list(citation_plan.get(display_name, []))
            assigned_tag_list = [str(item.get("tag") or "") for item in planned_items if item.get("tag")]
            claim_plan_text = agent.format_claim_evidence_plan(planned_items)
            section_id = str(sec.get("section_id") or "")
            section_role = str(
                APP_CONFIG.get("section_reasoning_profiles", {}).get(mode, {}).get(section_id) or ""
            )
            mode_directive = ""
            if section_id in {"theory", "theoretical_framing"}:
                mode_directive = str(APP_CONFIG.get("hypothesis_development_directive") or "")
            elif section_id in {"methodology", "methods"}:
                mode_directive = str(APP_CONFIG.get("methodological_defense_directive") or "")
            elif section_id == "discussion":
                mode_directive = str(APP_CONFIG.get("discussion_depth_directive") or "")
            elif section_id == "conclusion":
                mode_directive = str(APP_CONFIG.get("conclusion_anchor_directive") or "")

            if is_quarantined or section_id in {"results", "findings"}:
                citation_rule = (
                    "NO EXTERNAL LITERATURE CITATIONS ALLOWED IN THIS UNIT. "
                    "Report only frozen empirical/qualitative evidence and the interpretation permitted by the section role."
                )
            else:
                citation_rule = (
                    "Use the citation-first claim-evidence plan below. Every listed item has already been assigned to this unit on evidence relevance: use each assigned item exactly once unless it would contradict the frozen research evidence. "
                    "For each item, write one distinct claim no broader than the supplied evidence and attach exactly its assigned [CITE-XX] tag. "
                    "Never stack sources, substitute tags, or use literature as evidence for this study's own facts.\n"
                    f"<CLAIM_EVIDENCE_PLAN>\n{claim_plan_text}\n</CLAIM_EVIDENCE_PLAN>"
                )
            if mode == "asq" and section_id not in {"findings", "appendix"}:
                citation_rule += (
                    "\nASQ EVIDENCE-CHANNEL RULE: A sentence whose factual subject is this study's setting, participants, interviews, data sources, cases, actors, observations, events, quotations, analytic steps, or empirical findings must not carry an external [CITE-XX] tag. "
                    "Place any prior-literature or methodological-rationale claim in a separate sentence and cite only that external claim."
                )
            memory_context = agent._build_memory_context(sec_title, 2500)
            memory_block = f"\n<GLOBAL_MEMORY>\n{memory_context}\n</GLOBAL_MEMORY>\n" if memory_context else ""
            snippets = rag_engine.get_relevant_context(display_name) if rag_engine is not None else ""
            if snippets:
                memory_block += f"\n<TARGETED_RAG>\n{snippets}\n</TARGETED_RAG>\n"
            heading_directive = "Do not output the section heading; it will be inserted by the system."
            if subsection:
                heading_directive += (
                    f" Do not output the subsection heading '{subsection}'; it will also be inserted by the system. "
                    "Use level-3 headings only when analytically necessary."
                )
            elif sec_title.lower() in {"introduction", "conclusion"}:
                heading_directive += " Do not use additional headings."
            else:
                heading_directive += (
                    " Use level-2 or level-3 headings only when they clarify the argument; "
                    "never use level-4 headings or numbered headings."
                )
            table_rule = (
                "Present only frozen statistical evidence in standard Markdown tables with plain-text captions."
                if sec["is_method_or_result"] and mode == "amj" else ""
            )
            concepts = ", ".join(sec.get("concepts") or [])
            theme_evidence = [item for item in (sec.get("theme_evidence") or []) if isinstance(item, dict)]
            evidence_profiles = []
            for item in theme_evidence:
                stage = str(item.get("stage") or "Not specified").strip()
                role = str(item.get("evidence_role") or "typical").strip()
                case_label = str(item.get("case_label") or "Not specified").strip()
                evidence_profiles.append(f"stage={stage}; case={case_label}; role={role}")
            planned_quote_count = sum(
                1 for record in quote_invocation_plan.values()
                if str(record.get("render_task_key") or "") == self._qualitative_task_key(sec)
            )
            qualitative_rule = (
                f"Assigned qualitative concepts: {concepts}. Assigned evidence profiles: {' | '.join(evidence_profiles)}. "
                f"The Executor has frozen {planned_quote_count} quotation invocation(s) for this task. "
                "Use the assigned evidence analytically, but output narrative prose only: do not reproduce any verbatim quotation and do not output any QUOTE-XX identifier. "
                "The Executor inserts the frozen quotations deterministically after narrative/citation QA. Preserve stage/case/deviant meaning and use deviant evidence to qualify rather than erase the pattern."
                if concepts or evidence_profiles else ""
            )

            scoped_data_md = user_data_md
            if mode == "asq":
                if section_id == "appendix":
                    # The appendix is the only non-owning unit allowed to see
                    # the full frozen evidence table. It is excluded from the
                    # body duplicate-quote gate by design.
                    scoped_data_md = user_data_md
                elif section_id == "findings":
                    current_theme = str(sec.get("theme_title") or "").strip()
                    scoped_data_md = qual_engine.build_scoped_evidence_md(
                        data_metadata,
                        include_themes=True,
                        theme_titles=[current_theme] if current_theme else None,
                        assigned_evidence=theme_evidence,
                        include_quotes=bool(theme_evidence),
                        include_quote_ids=False,
                    )
                elif section_id == "discussion":
                    scoped_data_md = qual_engine.build_scoped_evidence_md(
                        data_metadata,
                        include_themes=True,
                    )
                else:
                    scoped_data_md = qual_engine.build_scoped_evidence_md(
                        data_metadata,
                        include_themes=False,
                    )
            evidence_packet = (
                f"{base_evidence_contract}\n<FROZEN_RESEARCH_EVIDENCE>\n{scoped_data_md}\n</FROZEN_RESEARCH_EVIDENCE>"
                if scoped_data_md else base_evidence_contract
            )
            completed_label = ", ".join(completed_sections)
            anti_repeat = (
                f"Already completed: {completed_label}. Do not restate their background, gap, or contribution."
                if completed_label else ""
            )
            voice = str(APP_CONFIG.get("journal_voice_profiles", {}).get(mode) or "")
            sys_prompt = (
                APP_CONFIG.get("sys_academic_writer", "") + "\n"
                + evidence_packet + "\n" + voice + "\n"
                + APP_CONFIG.get("deep_refinement_directive", "")
            )
            word_budget = APP_CONFIG.get("word_budget", {})
            min_ratio = float(word_budget.get("section_min_ratio", 0.92))
            max_ratio = float(word_budget.get("section_max_ratio", 1.08))
            prompt_context_tail = context_tail[-2500:]
            if mode == "asq":
                prompt_context_tail = self._redact_qualitative_quotes(prompt_context_tail, data_metadata)
            user_prompt = f"""Write the assigned English manuscript unit.
Section: {sec_title}
Subsection: {subsection or 'None'}
Section reasoning role: {section_role}
Additional mode directive: {mode_directive}
Planning length: approximately {int(adjusted_target * min_ratio)}-{int(adjusted_target * max_ratio)} words. This is guidance, not a content quota. Prioritize Conciseness, Accuracy, and Rigor; never add filler or remove useful reasoning solely to hit the range.
{heading_directive}
{citation_rule}
{table_rule}
{str(sec.get('required_content') or '')}
{qualitative_rule}
{anti_repeat}
{self._canonical_concept_writer_directive(canonical_concept_ledger)}
<PROVISIONAL_RESEARCH_LOGIC>
{research_logic_contract}
</PROVISIONAL_RESEARCH_LOGIC>
<WORKING_ABSTRACT_ANCHOR>
{working_abstract}
</WORKING_ABSTRACT_ANCHOR>
ANCHOR PRECEDENCE: Frozen research evidence and the PROVISIONAL_RESEARCH_LOGIC override the Working Abstract if any tension appears. The Working Abstract is continuity guidance only and must never force a result.
{memory_block}
Literature synthesis: {synthesis_result}
<FROZEN_RESEARCH_EVIDENCE>
{scoped_data_md}
</FROZEN_RESEARCH_EVIDENCE>
Previous tail: {prompt_context_tail}
Output only final academic prose. Do not report the word count."""
            if agent._count_tokens(sys_prompt) + agent._count_tokens(user_prompt) > 60000:
                user_prompt = agent._truncate_context(user_prompt, 60000 - agent._count_tokens(sys_prompt))

            current_index = len(completed_sections) + 1
            pct = min(88, 15 + int((current_index / max(1, total_steps)) * 72))
            self._progress(pct, display_name)
            prefix = ""
            if sec.get("section_first"):
                prefix += f"# {sec_title}\n\n"
            if sec.get("subsection_first") and subsection:
                prefix += f"## {subsection}\n\n"
            if mode == "asq" and section_id == "appendix":
                # R6A-R4: Appendix is a deterministic reader-facing projection
                # of the Evidence Ledger. It is never regenerated by the LLM.
                en_raw = qual_engine.build_appendix_evidence_md(data_metadata)
            else:
                en_raw = self._stream_text(
                    agent.stream_deepseek(
                        sys_prompt, user_prompt, max_tokens=7000,
                        model_role="writing", thinking=False, allow_fallback=False,
                    ),
                    preview_prefix=full_draft_en + prefix,
                    preview_stage=display_name,
                )
                en_raw = re.sub(r"<think>.*?</think>", "", en_raw, flags=re.S).strip()
                en_raw = re.sub(rf"(?im)^#\s*{re.escape(sec_title)}\s*\n+", "", en_raw).strip()
                if subsection:
                    en_raw = re.sub(rf"(?im)^##\s*{re.escape(subsection)}\s*\n+", "", en_raw).strip()
                en_raw = re.sub(r"(?m)^(#{2,3})\s+[\d\.\s一二三四五六七八九十、]+\s*(.*)", r"\1 \2", en_raw)
                en_raw = re.sub(r"(?m)^#{4,}\s+(.*)", r"**\1**", en_raw)
                en_raw = re.sub(r"(?m)^#\s+(.+)$", r"## \1", en_raw)
                en_raw = agent.critique_and_repair_section(
                    en_raw, section_id=section_id, section_name=display_name, mode=mode,
                    evidence_contract=evidence_packet,
                )
                en_raw = agent.polish_academic_section(en_raw, display_name, abstract=False, mode=mode)
                if mode == "asq":
                    # Model prose has no authority over quote presence/count.
                    en_raw = self._strip_model_controlled_qualitative_quotes(en_raw, data_metadata)
            allow_deterministic_citation_reinforcement = not (
                mode == "asq" and section_id in {"methods", "discussion"}
            )
            if not is_quarantined and assigned_tag_list and allow_deterministic_citation_reinforcement:
                en_raw = agent.reinforce_claim_specific_citation_coverage(
                    en_raw, assigned_tag_list, section_name=display_name
                )
            used_before = set(agent.used_citations)
            en_resolved = agent.audit_and_resolve_citations(
                prefix + en_raw, section_name=display_name, quarantine=is_quarantined
            )
            used_after = set(agent.used_citations)
            survived = used_after - used_before
            if assigned_tag_list and not is_quarantined:
                required_survival = min(
                    len(assigned_tag_list),
                    max(1, int(math.ceil(len(assigned_tag_list) * 0.78))),
                )
                if len(survived) < required_survival:
                    missing_items = [
                        item for item in planned_items
                        if str(item.get("tag") or "") not in used_after
                    ]
                    needed = max(0, required_survival - len(survived))
                    recovery_items = agent.select_contextual_citation_recovery_items(
                        missing_items, section_name=display_name, section_context=en_resolved,
                        needed=needed, excluded_tags=used_after,
                    )
                    if len(recovery_items) < needed:
                        recovery_items.extend(
                            agent.fetch_contextual_jit_recovery_items(
                                topic=topic, section_name=display_name, section_context=en_resolved,
                                needed=needed - len(recovery_items),
                                excluded_tags=used_after | {str(item.get("tag") or "") for item in recovery_items},
                            )
                        )
                    supplemental_raw = agent.build_citation_recovery_paragraph(
                        recovery_items[:needed], section_name=display_name, section_context=en_resolved
                    )
                    if supplemental_raw:
                        supplemental_resolved = agent.audit_and_resolve_citations(
                            supplemental_raw,
                            section_name=f"{display_name} — citation recovery",
                            quarantine=False,
                        )
                        if supplemental_resolved.strip():
                            en_resolved = en_resolved.rstrip() + "\n\n" + supplemental_resolved.strip()
                        used_after = set(agent.used_citations)
                        survived = used_after - used_before
            if mode == "asq" and section_id == "findings":
                en_resolved = self._render_planned_qualitative_quotes(
                    en_resolved,
                    task=sec,
                    data_metadata=data_metadata,
                    quote_invocation_plan=quote_invocation_plan,
                )
            full_draft_en += en_resolved + "\n\n"
            if assigned_tag_list:
                logging.info(
                    "Citation-first survival: section=%s planned=%s validated_new=%s",
                    display_name, len(assigned_tag_list), len(survived),
                )
            self._publish_preview(full_draft_en, display_name, force=True)
            agent._extract_memory_from_text(en_raw, sec_title)
            context_tail = " ".join(en_raw.split()[-200:])
            actual_words = self._english_count_for_target(en_resolved)
            if not bool(sec.get("exclude_from_word_budget")):
                accumulated_deviation += actual_words - adjusted_target
                self._upsert_quality_diagnostic(
                    quality_diagnostics, self._section_word_diagnostic(display_name, actual_words, adjusted_target)
                )
            completed_sections.append(display_name)
            section_key = str(sec.get('section_id') or sec_title)
            if self._section_is_complete(adaptive_structure, completed_sections, section_key):
                frozen_section = self._extract_top_level_section(full_draft_en, sec_title)
                self._enqueue_translation_unit(
                    translation_agent,
                    unit_key=section_key, unit_name=sec_title, source_text=frozen_section,
                    translation_sections=translation_sections,
                    english_commit_text=full_draft_en,
                )
            # Collect only already-finished target jobs.  This call never waits,
            # so English Section N+1 can start while target Section N runs.
            translation_sections, translation_glossary = self._collect_translation_results(
                translation_sections, translation_glossary, block=False
            )
            partial_translation = self._assemble_partial_translation(translation_sections, adaptive_structure, include_front=True)
            state.update(
                {
                    "completed_sections": completed_sections,
                    "full_draft_en": full_draft_en,
                    "full_draft_translated": partial_translation,
                    "translation_sections": translation_sections,
                    "translation_glossary": translation_glossary,
                    "context_tail": context_tail,
                    "accumulated_deviation": accumulated_deviation,
                    "quality_diagnostics": quality_diagnostics,
                    "agent_state": agent.export_state(),
                    "data_metadata": data_metadata,
                }
            )
            self._checkpoint(f"section_{len(completed_sections):03d}", state, pct)

        if not state.get("english_finalized"):
            self._progress(91, "english_validation")
            full_draft_en = _clean_trailing_references(full_draft_en, ["Reference", "References", "Reference List"])
            # Validate the completed body while the Working Abstract is still in
            # place. Final front matter must not be generated until all body
            # sections, citation cleanup, and any Discussion rebalance are frozen.
            agent.validate_manuscript_structure(full_draft_en, adaptive_structure, translated=False)
            if mode == "asq":
                self._validate_qualitative_quote_rendering(
                    full_draft_en, quote_invocation_plan, data_metadata
                )
                repeated_quotes = agent.find_repeated_qualitative_quotes(full_draft_en, data_metadata)
                if repeated_quotes:
                    raise RuntimeError(f"Repeated qualitative quotations: {repeated_quotes[:5]}")
            references = []
            seen_refs = set()
            for tag in sorted(agent.used_citations, key=agent._citation_tag_number):
                ref = str((agent.citation_map.get(tag) or {}).get("ref") or "").strip()
                if ref and ref not in seen_refs:
                    references.append(f"- {ref}")
                    seen_refs.add(ref)
            if len(references) < MIN_FINAL_REFERENCES:
                raise RuntimeError(
                    "Insufficient final cited references after semantic validation: "
                    f"{len(references)}/{MIN_FINAL_REFERENCES}."
                )
            full_draft_en = full_draft_en.rstrip() + "\n\n# References\n" + "\n\n".join(references)
            actual_words = self._english_count_for_target(full_draft_en)
            self._upsert_quality_diagnostic(
                quality_diagnostics, self._validate_target_length(full_draft_en, target_words)
            )
            body_only, _reference_tail = agent.validate_reference_tail(
                full_draft_en, minimum_references=MIN_FINAL_REFERENCES
            )
            agent.validate_manuscript_structure(body_only, adaptive_structure, translated=False)
            agent.enforce_resolved_claim_specific_citations(body_only)

            # Body is now frozen. Generate both Final Abstract and Final Keywords
            # from the completed manuscript/evidence, then replace the provisional
            # front matter deterministically. No body rebalance may occur afterward.
            self._progress(92, "final_front_matter")
            final_front = agent.generate_final_front_matter(
                topic, keywords, body_only, data_evidence=user_data_md, mode=mode
            )
            final_abstract = str(final_front.get("abstract") or "").strip()
            final_keywords = list(final_front.get("keywords") or [])
            if not final_abstract or not final_keywords:
                raise RuntimeError("final_front_matter_missing")
            full_draft_en = replace_abstract(full_draft_en, final_abstract)
            full_draft_en = self._replace_keywords(full_draft_en, final_keywords)
            body_only, _reference_tail = agent.validate_reference_tail(
                full_draft_en, minimum_references=MIN_FINAL_REFERENCES
            )
            agent.validate_manuscript_structure(body_only, adaptive_structure, translated=False)
            agent.enforce_resolved_claim_specific_citations(body_only)
            self._upsert_quality_diagnostic(
                quality_diagnostics, self._validate_target_length(full_draft_en, target_words)
            )

            # Final front matter has a different source hash from the Working
            # front matter. Retranslate only __front_matter__; unchanged body
            # translations are retained and reused.
            section_order_for_front = self._unique_top_level_sections(adaptive_structure)
            if section_order_for_front:
                final_front_source = self._extract_front_matter(body_only, section_order_for_front[0][1])
                self._enqueue_translation_unit(
                    translation_agent,
                    unit_key='__front_matter__', unit_name='Final Front Matter',
                    source_text=final_front_source,
                    translation_sections=translation_sections,
                    english_commit_text=full_draft_en,
                )
            translation_sections, translation_glossary = self._collect_translation_results(
                translation_sections, translation_glossary, block=False
            )
            partial_target = self._assemble_partial_translation(
                translation_sections, adaptive_structure, include_front=True
            )
            self._publish_preview(full_draft_en, "english_complete", force=True, complete=True)
            state.update(
                {
                    "full_draft_en": full_draft_en,
                    "full_draft_translated": partial_target,
                    "translation_sections": translation_sections,
                    "translation_glossary": translation_glossary,
                    "final_abstract": final_abstract,
                    "final_keywords": final_keywords,
                    "front_matter_phase": "final",
                    "quality_diagnostics": quality_diagnostics,
                    "english_finalized": True,
                    "translation_complete": False,
                    "agent_state": agent.export_state(),
                }
            )
            self._checkpoint("english_complete", state, 93)
        else:
            full_draft_en = str(state.get("full_draft_en") or "")

        full_draft_translated = str(state.get("full_draft_translated") or "")
        translation_sections = dict(state.get('translation_sections') or translation_sections or {})
        translation_glossary = dict(state.get('translation_glossary') or translation_glossary or {})
        if not state.get("translation_complete") or not full_draft_translated:
            self._progress(94, "translation")
            # English is fully frozen.  Wait for all previously queued section
            # translations before the strict final gate; checkpoint ownership
            # remains on this main execution thread.
            translation_sections, translation_glossary = self._collect_translation_results(
                translation_sections, translation_glossary, block=True
            )
            self._shutdown_translation_worker(wait=True)
            body_only, reference_tail = agent.validate_reference_tail(
                full_draft_en, minimum_references=MIN_FINAL_REFERENCES
            )
            section_order = self._unique_top_level_sections(adaptive_structure)
            if not section_order:
                raise RuntimeError('translation_section_plan_missing')
            front_matter = self._extract_front_matter(body_only, section_order[0][1])
            final_units = [('__front_matter__', 'Front Matter', front_matter)]
            for key, title in section_order:
                final_units.append((key, title, self._extract_top_level_section(body_only, title)))
            for index, (unit_key, unit_name, source_text) in enumerate(final_units, start=1):
                translation_sections, translation_glossary = self._translate_frozen_unit(
                    translation_agent, unit_key=unit_key, unit_name=unit_name, source_text=source_text,
                    target_lang=target_lang, translation_sections=translation_sections,
                    translation_glossary=translation_glossary, adaptive_structure=adaptive_structure,
                    include_front=True, defer_failures=False,
                    english_commit_text=full_draft_en,
                )
                partial = self._assemble_partial_translation(
                    translation_sections, adaptive_structure, include_front=True
                )
                state.update({
                    'full_draft_en': full_draft_en,
                    'full_draft_translated': partial,
                    'translation_sections': translation_sections,
                    'translation_glossary': translation_glossary,
                    'agent_state': agent.export_state(),
                })
                pct = min(96, 94 + int(index * 2 / max(1, len(final_units))))
                self._checkpoint(f'translation_{unit_key}', state, pct)

            unresolved_units = [
                key for key, _name, _source in final_units
                if (translation_sections.get(key) or {}).get('qa_status') not in {'passed', 'passed_with_warning'}
                or not (translation_sections.get(key) or {}).get('translated_text')
            ]
            if unresolved_units:
                raise RuntimeError('translation_final_gate_unresolved:' + ','.join(unresolved_units[:8]))
            full_draft_translated = self._assemble_partial_translation(
                translation_sections, adaptive_structure, include_front=True
            ).rstrip()
            full_draft_translated += (
                f"\n\n# {agent._localized_reference_heading(target_lang)}\n" + reference_tail.strip()
            )
            agent.validate_translation_alignment(full_draft_en, full_draft_translated)
            self._publish_target_preview(
                full_draft_translated, 'translation_complete', force=True, complete=True
            )
            state.update(
                {
                    "full_draft_translated": full_draft_translated,
                    "translation_sections": translation_sections,
                    "translation_glossary": translation_glossary,
                    "translation_complete": True,
                    "agent_state": agent.export_state(),
                }
            )
            self._checkpoint("translation_complete", state, 96)

        existing_output = str(state.get("output_filename") or "").strip()
        if state.get("package_validated") and existing_output:
            existing_path = self.output_dir / os.path.basename(existing_output)
            if existing_path.is_file() and existing_path.stat().st_size > 0:
                self._progress(99, "package_validated")
                return {"output_filename": existing_path.name, "output_path": str(existing_path)}

        self._progress(97, "packaging")
        bj_time = datetime.now(timezone(timedelta(hours=8))).strftime("%Y%m%d_%H%M")
        package = build_validated_package(
            english_markdown=full_draft_en,
            translated_markdown=full_draft_translated,
            english_filename=f"Research_EN_{bj_time}.docx",
            translated_filename=f"Research_{output_suffix}_{bj_time}.docx",
            data_metadata=data_metadata,
            mode=mode,
            citation_audit=agent.citation_audit_log,
            literature_metrics=agent.get_source_metrics(),
            quality_diagnostics=quality_diagnostics,
        )
        output_filename = f"SmartPaper_{bj_time}.zip"
        pending = self.output_dir / (".pending_" + output_filename)
        pending.write_bytes(package.zip_bytes)
        final = self.output_dir / output_filename
        os.replace(pending, final)
        state.update(
            {
                "output_filename": output_filename,
                "package_validated": True,
                "agent_state": agent.export_state(),
            }
        )
        self._checkpoint("package_validated", state, 99)
        return {"output_filename": output_filename, "output_path": str(final)}

    def run(self) -> dict[str, Any]:
        try:
            return self._run_impl()
        finally:
            self._shutdown_translation_worker(wait=False)

    def _progress_activity(self) -> None:
        # Activity remains Executor-owned. We also snapshot actual request
        # counters so the next Executor heartbeat can persist live UI metrics.
        ds_calls, s2_calls, oa_calls = self._compute_request_counts()
        try:
            self.progress_callback(
                activity_only=True,
                ds_calls=ds_calls,
                s2_calls=s2_calls,
                oa_calls=oa_calls,
            )
        except TypeError:
            pass