lllin000_PaperForge/paperforge/worker/ocr_figures.py

from __future__ import annotations

import contextlib
import itertools
import re
from pathlib import Path
from typing import Any

from paperforge.core.io import write_json
from paperforge.worker.ocr_roles import _PANEL_LABEL_PATTERN
from paperforge.worker.ocr_scores import score_figure_caption, score_figure_match

_FIGURE_NUMBER_PATTERN = re.compile(
    r"(?:Figure|Fig\.?|Supplementary\s+Figure|Supplementary\s+Fig\.?|"
    r"Extended\s+Data\s+Figure|Extended\s+Data\s+Fig\.?|图|圖|ͼ)\s*"
    r"(?:S\.?\s*)?(\d+(?:\.\d+)?)",
    flags=re.IGNORECASE,
)

_FRONTMATTER_VISUAL_VETO = (
    "graphical abstract",
    "table of contents",
    "highlights",
    "available with this article",
    "supplementary data",
    "supporting information",
    "video abstract",
    "visual abstract",
)


def _has_frontmatter_visual_veto(text: str) -> bool:
    lower = " ".join(text.lower().split())
    if re.search(r"\btoc\b", lower):
        return True
    return any(
        phrase in lower
        for phrase in _FRONTMATTER_VISUAL_VETO
    )


_BODY_MENTION_VERBS = (
    "shows",
    "illustrates",
    "depicts",
    "presents",
    "summarizes",
    "demonstrates",
    "displays",
    "reveals",
    "indicates",
    "highlights",
    "compares",
    "outlines",
    "reports",
    "lists",
)

_BODY_MENTION_PATTERN = re.compile(
    r"\b(?:Figure|Fig\.?|Supplementary\s+Figure|Supplementary\s+Fig\.?|"
    r"Extended\s+Data\s+Figure|Extended\s+Data\s+Fig\.?)\s+"
    r"\d+\.?\s+(?:" + "|".join(_BODY_MENTION_VERBS) + r")\b",
    flags=re.IGNORECASE,
)

_INLINE_FIGURE_MENTION_VERBS = (
    "shows",
    "show",
    "shown",
    "illustrates",
    "illustrate",
    "depicts",
    "demonstrates",
    "demonstrate",
    "presents",
    "present",
    "indicates",
    "indicate",
    "reveals",
    "reveal",
    "suggests",
    "suggest",
    "summarizes",
    "summarize",
    "compares",
    "compare",
)


def _looks_like_inline_figure_mention(text: str, block: dict | None = None) -> bool:
    """
    Ponytail: position and style are the reliable signals for distinguishing
    figure captions from body mentions.  Text-content heuristics (verb lists,
    word-count thresholds) introduce errors for long captions and non-English
    prose.  Default to False when the block is already classified as a figure
    caption in a display zone.
    """
    if block is not None:
        role = str(block.get("role") or "")
        zone = str(block.get("zone") or "")
        style = str(block.get("style_family") or "")
        if role == "figure_caption":
            return False
        if role == "figure_caption_candidate" and zone != "body_zone":
            return False
        if zone == "display_zone" and style == "legend_like":
            return False
    return False


def _extract_figure_number(text: str) -> int | None:
    m = _FIGURE_NUMBER_PATTERN.search(text)
    if m:
        try:
            return int(float(m.group(1)))
        except ValueError:
            return None
    return None


def _extract_figure_namespace(text: str) -> str:
    lower = text.lower()
    if "supplementary" in lower:
        return "supplementary"
    if "extended data" in lower:
        return "extended_data"
    return "main"


_FIGURE_MARKER_PATTERN = re.compile(
    r"(?P<prefix>Supplementary\s+Figure|Supplementary\s+Fig\.?|"
    r"Extended\s+Data\s+Figure|Extended\s+Data\s+Fig\.?|"
    r"Figure|Fig\.?)\s*"
    r"(?P<s_prefix>S\.?\s*)?"
    r"(?P<number>\d+(?:\.\d+)?)",
    re.I,
)


def _extract_figure_marker(text: str) -> dict:
    m = _FIGURE_MARKER_PATTERN.search(text)
    if not m:
        return {
            "namespace": "main",
            "number": None,
            "raw_prefix": "",
            "has_s_prefix": False,
            "marker_text": "",
        }
    lower = text.lower()
    has_s = bool(m.group("s_prefix"))
    if has_s or "supplementary" in lower or "supporting" in lower or "additional file" in lower or "appendix" in lower:
        namespace = "supplementary"
    elif "extended data" in lower or "extended figure" in lower:
        namespace = "extended_data"
    else:
        namespace = "main"
    number_raw = m.group("number")
    try:
        number = int(float(number_raw))
    except ValueError:
        number = None
    return {
        "namespace": namespace,
        "number": number,
        "raw_prefix": m.group("prefix"),
        "has_s_prefix": has_s,
        "marker_text": m.group(0),
    }


def _validate_page_local_caption_grammar(
    hypotheses: list[dict],
    legends: list[dict],
) -> list[dict]:
    """Annotate local pairing hypotheses with grammar consistency status.

    If a page has multiple captions with different prefix styles
    (e.g. "Figure 1." vs "Fig 1."), those hypotheses are marked
    'conflict'. Uniform styles get 'accepted'. Annotation-only;
    does not change matching behavior.
    """
    import re

    _GRAMMAR_PREFIX_PATTERN = re.compile(r"^(?:Fig(?:ure)?|FIG(?:URE)?)\s*[\.:]?\s*(\d+)", re.IGNORECASE)

    def _grammar_prefix(text: str) -> str:
        m = _GRAMMAR_PREFIX_PATTERN.search(text)
        if not m:
            return ""
        prefix_start = text[:m.start(1)]
        return prefix_start.strip().lower().replace(".", "").replace(":", "").replace(" ", "")

    legend_by_id: dict[str, dict] = {}
    for leg in legends:
        legend_by_id[str(leg.get("block_id", ""))] = leg

    page_grammar: dict[int, set[str]] = {}
    for h in hypotheses:
        lid = str(h.get("legend_block_id", ""))
        leg = legend_by_id.get(lid)
        if leg is None:
            continue
        page = int(leg.get("page", 0) or 0)
        prefix = _grammar_prefix(str(leg.get("text", "") or ""))
        if prefix:
            page_grammar.setdefault(page, set()).add(prefix)

    for h in hypotheses:
        lid = str(h.get("legend_block_id", ""))
        leg = legend_by_id.get(lid)
        if leg is None:
            h["grammar_status"] = "deferred"
            h["grammar_reason"] = "legend not found"
            h["grammar_evidence"] = []
            continue

        page = int(leg.get("page", 0) or 0)
        prefixes = page_grammar.get(page, set())

        if not prefixes:
            h["grammar_status"] = "deferred"
            h["grammar_reason"] = "no grammar prefix detected"
            h["grammar_evidence"] = []
        elif len(prefixes) == 1:
            h["grammar_status"] = "accepted"
            h["grammar_reason"] = f"uniform page-local grammar ({list(prefixes)[0]})"
            h["grammar_evidence"] = [f"grammar_prefix:{list(prefixes)[0]}"]
        else:
            h["grammar_status"] = "conflict"
            h["grammar_reason"] = f"mixed page-local grammar ({', '.join(sorted(prefixes))})"
            h["grammar_evidence"] = [f"grammar_prefix:{p}" for p in sorted(prefixes)]

    return hypotheses


def _format_figure_id(namespace: str, number: int) -> str:
    if namespace == "supplementary":
        return f"figure_s{number:03d}"
    if namespace == "extended_data":
        return f"figure_ed{number:03d}"
    return f"figure_{number:03d}"


def _page_assets_strict_ok(page_legends: list[dict], page_assets_count: int, full_captions_on_page: set[str]) -> bool:
    if len(page_legends) == 1:
        return True
    return not full_captions_on_page


TABLE_LIKE_RAW = frozenset({"table", "table_image"})


def _is_safe_page_assets_group(
    group: dict,
    legend: dict,
    page_blocks: list[dict],
    page_numbered_legend_count: int,
    page_width: float,
    page_height: float,
) -> tuple[bool, list[str]]:
    evidence: list[str] = []

    # 1. Legend must have a formal figure number
    if _extract_figure_number(str(legend.get("text") or "")) is None:
        return False, ["legend_not_numbered"]

    # 2. Single deduped numbered legend on page
    if page_numbered_legend_count != 1:
        return False, ["multiple_numbered_legends"]

    # 3. Page context
    if not page_blocks or page_height <= 0:
        return False, ["missing_page_context"]

    media_blocks = group.get("media_blocks", [])
    cluster_bbox = group.get("cluster_bbox")
    if not cluster_bbox or len(cluster_bbox) < 4:
        return False, ["invalid_cluster_bbox"]

    # 4. Require at least 3 media blocks
    if len(media_blocks) < 3:
        return False, ["insufficient_media_count"]

    # 5. Page coverage
    cw = cluster_bbox[2] - cluster_bbox[0]
    ch = cluster_bbox[3] - cluster_bbox[1]
    page_area = max(1.0, page_width * page_height)
    cluster_area = max(1.0, cw * ch)
    coverage_ratio = cluster_area / page_area
    asset_area_sum = 0.0
    for mb in media_blocks:
        bb = mb.get("bbox") or [0, 0, 0, 0]
        if len(bb) >= 4:
            asset_area_sum += max(0.0, (bb[2] - bb[0]) * (bb[3] - bb[1]))
    fill_ratio = asset_area_sum / cluster_area if cluster_area > 0 else 0.0
    if coverage_ratio > 0.75:
        return False, ["excessive_page_coverage"]
    if coverage_ratio > 0.65 and fill_ratio < 0.25:
        return False, ["excessive_coverage_low_fill"]

    # 6. Compact geometry: media_blocks must form ONE semantic cluster
    semantic_clusters = _cluster_semantic_page_assets(
        media_blocks, page_blocks, page_width, page_height,
    )
    group_block_ids = {str(b.get("block_id")) for b in media_blocks if b.get("block_id")}
    cluster_member_ids = [
        {str(b.get("block_id")) for b in cluster if b.get("block_id")}
        for cluster in semantic_clusters
    ]
    if group_block_ids not in cluster_member_ids:
        return False, ["media_not_compact"]

    # 7. No text separator between any paired media blocks
    for a, b in itertools.combinations(media_blocks, 2):
        if _has_text_separator(a, b, page_blocks):
            return False, ["text_separator_between_assets"]

    # 8. No table-like assets
    for mb in media_blocks:
        raw_label = str(mb.get("raw_label") or "").lower()
        text = str(mb.get("text") or "").lower()
        if raw_label in TABLE_LIKE_RAW and "<img" not in text:
            return False, ["group_contains_table_like_asset"]
        hint = str(mb.get("asset_family_hint") or "")
        if hint == "table_like":
            conf = float(mb.get("asset_family_confidence", 0) or 0)
            if conf >= 0.70:
                return False, ["group_contains_table_like_asset"]

    return True, evidence


def _compute_overlap_score(a_bbox: list[float], b_bbox: list[float]) -> float:
    ax1, ay1, ax2, ay2 = a_bbox
    bx1, by1, bx2, by2 = b_bbox

    ix1 = max(ax1, bx1)
    iy1 = max(ay1, by1)
    ix2 = min(ax2, bx2)
    iy2 = min(ay2, by2)

    iw = max(0, ix2 - ix1)
    ih = max(0, iy2 - iy1)
    intersection = iw * ih
    if intersection == 0:
        return 0.0

    a_area = (ax2 - ax1) * (ay2 - ay1)
    b_area = (bx2 - bx1) * (by2 - by1)
    union = a_area + b_area - intersection
    if union == 0:
        return 0.0
    return intersection / union


def _centroid_y(bbox: list[float]) -> float:
    return (bbox[1] + bbox[3]) / 2


def _looks_like_figure_narrative_prose(text: str) -> bool:
    if not text:
        return False
    after_fig = _FIGURE_NUMBER_PATTERN.sub("", text, count=1).strip().lstrip(". ")
    if after_fig.count(". ") >= 2:
        return True
    prose_markers = ["we ", "our ", "this study ", "here we ", "in this "]
    if any(m in text.lower() for m in prose_markers):
        return True
    if re.search(r"\$?\^\{[^}]+\}\$?", text):
        return True
    return False


def _is_body_mention(block: dict) -> bool:
    zone = str(block.get("zone") or "")
    style_family = str(block.get("style_family") or "")
    raw_role = block.get("raw_role", block.get("role", ""))
    if zone == "display_zone" and style_family == "legend_like":
        return False
    if raw_role == "body_paragraph":
        return True
    if raw_role == "figure_caption_candidate":
        text = block.get("text", "")
        if text and _FIGURE_NUMBER_PATTERN.search(text):
            if _looks_like_inline_figure_mention(text, block):
                return True
            return False
        if text and _looks_like_figure_narrative_prose(text):
            return True
    if block.get("block_label", "") == "text":
        text = block.get("text", "")
        return bool(_BODY_MENTION_PATTERN.search(text))
    return False


_PANEL_SUBCAPTION_PATTERN = re.compile(r"^\s*[a-z][\.\)]\s")
_TRUNCATED_LEGEND_ONLY_PATTERN = re.compile(
    r"^(?:Figure|Fig\.?|Supplementary\s+Figure|Supplementary\s+Fig\.?|"
    r"Extended\s+Data\s+Figure|Extended\s+Data\s+Fig\.?)\s+(?:S)?\d+(?:\.0+)?\.?$",
    flags=re.IGNORECASE,
)


def _is_validation_first_legend_candidate(block: dict) -> bool:
    role = str(block.get("role") or "")
    marker_signature = block.get("marker_signature") or {}
    marker_type = str(marker_signature.get("type") or "none")
    zone = str(block.get("zone") or "")
    style_family = str(block.get("style_family") or "")
    seed_role = str(block.get("seed_role") or "")
    raw_label = str(block.get("raw_label") or "")

    if role in {"figure_caption", "figure_caption_candidate"}:
        return False
    if marker_type != "figure_number":
        return False

    return (zone in {"body_zone", "display_zone", "tail_nonref_hold_zone"} and style_family == "legend_like") or (
        role not in {"figure_caption", "figure_caption_candidate"}
        and zone == "display_zone"
        and raw_label == "figure_title"
        and seed_role == "figure_caption"
    )


def _has_anchor_supported_legend_context(block: dict) -> bool:
    marker_signature = block.get("marker_signature") or {}
    marker_type = str(marker_signature.get("type") or "none")
    style_family = str(block.get("style_family") or "")
    style_family_authority = str(block.get("style_family_authority") or "")
    zone = str(block.get("zone") or "")
    seed_role = str(block.get("seed_role") or "")
    raw_label = str(block.get("raw_label") or "")
    return (
        marker_type == "figure_number"
        and style_family == "legend_like"
        and style_family_authority in {"figure_marker", "figure_family_anchor"}
    ) or (
        marker_type == "figure_number"
        and zone == "display_zone"
        and raw_label == "figure_title"
        and seed_role == "figure_caption"
    )


def _is_insufficient_legend_evidence(block: dict) -> bool:
    text = str(block.get("text") or "").strip()
    marker_signature = block.get("marker_signature") or {}
    marker_type = str(marker_signature.get("type") or "none")
    style_family = str(block.get("style_family") or "")

    if marker_type != "figure_number":
        return False
    if style_family != "legend_like":
        return False
    if not _TRUNCATED_LEGEND_ONLY_PATTERN.fullmatch(text):
        return False

    return True


def _has_strong_explicit_caption_text(block: dict) -> bool:
    text = str(block.get("text") or "").strip()
    role = str(block.get("role") or "")
    if role not in {"figure_caption", "figure_caption_candidate"} and not _is_validation_first_legend_candidate(block):
        return False
    if _is_insufficient_legend_evidence(block):
        return False
    words = [w for w in re.split(r"\s+", text) if w]
    return len(words) >= 5 and len(text) >= 30


def _is_formal_legend(text: str, block: dict | None = None, page_width: float = 1200) -> bool:
    if not text:
        return False

    if _FIGURE_NUMBER_PATTERN.search(text):
        return True

    if block is not None:
        bbox = block.get("bbox") or block.get("block_bbox")
        if bbox and len(bbox) >= 4:
            block_width = bbox[2] - bbox[0]
            if block_width < page_width * 0.3:
                return False

        lower = text.lower().strip()
        axis_words = {
            "days",
            "time",
            "concentration",
            "percentage",
            "volume",
            "frequency",
            "intensity",
            "ratio",
            "expression",
            "level",
            "content",
            "activity",
            "treatment",
            "group",
            "control",
            "dose",
            "response",
            "size",
            "culture",
            "medium",
            "supplemented",
            "differentiation",
            "osteogenic",
            "chondrogenic",
            "adipogenic",
            "induction",
            "stimulation",
            "exposure",
            "incubation",
            "harvest",
            "collection",
        }
        words = set(lower.rstrip(". ").split())
        stop_words = {
            "of",
            "the",
            "in",
            "and",
            "to",
            "a",
            "an",
            "by",
            "at",
            "for",
            "with",
            "on",
            "is",
            "are",
            "was",
            "were",
            "post",
            "after",
            "during",
            "before",
        }
        text_len = len(text)
        if text_len < 100 and words and words.issubset(axis_words | stop_words):
            return False

    return not (_PANEL_SUBCAPTION_PATTERN.match(text) and not _FIGURE_NUMBER_PATTERN.search(text))


def _cluster_bbox(bboxes: list[list[float]]) -> list[float]:
    if not bboxes:
        return [0, 0, 0, 0]
    x1 = min(b[0] for b in bboxes)
    y1 = min(b[1] for b in bboxes)
    x2 = max(b[2] for b in bboxes)
    y2 = max(b[3] for b in bboxes)
    return [x1, y1, x2, y2]


def _asset_gap_right(a: dict, b: dict) -> float:
    ab = a.get("bbox") or [0, 0, 0, 0]
    bb = b.get("bbox") or [0, 0, 0, 0]
    return max(0.0, bb[0] - ab[2])


def _asset_gap_below(a: dict, b: dict) -> float:
    ab = a.get("bbox") or [0, 0, 0, 0]
    bb = b.get("bbox") or [0, 0, 0, 0]
    return max(0.0, bb[1] - ab[3])


def _rect_intersection_area(a: list[float], b: list[float]) -> float:
    x1 = max(a[0], b[0])
    y1 = max(a[1], b[1])
    x2 = min(a[2], b[2])
    y2 = min(a[3], b[3])
    return max(0.0, x2 - x1) * max(0.0, y2 - y1)


def _has_text_separator(a: dict, b: dict, page_blocks: list[dict]) -> bool:
    ab = a.get("bbox", [0, 0, 0, 0])
    bb = b.get("bbox", [0, 0, 0, 0])
    a_page = a.get("page")

    a_y1, a_y2 = ab[1], ab[3]
    b_y1, b_y2 = bb[1], bb[3]
    a_x1, a_x2 = ab[0], ab[2]
    b_x1, b_x2 = bb[0], bb[2]

    v_overlap = max(0.0, min(a_y2, b_y2) - max(a_y1, b_y1))
    h_gap = max(0.0, b_x1 - a_x2, a_x1 - b_x2)
    h_overlap = max(0.0, min(a_x2, b_x2) - max(a_x1, b_x1))
    v_gap = max(0.0, b_y1 - a_y2, a_y1 - b_y2)

    if v_overlap > 0 and h_gap > 0:
        gap_rect = [min(a_x2, b_x2), max(a_y1, b_y1), max(a_x1, b_x1), min(a_y2, b_y2)]
    elif h_overlap > 0 and v_gap > 0:
        gap_rect = [max(a_x1, b_x1), min(a_y2, b_y2), min(a_x2, b_x2), max(a_y1, b_y1)]
    else:
        gap_rect = [min(a_x1, b_x1), min(a_y1, b_y1), max(a_x2, b_x2), max(a_y2, b_y2)]

    for block in page_blocks:
        if block.get("page") != a_page:
            continue
        role = block.get("role", "")
        if role not in (
            "body_paragraph",
            "section_heading",
            "subsection_heading",
            "sub_subsection_heading",
            "backmatter_heading",
            "backmatter_body",
            "tail_candidate_body",
            "reference_heading",
            "reference_item",
        ):
            continue
        txt = str(block.get("text", "") or "").strip()
        if not txt or len(txt) < 10:
            continue
        cb = block.get("bbox", [0, 0, 0, 0])
        block_area = max(1.0, (cb[2] - cb[0]) * (cb[3] - cb[1]))
        if _rect_intersection_area(gap_rect, cb) / block_area >= 0.3:
            return True
    return False


def _cluster_page_assets(
    page_assets: list[dict],
    all_blocks: list[dict],
    n_legends: int,
    page_width: float,
    page_height: float,
) -> list[list[dict]]:
    if not page_assets:
        return []
    if len(page_assets) == 1:
        return [list(page_assets)]

    h_threshold = max(page_width * 0.12, 40.0)
    v_threshold = max(min(page_width, page_height) * 0.08, 40.0)
    parent = list(range(len(page_assets)))

    def find(x):
        while parent[x] != x:
            parent[x] = parent[parent[x]]
            x = parent[x]
        return x

    def union(x, y):
        px, py = find(x), find(y)
        if px != py:
            parent[py] = px

    for i in range(len(page_assets)):
        for j in range(i + 1, len(page_assets)):
            a, b = page_assets[i], page_assets[j]
            ab = a.get("bbox", [0, 0, 0, 0])
            bb = b.get("bbox", [0, 0, 0, 0])

            h_gap = max(0.0, bb[0] - ab[2], ab[0] - bb[2])
            v_gap = max(0.0, bb[1] - ab[3], ab[1] - bb[3])

            if h_gap > h_threshold:
                if n_legends <= 1:
                    a_y1, a_y2 = ab[1], ab[3]
                    b_y1, b_y2 = bb[1], bb[3]
                    y_overlap = max(0, min(a_y2, b_y2) - max(a_y1, b_y1))
                    shorter_h = min(a_y2 - a_y1, b_y2 - b_y1)
                    if shorter_h > 0 and y_overlap / shorter_h >= 0.5:
                        if h_gap > page_width * 0.25:
                            continue
                    else:
                        continue
                else:
                    continue

            if v_gap > v_threshold:
                continue

            if _has_text_separator(a, b, all_blocks):
                continue

            union(i, j)

    clusters: dict[int, list[dict]] = {}
    for i, block in enumerate(page_assets):
        root = find(i)
        if root not in clusters:
            clusters[root] = []
        clusters[root].append(block)

    return list(clusters.values())


def _media_clusters(blocks: list[dict], page_width: float = 1200) -> list[list[dict]]:
    media = [
        b
        for b in blocks
        if not b.get("_non_body_media")
        and (
            b.get("role") == "figure_asset"
            or (
                b.get("role") == "media_asset"
                and (
                    b.get("raw_label", "") in {"image", "chart", "figure"}
                    or (b.get("raw_label", "") == "table" and "<img" in str(b.get("text") or "").lower())
                )
            )
        )
    ]
    media.sort(key=lambda b: (b.get("page", 0), b.get("bbox", [0, 0, 0, 0])[1], b.get("bbox", [0, 0, 0, 0])[0]))

    clusters: list[list[dict]] = []
    for m in media:
        page = m.get("page", 0)
        bbox = m.get("bbox", [0, 0, 0, 0])
        placed = False
        for cluster in clusters:
            c_page = cluster[0].get("page", 0)
            if c_page != page:
                continue
            c_bbox = _cluster_bbox([cb.get("bbox", [0, 0, 0, 0]) for cb in cluster])
            mx1, my1, mx2, my2 = bbox
            cx1, cy1, cx2, cy2 = c_bbox
            h_overlap = mx1 < cx2 and cx1 < mx2
            v_overlap = my1 < cy2 and cy1 < my2
            h_gap = max(cx1 - mx2, mx1 - cx2, 0)
            v_gap = max(cy1 - my2, my1 - cy2, 0)
            small_h = min(my2 - my1, cy2 - cy1)
            if h_overlap and v_gap < small_h * 0.3:
                cluster.append(m)
                placed = True
                break
            if v_overlap and h_gap < 50:
                cluster.append(m)
                placed = True
                break
        if not placed:
            clusters.append([m])
    return clusters


def _filter_figure_assets(assets: list[dict]) -> list[dict]:
    result = []
    for b in assets:
        if b.get("_non_body_media"):
            continue
        role = b.get("role", "")
        if role == "figure_asset":
            result.append(b)
        elif role == "media_asset":
            rl = str(b.get("raw_label", "") or "")
            if (
                rl in {"image", "chart", "figure"}
                or not rl.strip()
                or rl == "table"
                and "<img" in str(b.get("text", "") or "").lower()
            ):
                result.append(b)
    return result


def _bbox_width(bbox: list[float]) -> float:
    return float(bbox[2] - bbox[0])


def _bbox_height(bbox: list[float]) -> float:
    return float(bbox[3] - bbox[1])


def _bbox_center_y(bbox: list[float]) -> float:
    return float(bbox[1] + bbox[3]) / 2.0


def _candidate_group_entry(
    group_id: str, page: int, media_blocks: list[dict], group_type: str, evidence: list[str]
) -> dict:
    return {
        "group_id": group_id,
        "page": page,
        "group_type": group_type,
        "asset_block_ids": [b.get("block_id") for b in media_blocks if b.get("block_id") is not None],
        "media_blocks": media_blocks,
        "cluster_bbox": _cluster_bbox([b.get("bbox", [0, 0, 0, 0]) for b in media_blocks]),
        "group_evidence": evidence,
    }


def _semantic_group_topology(groups: list[dict]) -> set[frozenset[str]]:
    return {frozenset(str(bid) for bid in g.get("asset_block_ids", [])) for g in groups}


def _estimate_page_height(page_blocks: list[dict]) -> float:
    explicit = [float(b["page_height"]) for b in page_blocks if b.get("page_height")]
    if explicit:
        return max(explicit)
    bottoms = [float((b.get("bbox") or [0, 0, 0, 0])[3]) for b in page_blocks if len(b.get("bbox") or []) >= 4]
    return max(bottoms, default=1600.0)


def _cluster_semantic_page_assets(
    page_assets: list[dict],
    page_blocks: list[dict],
    page_width: float,
    page_height: float,
) -> list[list[dict]]:
    if not page_assets:
        return []
    if len(page_assets) == 1:
        return [list(page_assets)]

    # Semantic grouping stays stricter than the legacy caption-aware path so
    # ordinary side-by-side same-page figures do not collapse into one group.
    h_threshold = max(page_width * 0.03, 40.0)
    v_threshold = max(min(page_width, page_height) * 0.08, 40.0)
    parent = list(range(len(page_assets)))

    def find(x):
        while parent[x] != x:
            parent[x] = parent[parent[x]]
            x = parent[x]
        return x

    def union(x, y):
        px, py = find(x), find(y)
        if px != py:
            parent[py] = px

    for i in range(len(page_assets)):
        for j in range(i + 1, len(page_assets)):
            a, b = page_assets[i], page_assets[j]
            ab = a.get("bbox", [0, 0, 0, 0])
            bb = b.get("bbox", [0, 0, 0, 0])

            h_gap = max(0.0, bb[0] - ab[2], ab[0] - bb[2])
            v_gap = max(0.0, bb[1] - ab[3], ab[1] - bb[3])

            if h_gap > h_threshold:
                continue
            if v_gap > v_threshold:
                continue
            if _has_text_separator(a, b, page_blocks):
                continue

            union(i, j)

    clusters: dict[int, list[dict]] = {}
    for i, block in enumerate(page_assets):
        root = find(i)
        if root not in clusters:
            clusters[root] = []
        clusters[root].append(block)

    return list(clusters.values())


def _build_semantic_figure_groups_from_assets(
    assets: list[dict],
    all_blocks: list[dict],
    *,
    page_width: float = 1200,
) -> list[dict]:
    # semantic groups are caption-independent; caption/text may only act as neutral separators
    # and caption count may not change topology.
    media = _filter_figure_assets(assets)
    groups: list[dict] = []
    next_id = 1

    by_page: dict[int, list[dict]] = {}
    for block in media:
        by_page.setdefault(int(block.get("page", 0) or 0), []).append(block)

    for page, page_media in by_page.items():
        page_blocks = [b for b in all_blocks if int(b.get("page", 0) or 0) == page]
        page_height = _estimate_page_height(page_blocks)
        clusters = _cluster_semantic_page_assets(page_media, page_blocks, page_width, page_height)
        page_groups: list[dict] = []
        for cluster in clusters:
            gt = "distance_cluster" if len(cluster) >= 2 else "single_asset"
            entry = _candidate_group_entry(
                f"group_{next_id:04d}",
                page,
                cluster,
                gt,
                ["same_page", "distance_clustered" if gt == "distance_cluster" else "single_asset"],
            )
            page_groups.append(entry)
            next_id += 1

        page_group_count = len(page_groups)
        page_distance_cluster_count = sum(1 for g in page_groups if g["group_type"] == "distance_cluster")
        for g in page_groups:
            g["page_group_count"] = page_group_count
            g["page_distance_cluster_count"] = page_distance_cluster_count
            g["safe_auto_match"] = (
                page_group_count == 1
                and g["group_type"] == "distance_cluster"
                and len(g.get("media_blocks", [])) >= 2
            )

        groups.extend(page_groups)

    return groups


def _build_caption_band_group_assist(
    semantic_groups: list[dict],
    page_legends: list[dict],
    page_media: list[dict],
    page_height: float,
) -> dict[str, dict]:
    if len(page_legends) < 2 or not semantic_groups or not page_media:
        return {}

    band_map = _partition_assets_by_caption_bands(page_legends, page_media, page_height)
    assist: dict[str, dict] = {}
    for group in semantic_groups:
        asset_ids = {str(bid) for bid in group.get("asset_block_ids", []) if bid is not None}
        if not asset_ids:
            assist[str(group.get("group_id", ""))] = {
                "caption_band_ids": [],
                "best_caption_band_id": None,
                "overlap_ratio": 0.0,
                "band_overlap": {},
                "evidence": [],
            }
            continue

        overlap_by_band: dict[str, float] = {}
        for band_id, band_assets in band_map.items():
            band_asset_ids = {str(a.get("block_id", "")) for a in band_assets if a.get("block_id") is not None}
            if not band_asset_ids:
                continue
            overlap = len(asset_ids & band_asset_ids) / max(1, len(asset_ids))
            if overlap > 0:
                overlap_by_band[str(band_id)] = overlap

        best_caption_band_id = None
        overlap_ratio = 0.0
        if overlap_by_band:
            best_caption_band_id, overlap_ratio = max(overlap_by_band.items(), key=lambda item: item[1])

        assist[str(group.get("group_id", ""))] = {
            "caption_band_ids": sorted(overlap_by_band),
            "best_caption_band_id": best_caption_band_id,
            "overlap_ratio": overlap_ratio,
            "band_overlap": overlap_by_band,
            "evidence": ["caption_band_assist"] if overlap_by_band else [],
        }

    return assist


def _build_candidate_figure_groups_from_assets(
    assets: list[dict],
    all_blocks: list[dict],
    legends: list[dict],
    page_width: float = 1200,
) -> list[dict]:
    groups = _build_semantic_figure_groups_from_assets(assets, all_blocks, page_width=page_width)
    groups_by_page: dict[int, list[dict]] = {}
    media_by_page: dict[int, list[dict]] = {}
    for group in groups:
        groups_by_page.setdefault(int(group.get("page", 0) or 0), []).append(group)
    for block in _filter_figure_assets(assets):
        media_by_page.setdefault(int(block.get("page", 0) or 0), []).append(block)

    for page, page_groups in groups_by_page.items():
        page_blocks = [b for b in all_blocks if int(b.get("page", 0) or 0) == page]
        page_height = _estimate_page_height(page_blocks)
        page_legends = [l for l in legends if l.get("page") == page]
        band_assist = _build_caption_band_group_assist(
            page_groups,
            page_legends,
            media_by_page.get(page, []),
            page_height,
        )
        for group in page_groups:
            group["page_legend_count"] = len(page_legends)
            group["assist"] = band_assist.get(str(group.get("group_id", "")), {
                "caption_band_ids": [],
                "best_caption_band_id": None,
                "overlap_ratio": 0.0,
                "band_overlap": {},
                "evidence": [],
            })

    return groups


def _score_legend_to_group(
    legend: dict,
    group: dict,
    *,
    caption_score: dict,
    page_width: float = 1200,
    page_height: float = 0,
    page_blocks: list[dict] | None = None,
    page_numbered_legend_count: int = 0,
    anchor_supported: bool = False,
    caption_text_supported: bool = False,
    family_supported: bool = False,
    zone_supported: bool = False,
) -> dict:
    gt = group.get("group_type", "")

    if gt == "distance_cluster":
        num_assets = len(group.get("media_blocks", []))
        if group.get("safe_auto_match") and num_assets >= 2:
            return {
                "score": 0.85,
                "decision": "matched",
                "evidence": ["same_page", "distance_clustered", "safe_auto_match"],
            }

        cluster_bbox = group.get("cluster_bbox", [0, 0, 0, 0])
        match_score = score_figure_match(
            legend,
            {"bbox": cluster_bbox, "page": group.get("page", 0)},
            caption_score=caption_score,
            anchor_supported=anchor_supported,
            caption_text_supported=caption_text_supported,
            family_supported=family_supported,
            zone_supported=zone_supported,
        )
        if num_assets >= 2 and match_score.get("score", 0) > 0:
            match_score = dict(match_score)
            match_score["score"] = min(1.0, match_score["score"] + 0.15)
            match_score.setdefault("evidence", []).append("multi_asset_coherence_bonus")
            if match_score["score"] >= 0.5 and match_score.get("decision") in ("candidate", "ambiguous"):
                match_score["decision"] = "matched"
        return match_score

    if gt == "page_assets":
        if not page_blocks or page_height <= 0:
            return {
                "score": 0.0,
                "decision": "rejected",
                "evidence": ["page_assets_missing_page_context"],
            }
        safe, gate_evidence = _is_safe_page_assets_group(
            group, legend, page_blocks, page_numbered_legend_count,
            page_width=page_width, page_height=page_height,
        )
        if not safe:
            return {
                "score": 0.0,
                "decision": "rejected",
                "evidence": ["page_assets_requires_gate"] + gate_evidence,
            }
        basic = {
            "score": 0.72,
            "decision": "matched",
            "evidence": ["same_page", "page_assets_safe_gate"] + gate_evidence,
        }
        if family_supported:
            basic["score"] = min(1.0, basic["score"] + 0.1)
            basic["evidence"].append("family_supported")
        return basic

    if gt == "single_asset":
        asset = group["media_blocks"][0]
        return score_figure_match(
            legend,
            asset,
            caption_score=caption_score,
            anchor_supported=anchor_supported,
            caption_text_supported=caption_text_supported,
            family_supported=family_supported,
            zone_supported=zone_supported,
        )

    cluster_bbox = group.get("cluster_bbox", [0, 0, 0, 0])
    match_score = score_figure_match(
        legend,
        {"bbox": cluster_bbox, "page": group.get("page", 0)},
        caption_score=caption_score,
        anchor_supported=anchor_supported,
        caption_text_supported=caption_text_supported,
        family_supported=family_supported,
        zone_supported=zone_supported,
    )
    num_assets = len(group.get("media_blocks", []))
    if num_assets > 1 and match_score.get("score", 0) > 0:
        coherence_bonus = 0.15
        match_score = dict(match_score)
        match_score["score"] = min(1.0, match_score["score"] + coherence_bonus)
        match_score.setdefault("evidence", []).append("multi_asset_coherence_bonus")
        if match_score["score"] >= 0.5 and match_score.get("decision") in ("ambiguous", "candidate"):
            match_score["decision"] = "matched"
        if match_score.get("decision") == "rejected":
            match_score["decision"] = "candidate"
    return match_score


def _asset_page_id(page: Any, block_id: Any) -> tuple[int, str] | None:
    if block_id in (None, ""):
        return None
    return (int(page or 0), str(block_id))


def _make_local_pairing_hypothesis(
    legend: dict,
    group: dict,
    *,
    mode: str,
    local_score: float,
    evidence: list[str] | None = None,
    conflicts: list[str] | None = None,
) -> dict:
    media_blocks = group.get("media_blocks", [])
    if media_blocks:
        asset_ids = [
            aid
            for block in media_blocks
            if (aid := _asset_page_id(block.get("page", group.get("page", 0)), block.get("block_id", ""))) is not None
        ]
    else:
        group_page = group.get("page", 0)
        asset_ids = [
            aid for block_id in group.get("asset_block_ids", []) if (aid := _asset_page_id(group_page, block_id)) is not None
        ]
    return {
        "legend_block_id": str(legend.get("block_id", "")),
        "group_id": str(group.get("group_id", "")),
        "mode": str(mode),
        "local_score": float(local_score),
        "evidence": list(evidence or []),
        "conflicts": list(conflicts or []),
        "would_consume_asset_ids": asset_ids,
    }


def _infer_local_pairing_mode(legend: dict, group: dict, *, page_width: float = 1200) -> str:
    legend_bbox = legend.get("bbox") or legend.get("block_bbox") or [0, 0, 0, 0]
    group_bbox = group.get("cluster_bbox") or [0, 0, 0, 0]
    if len(legend_bbox) >= 4 and len(group_bbox) >= 4:
        legend_width = float(legend_bbox[2] - legend_bbox[0])
        legend_height = max(1.0, float(legend_bbox[3] - legend_bbox[1]))
        group_height = max(1.0, float(group_bbox[3] - group_bbox[1]))
        y_overlap = max(0.0, min(legend_bbox[3], group_bbox[3]) - max(legend_bbox[1], group_bbox[1]))
        x_gap = min(abs(group_bbox[0] - legend_bbox[2]), abs(legend_bbox[0] - group_bbox[2]))
        if (
            y_overlap >= min(legend_height, group_height) * 0.5
            and x_gap <= float(page_width) * 0.25
            and legend_width < float(page_width) * 0.6
        ):
            return "caption_sidecar"
    vertical_side = _asset_vertical_side(legend, group)
    if vertical_side == "below":
        return "caption_above"
    if vertical_side == "above":
        return "caption_below"
    return "caption_below"


def _mark_hypothesis_conflict(hypothesis: dict, conflict: str) -> None:
    if not conflict:
        return
    conflicts = hypothesis.setdefault("conflicts", [])
    if conflict not in conflicts:
        conflicts.append(conflict)


def _project_asset_record(asset: dict) -> dict:
    record = {
        "block_id": asset.get("block_id", ""),
        "bbox": asset.get("bbox", [0, 0, 0, 0]),
        "page": int(asset.get("page", 0) or 0),
    }
    for key in ("asset_family_hint", "asset_family_confidence", "asset_family_evidence"):
        if key in asset:
            record[key] = asset.get(key)
    return record


def _has_protected_figure_ownership(entry: dict, *, unresolved_legend_ids: set[str] | None = None) -> bool:
    settlement_type = str(entry.get("settlement_type") or "")
    if settlement_type not in {
        "same_page",
        "grouped_approximate",
        "composite_parent",
        "scoped_composite_parent",
    }:
        return False
    matched_assets = entry.get("matched_assets", []) or []
    if len(matched_assets) < 1:
        return False
    flags = set(entry.get("flags", []) or [])
    if "ordering_violation" in flags or "ownership_ambiguous" in flags:
        return False
    unresolved = unresolved_legend_ids or set()
    if settlement_type == "same_page":
        if len(matched_assets) > 1 and "composite_parent_match" not in flags:
            return False
        if unresolved and str(entry.get("legend_block_id", "")) not in unresolved and len(matched_assets) > 1:
            return False
    return True


class FigureOwnershipRegistry:
    def __init__(
        self,
        *,
        used_group_ids: set[str] | None = None,
        used_asset_page_ids: set[tuple[int, str]] | None = None,
    ) -> None:
        self.used_group_ids = used_group_ids if used_group_ids is not None else set()
        self.used_asset_page_ids = used_asset_page_ids if used_asset_page_ids is not None else set()
        self.group_states: dict[str, dict[str, str]] = {}
        self.asset_states: dict[tuple[int, str], dict[str, str]] = {}

    def reserve_group(self, group_id: str, *, reason: str) -> None:
        self._require_reason(reason)
        self.group_states[str(group_id)] = {"state": "reserved", "reason": reason}

    def transition_reserved_to_held(self, group_id: str, *, reason: str) -> None:
        self._require_reason(reason)
        self.group_states[str(group_id)] = {"state": "held", "reason": reason}

    def match_group(self, group: dict, *, owner_id: str, owner_family: str) -> None:
        group_id = str(group.get("group_id", ""))
        if group_id:
            self.group_states[group_id] = {
                "state": "matched",
                "owner_id": str(owner_id),
                "owner_family": str(owner_family),
            }
            self.used_group_ids.add(group_id)
        self.mark_assets_owned(self._group_asset_page_ids(group), owner_id=owner_id, owner_family=owner_family)

    def mark_assets_owned(
        self,
        asset_ids: list[tuple[int, str]],
        *,
        owner_id: str,
        owner_family: str,
    ) -> None:
        normalized_owner_id = str(owner_id)
        normalized_owner_family = str(owner_family)
        for asset_id in asset_ids:
            normalized = _asset_page_id(asset_id[0], asset_id[1])
            if normalized is None:
                continue
            current = self.asset_states.get(normalized)
            if current and current.get("owner_family") != normalized_owner_family:
                raise ValueError(f"Asset {normalized[1]} already owned by {current.get('owner_family')}")
            self.asset_states[normalized] = {
                "state": f"owned_by_{normalized_owner_family}",
                "owner_id": normalized_owner_id,
                "owner_family": normalized_owner_family,
            }
            self.used_asset_page_ids.add(normalized)

    def block_asset(self, asset_id: tuple[int, str], *, reason: str) -> None:
        self._require_reason(reason)
        normalized = _asset_page_id(asset_id[0], asset_id[1])
        if normalized is None:
            raise ValueError("blocked asset requires an asset id")
        self.asset_states[normalized] = {"state": "blocked", "reason": reason}

    def can_consume_group(self, group: dict) -> bool:
        group_id = str(group.get("group_id", ""))
        if group_id and group_id in self.used_group_ids:
            return False
        return self.can_consume_assets(self._group_asset_page_ids(group))

    def can_consume_assets(self, asset_ids: list[tuple[int, str]]) -> bool:
        for asset_id in asset_ids:
            normalized = _asset_page_id(asset_id[0], asset_id[1])
            if normalized is None:
                continue
            if normalized in self.used_asset_page_ids:
                return False
            state = self.asset_states.get(normalized)
            if state and state.get("state") == "soft_reserved":
                return False
        return True

    def soft_reserve_assets(self, asset_ids: list[tuple[int, str]], *, owner_id: str, reason: str) -> None:
        self._require_reason(reason)
        normalized_owner_id = str(owner_id)
        for asset_id in asset_ids:
            normalized = _asset_page_id(asset_id[0], asset_id[1])
            if normalized is None:
                continue
            self.asset_states[normalized] = {
                "state": "soft_reserved",
                "owner_id": normalized_owner_id,
                "reason": reason,
            }

    def finalize_soft_reservation(self, asset_ids: list[tuple[int, str]], *, owner_id: str, owner_family: str) -> None:
        normalized_owner_id = str(owner_id)
        normalized_owner_family = str(owner_family)
        for asset_id in asset_ids:
            normalized = _asset_page_id(asset_id[0], asset_id[1])
            if normalized is None:
                continue
            current = self.asset_states.get(normalized)
            if current is None or current.get("state") != "soft_reserved":
                continue
            self.asset_states[normalized] = {
                "state": f"owned_by_{normalized_owner_family}",
                "owner_id": normalized_owner_id,
                "owner_family": normalized_owner_family,
            }
            self.used_asset_page_ids.add(normalized)

    def release_soft_reservation(self, asset_ids: list[tuple[int, str]], *, owner_id: str) -> None:
        normalized_owner_id = str(owner_id)
        for asset_id in asset_ids:
            normalized = _asset_page_id(asset_id[0], asset_id[1])
            if normalized is None:
                continue
            current = self.asset_states.get(normalized)
            if current and current.get("state") == "soft_reserved" and current.get("owner_id") == normalized_owner_id:
                del self.asset_states[normalized]

    def _group_asset_page_ids(self, group: dict) -> list[tuple[int, str]]:
        media_blocks = group.get("media_blocks", [])
        if media_blocks:
            return [
                normalized
                for block in media_blocks
                if (normalized := _asset_page_id(block.get("page", group.get("page", 0)), block.get("block_id", "")))
                is not None
            ]
        group_page = group.get("page", 0)
        return [
            normalized
            for bid in group.get("asset_block_ids", [])
            if (normalized := _asset_page_id(group_page, bid)) is not None
        ]

    def _require_reason(self, reason: str) -> None:
        if not str(reason).strip():
            raise ValueError("ownership reason is required")


def _ownership_decision_metadata(decision: str, provenance: str, *, strong: bool, reason: str = "") -> dict:
    return {
        "ownership_decision": decision,
        "decision_provenance": provenance,
        "strong_ownership": strong,
        "decision_reason": reason,
    }


def _fallback_eligible_asset_page_ids(
    asset_page_ids: list[tuple[int, str]],
    *,
    used_asset_page_ids: set[tuple[int, str]],
    blocked_asset_page_ids: set[tuple[int, str]],
    grouped_asset_page_ids: set[tuple[int, str]] | None = None,
    allow_grouped: bool = False,
) -> list[tuple[int, str]]:
    grouped = grouped_asset_page_ids or set()
    eligible: list[tuple[int, str]] = []
    for page, block_id in asset_page_ids:
        asset_id = _asset_page_id(page, block_id)
        if asset_id is None:
            continue
        if asset_id in used_asset_page_ids or asset_id in blocked_asset_page_ids:
            continue
        if not allow_grouped and asset_id in grouped:
            continue
        eligible.append(asset_id)
    return eligible


def _fallback_can_consume(
    asset_page_ids: list[tuple[int, str]],
    *,
    used_asset_page_ids: set[tuple[int, str]],
    blocked_asset_page_ids: set[tuple[int, str]],
    grouped_asset_page_ids: set[tuple[int, str]] | None = None,
    allow_grouped: bool = False,
) -> bool:
    normalized = [aid for aid in (_asset_page_id(page, block_id) for page, block_id in asset_page_ids) if aid is not None]
    return len(_fallback_eligible_asset_page_ids(
        normalized,
        used_asset_page_ids=used_asset_page_ids,
        blocked_asset_page_ids=blocked_asset_page_ids,
        grouped_asset_page_ids=grouped_asset_page_ids,
        allow_grouped=allow_grouped,
    )) == len(normalized)


def _fallback_eligible_groups(
    groups: list[dict],
    *,
    used_group_ids: set[str],
    used_asset_page_ids: set[tuple[int, str]],
    blocked_asset_page_ids: set[tuple[int, str]] | None = None,
    grouped_asset_page_ids: set[tuple[int, str]] | None = None,
    allow_grouped: bool = True,
) -> list[dict]:
    blocked = blocked_asset_page_ids or set()
    eligible: list[dict] = []
    for group in groups:
        group_id = str(group.get("group_id", ""))
        if group_id and group_id in used_group_ids:
            continue
        asset_ids = [
            aid
            for aid in (
                _asset_page_id(group.get("page", 0), bid)
                for bid in group.get("asset_block_ids", [])
            )
            if aid is not None
        ]
        if not _fallback_can_consume(
            asset_ids,
            used_asset_page_ids=used_asset_page_ids,
            blocked_asset_page_ids=blocked,
            grouped_asset_page_ids=grouped_asset_page_ids,
            allow_grouped=allow_grouped,
        ):
            continue
        eligible.append(group)
    return eligible


def _expand_matched_assets_locally(
    legend: dict,
    matched_assets: list[dict],
    assets: list[dict],
    ownership: FigureOwnershipRegistry,
    page_captions: list[dict],
) -> list[dict]:
    if not matched_assets:
        return matched_assets

    legend_bbox = legend.get("bbox") or [0, 0, 0, 0]
    legend_top = legend_bbox[1] if len(legend_bbox) >= 4 else 0
    current = list(matched_assets)
    changed = True

    while changed:
        changed = False
        cluster_bbox = _cluster_bbox([a.get("bbox", [0, 0, 0, 0]) for a in current])
        cx1, cy1, cx2, cy2 = cluster_bbox

        lower_caption_tops = [
            (cap.get("bbox") or [0, 0, 0, 0])[1]
            for cap in page_captions
            if cap.get("block_id") != legend.get("block_id") and (cap.get("bbox") or [0, 0, 0, 0])[1] > cy2
        ]
        next_caption_top = min(lower_caption_tops) if lower_caption_tops else None

        for asset in assets:
            ap = asset.get("page", 0)
            aid = asset.get("block_id", "")
            if not aid or not ownership.can_consume_assets([(ap, aid)]):
                continue
            if ap != legend.get("page", 0):
                continue

            bbox = asset.get("bbox") or [0, 0, 0, 0]
            ax1, ay1, ax2, ay2 = bbox
            if ay2 >= legend_top:
                continue
            if next_caption_top is not None and ay1 >= next_caption_top:
                continue

            vertical_gap = max(0.0, max(ay1 - cy2, cy1 - ay2))
            horizontal_overlap = max(0.0, min(cx2, ax2) - max(cx1, ax1))
            horizontal_gap = max(0.0, ax1 - cx2, cx1 - ax2)
            min_width = max(1.0, min(cx2 - cx1, ax2 - ax1))
            wide_enough_overlap = horizontal_overlap >= min_width * 0.25 or (
                horizontal_gap <= 30.0 and horizontal_gap < min_width * 0.1
            )
            touches_stack = vertical_gap <= 30.0

            if touches_stack and wide_enough_overlap:
                current.append(asset)
                ownership.mark_assets_owned([(ap, aid)], owner_id=str(legend.get("block_id", "")), owner_family="figure")
                changed = True

    current.sort(key=lambda a: ((a.get("bbox") or [0, 0, 0, 0])[1], (a.get("bbox") or [0, 0, 0, 0])[0]))
    return current


def _caption_style_match(block: dict, all_blocks: list[dict]) -> bool:
    span = block.get("span_metadata") or {}
    if isinstance(span, list):
        span = span[0] if span else {}
    block_size = span.get("size")
    block_font = str(span.get("font", "") or "").lower()
    if block_size is None and not block_font:
        return False
    for b in all_blocks:
        if b is block:
            continue
        if b.get("role") != "figure_caption":
            continue
        s = b.get("span_metadata") or {}
        if isinstance(s, list):
            s = s[0] if s else {}
        sz = s.get("size")
        fn = str(s.get("font", "") or "").lower()
        if block_size is not None and sz is not None and abs(block_size - sz) <= 1.0:
            return True
        if block_font and fn and block_font == fn:
            return True
    return False


def _precaption_media_region(media_cluster: list[dict], caption_block: dict) -> bool:
    cluster_bottom = max(b.get("bbox", [0, 0, 0, 0])[3] for b in media_cluster)
    caption_top = caption_block.get("bbox", [0, 0, 0, 0])[1]
    tolerance = 10
    return cluster_bottom < caption_top + tolerance


def _compute_candidate_figure_regions(blocks: list[dict], page_width: float = 1200) -> list[dict]:
    clusters = _media_clusters(blocks, page_width)
    captions = [b for b in blocks if b.get("role") == "figure_caption"]
    regions: list[dict] = []
    for i, cluster in enumerate(clusters):
        cluster_bbox = _cluster_bbox([b.get("bbox", [0, 0, 0, 0]) for b in cluster])
        page = cluster[0].get("page", 0)
        attached: list[dict] = []
        unvalidated: list[dict] = []
        for cap in captions:
            if cap.get("page", 0) != page:
                continue
            if _precaption_media_region(cluster, cap):
                attached.append(cap)
            else:
                unvalidated.append(cap)
        regions.append(
            {
                "region_id": f"region_{i + 1:03d}",
                "page": page,
                "cluster_bbox": cluster_bbox,
                "media_blocks": cluster,
                "attached_captions": attached,
                "unvalidated_captions": unvalidated,
            }
        )
    return regions


def is_embedded_figure_text(block: dict, all_blocks: list[dict], page_width: float = 1200) -> bool:
    role = block.get("role", "")
    if role in ("figure_caption", "figure_caption_candidate"):
        return False
    text = block.get("text", "")
    if text and _is_formal_legend(text, block, page_width):
        return False

    block_bbox = block.get("bbox") or block.get("block_bbox")
    if not block_bbox or len(block_bbox) < 4:
        return False
    bx1, by1, bx2, by2 = block_bbox[:4]
    cx = (bx1 + bx2) / 2
    cy = (by1 + by2) / 2
    block_page = block.get("page", 0)

    for other in all_blocks:
        if other is block:
            continue
        if other.get("role") not in ("figure_asset", "media_asset"):
            continue
        if other.get("page", 0) != block_page:
            continue
        ob = other.get("bbox") or other.get("block_bbox")
        if not ob or len(ob) < 4:
            continue
        ox1, oy1, ox2, oy2 = ob[:4]
        if ox1 <= cx <= ox2 and oy1 <= cy <= oy2:
            return True
        block_width = bx2 - bx1
        if block_width < page_width * 0.2:
            h_overlap = bx1 < ox2 and ox1 < bx2
            if h_overlap:
                return True

    return False


def _formal_figure_caption_blocks(blocks: list[dict]) -> dict[int, list[dict]]:
    by_page: dict[int, list[dict]] = {}
    for block in blocks:
        role = str(block.get("role") or "")
        text = str(block.get("text") or "")
        if role not in {"figure_caption", "figure_caption_candidate"}:
            continue
        if _extract_figure_number(text) is None:
            continue
        page = int(block.get("page", 0) or 0)
        by_page.setdefault(page, []).append(block)
    for page_blocks in by_page.values():
        page_blocks.sort(key=lambda b: (b.get("bbox") or [0, 0, 0, 0])[1])
    return by_page


def _caption_width_ratio(block: dict, page_width: float) -> float:
    bbox = block.get("bbox") or [0, 0, 0, 0]
    if len(bbox) < 4 or page_width <= 0:
        return 0.0
    return max(0.0, float(bbox[2] - bbox[0]) / float(page_width))


def _same_page_narrow_caption_column(page_captions: list[dict], page_width: float) -> list[dict]:
    formal = [
        cap
        for cap in page_captions
        if _extract_figure_number(str(cap.get("text", ""))) is not None
        if _caption_width_ratio(cap, page_width) < 0.6
        and not _PANEL_LABEL_PATTERN.match(str(cap.get("text", "")).strip())
    ]
    if len(formal) < 2:
        return []

    def _center_x(cap: dict) -> float:
        bb = cap.get("bbox") or [0, 0, 0, 0]
        return (bb[0] + bb[2]) / 2

    max_spread = page_width * 0.08
    centers = [_center_x(cap) for cap in formal]

    if max(centers) - min(centers) <= max_spread:
        return sorted(formal, key=lambda cap: (cap.get("bbox") or [0, 0, 0, 0])[1])

    # Outlier-tolerant: cluster by x-center and keep the largest aligned column.
    ordered_by_x = sorted(formal, key=_center_x)
    clusters: list[list[dict]] = []

    for cap in ordered_by_x:
        cx = _center_x(cap)
        placed = False
        for cluster in clusters:
            cluster_centers = [_center_x(c) for c in cluster]
            cluster_mean = sum(cluster_centers) / len(cluster_centers)
            if abs(cx - cluster_mean) <= max_spread:
                cluster.append(cap)
                placed = True
                break
        if not placed:
            clusters.append([cap])

    viable = [c for c in clusters if len(c) >= 2]
    if not viable:
        return []

    best = max(viable, key=lambda c: (len(c), -_center_x(c[0])))

    best_centers = [_center_x(c) for c in best]
    if max(best_centers) - min(best_centers) > max_spread:
        return []

    return sorted(best, key=lambda cap: (cap.get("bbox") or [0, 0, 0, 0])[1])


def _caption_row_coupled_assets(caption: dict, assets: list[dict], *, page_width: float = 1200) -> list[dict]:
    caption_bbox = caption.get("bbox") or [0, 0, 0, 0]
    if len(caption_bbox) < 4:
        return []
    caption_height = max(1.0, float(caption_bbox[3] - caption_bbox[1]))
    coupled: list[dict] = []
    for asset in assets:
        asset_bbox = asset.get("bbox") or [0, 0, 0, 0]
        if len(asset_bbox) < 4:
            continue
        y_overlap = max(0.0, min(caption_bbox[3], asset_bbox[3]) - max(caption_bbox[1], asset_bbox[1]))
        x_gap = min(abs(asset_bbox[0] - caption_bbox[2]), abs(caption_bbox[0] - asset_bbox[2]))
        if y_overlap >= caption_height * 0.5 and x_gap <= float(page_width) * 0.25:
            coupled.append(asset)
    return coupled


def _identify_bundle_source_legend_ids(legends: list[dict], assets: list[dict]) -> set[str]:
    assets_by_page: dict[int, int] = {}
    for asset in assets:
        page = int(asset.get("page", 0) or 0)
        assets_by_page[page] = assets_by_page.get(page, 0) + 1

    legends_by_page: dict[int, list[dict]] = {}
    for legend in legends:
        if _extract_figure_number(str(legend.get("text", "") or "")) is None:
            continue
        page = int(legend.get("page", 0) or 0)
        legends_by_page.setdefault(page, []).append(legend)

    bundle_source_ids: set[str] = set()
    for page, page_legends in legends_by_page.items():
        if len(page_legends) < 3:
            continue
        if assets_by_page.get(page, 0) > 0:
            continue
        for legend in page_legends:
            bundle_source_ids.add(str(legend.get("block_id", "")))
    return bundle_source_ids


def _legend_has_same_page_asset(legend: dict, assets: list[dict]) -> bool:
    legend_page = int(legend.get("page", 0) or 0)
    return any(int(asset.get("page", 0) or 0) == legend_page for asset in assets)


def _legend_has_adjacent_page_asset(legend: dict, assets: list[dict]) -> bool:
    legend_page = int(legend.get("page", 0) or 0)
    return any(abs(int(asset.get("page", 0) or 0) - legend_page) == 1 for asset in assets)


def _strip_caption_number_prefix(text: str) -> str:
    return _FIGURE_NUMBER_PATTERN.sub("", text, count=1).strip()


def _normalized_caption_body(text: str) -> str:
    body = _strip_caption_number_prefix(text).lower().strip()
    body = re.sub(r"[.!?:;,]+$", "", body)
    body = " ".join(body.split())
    return body


def _legend_dedup_priority(legend: dict, *, bundle_source_legend_ids: set[str], assets: list[dict]) -> tuple[int, int, int, int, int]:
    legend_id = str(legend.get("block_id", ""))
    role = str(legend.get("role") or "")
    return (
        0 if legend_id in bundle_source_legend_ids else 1,
        1 if _legend_has_same_page_asset(legend, assets) else 0,
        1 if _legend_has_adjacent_page_asset(legend, assets) else 0,
        1 if _has_strong_explicit_caption_text(legend) else 0,
        1 if role == "figure_caption" else 0,
    )


def _build_composite_parent_figure_groups_visual_only(
    atomic_groups: list[dict],
    assets: list[dict],
    structured_blocks: list[dict],
    page_width: float,
) -> list[dict]:
    """Build same-page composite parent candidates from visual geometry only.

    Composite parents are sets of atomic groups on the same page that
    share strong horizontal alignment and are vertically adjacent --
    typical of multi-panel figures (2x2 grids, columnar stacks).
    Caption/legend identity is intentionally excluded from topology.
    """
    groups_by_page: dict[int, list[dict]] = {}
    for group in atomic_groups:
        groups_by_page.setdefault(int(group.get("page", 0) or 0), []).append(group)

    results: list[dict] = []

    for page, page_groups in groups_by_page.items():
        if len(page_groups) < 2:
            continue

        n = len(page_groups)

        def _x_overlap_ratio(b1: list[float], b2: list[float]) -> float:
            if len(b1) < 4 or len(b2) < 4:
                return 0.0
            x1 = max(b1[0], b2[0])
            x2 = min(b1[2], b2[2])
            overlap = max(0.0, x2 - x1)
            min_width = min(b1[2] - b1[0], b2[2] - b2[0])
            return overlap / min_width if min_width > 0 else 0.0

        def _height_similarity(b1: list[float], b2: list[float]) -> float:
            if len(b1) < 4 or len(b2) < 4:
                return 0.0
            h1 = b1[3] - b1[1]
            h2 = b2[3] - b2[1]
            if max(h1, h2) == 0:
                return 0.0
            return min(h1, h2) / max(h1, h2)

        def _width_similarity(b1: list[float], b2: list[float]) -> float:
            if len(b1) < 4 or len(b2) < 4:
                return 0.0
            w1 = b1[2] - b1[0]
            w2 = b2[2] - b2[0]
            if max(w1, w2) == 0:
                return 0.0
            return min(w1, w2) / max(w1, w2)

        # Union-Find for connected components
        parent = list(range(n))

        def find(i: int) -> int:
            while parent[i] != i:
                parent[i] = parent[parent[i]]
                i = parent[i]
            return i

        def union(i: int, j: int) -> None:
            ri, rj = find(i), find(j)
            if ri != rj:
                parent[ri] = rj

        for i in range(n):
            for j in range(i + 1, n):
                bi = page_groups[i].get("cluster_bbox") or [0, 0, 0, 0]
                bj = page_groups[j].get("cluster_bbox") or [0, 0, 0, 0]
                if len(bi) < 4 or len(bj) < 4:
                    continue
                x_overlap = _x_overlap_ratio(bi, bj)
                h_sim = _height_similarity(bi, bj)
                w_sim = _width_similarity(bi, bj)
                if x_overlap >= 0.5 and (h_sim >= 0.4 or w_sim >= 0.5):
                    union(i, j)

        # Collect components with ≥2 groups
        components: dict[int, list[int]] = {}
        for i in range(n):
            root = find(i)
            components.setdefault(root, []).append(i)

        for root, indices in components.items():
            if len(indices) < 2:
                continue
            child_groups = [page_groups[i] for i in indices]
            child_ids = [str(g.get("group_id", "")) for g in child_groups]
            all_assets = []
            for g in child_groups:
                all_assets.extend(g.get("asset_block_ids", []))
            all_bboxes = [
                b for g in child_groups
                if (b := g.get("cluster_bbox")) and len(b) >= 4
            ]
            cluster_bbox = _cluster_bbox(all_bboxes) if all_bboxes else [0, 0, 0, 0]

            # Confidence scoring
            confidence = 0.50
            if len(child_groups) >= 3:
                confidence += 0.05
            if len(child_groups) >= 4:
                confidence += 0.05
            widths = []
            heights = []
            for g in child_groups:
                cb = g.get("cluster_bbox") or [0, 0, 0, 0]
                if len(cb) >= 4:
                    widths.append(cb[2] - cb[0])
                    heights.append(cb[3] - cb[1])
            if len(widths) >= 2:
                mean_w = sum(widths) / len(widths)
                if mean_w > 0:
                    cv_w = (sum((w - mean_w) ** 2 for w in widths) / len(widths)) ** 0.5 / mean_w
                    if cv_w < 0.25:
                        confidence += 0.05
            if len(heights) >= 2:
                mean_h = sum(heights) / len(heights)
                if mean_h > 0:
                    cv_h = (sum((h - mean_h) ** 2 for h in heights) / len(heights)) ** 0.5 / mean_h
                    if cv_h < 0.25:
                        confidence += 0.05
            confidence = min(0.75, confidence)

            results.append({
                "group_id": f"composite_parent_{page:04d}_{len(results):03d}",
                "group_type": "composite_parent",
                "page": page,
                "child_group_ids": child_ids,
                "asset_block_ids": list(dict.fromkeys(all_assets)),
                "cluster_bbox": cluster_bbox,
                "parent_evidence": [
                    "same_page",
                    "horizontal_alignment",
                    f"{len(child_groups)}_child_groups",
                ],
                "parent_confidence": confidence,
                "ownership_enabled": False,
            })

    return results


def _compute_grid_score(envelopes: list[dict]) -> float:
    if len(envelopes) < 2:
        return 0.0
    bboxes = [
        cb
        for env in envelopes
        if (cb := env.get("cluster_bbox") or env.get("bbox")) and len(cb) >= 4
    ]
    if len(bboxes) < 2:
        return 0.0
    y_centers = [(b[1] + b[3]) / 2 for b in bboxes]
    mean_y = sum(y_centers) / len(y_centers)
    page_h = max(b[3] for b in bboxes) - min(b[1] for b in bboxes)
    if page_h <= 0:
        return 0.0
    y_spread = sum(abs(y - mean_y) for y in y_centers) / len(y_centers)
    row_score = max(0.0, 1.0 - y_spread / page_h)
    x_centers = [(b[0] + b[2]) / 2 for b in bboxes]
    mean_x = sum(x_centers) / len(x_centers)
    page_w = max(b[2] for b in bboxes) - min(b[0] for b in bboxes)
    if page_w <= 0:
        return 0.0
    x_spread = sum(abs(x - mean_x) for x in x_centers) / len(x_centers)
    col_score = max(0.0, 1.0 - x_spread / page_w)
    return min(1.0, (row_score + col_score) / 2)


def _build_dense_composite_parent_candidates(
    atomic_groups: list[dict],
    unresolved_clusters: list[dict],
    numbered_legend_pages: set[int],
    page_width: float,
) -> list[dict]:
    results: list[dict] = []
    groups_by_page: dict[int, list[dict]] = {}
    for group in atomic_groups:
        page = int(group.get("page", 0) or 0)
        if page in numbered_legend_pages:
            groups_by_page.setdefault(page, []).append(group)

    uc_by_page: dict[int, list[dict]] = {}
    for uc in unresolved_clusters:
        page = int(uc.get("page", 0) or 0)
        if page in numbered_legend_pages:
            uc_by_page.setdefault(page, []).append(uc)

    for page in numbered_legend_pages:
        page_groups = groups_by_page.get(page, [])
        page_ucs = uc_by_page.get(page, [])

        total_asset_ids: set[str] = set()
        for g in page_groups:
            for bid in g.get("asset_block_ids", []):
                if bid:
                    total_asset_ids.add(str(bid))
        for uc in page_ucs:
            for bid in uc.get("media_block_ids", []):
                if bid:
                    total_asset_ids.add(str(bid))

        fragment_count = len(total_asset_ids)
        if fragment_count < 4:
            continue

        envelopes: list[dict] = []
        for g in page_groups:
            cb = g.get("cluster_bbox")
            if cb and len(cb) >= 4:
                envelopes.append({"cluster_bbox": cb})
        for uc in page_ucs:
            cb = uc.get("cluster_bbox")
            if cb and len(cb) >= 4:
                envelopes.append({"cluster_bbox": cb})

        if not envelopes:
            continue

        cluster_bbox = _cluster_bbox([e["cluster_bbox"] for e in envelopes])
        page_area = page_width * 1600.0
        envelope_area = (
            (cluster_bbox[2] - cluster_bbox[0]) * (cluster_bbox[3] - cluster_bbox[1])
            if len(cluster_bbox) >= 4
            else 0.0
        )
        compactness = min(1.0, envelope_area / max(1.0, page_area))

        grid_score = _compute_grid_score(envelopes)

        child_group_ids = [str(g.get("group_id", "")) for g in page_groups if g.get("group_id")]
        unresolved_cluster_ids = [
            str(uc.get("cluster_id", "")) for uc in page_ucs if uc.get("cluster_id")
        ]
        all_asset_ids: list[str] = list(dict.fromkeys(
            bid for g in page_groups for bid in g.get("asset_block_ids", []) if bid is not None
        ))

        reason = ["dense_fragment_page", f"{fragment_count}_visual_fragments"]
        if page_ucs:
            reason.append(f"{len(page_ucs)}_unresolved_clusters")

        results.append({
            "group_id": f"dense_composite_parent_{page:04d}_{len(results):03d}",
            "group_type": "composite_parent",
            "parent_subtype": "dense_composite",
            "page": page,
            "child_group_ids": child_group_ids,
            "unresolved_cluster_ids": unresolved_cluster_ids,
            "asset_block_ids": all_asset_ids,
            "embedded_text_block_ids": [],
            "cluster_bbox": cluster_bbox,
            "parent_evidence": reason,
            "parent_confidence": round(grid_score, 4),
            "fragment_count": fragment_count,
            "atomic_child_count": len(page_groups),
            "unresolved_child_count": len(page_ucs),
            "visual_mass": float(len(all_asset_ids)),
            "compactness": round(compactness, 4),
            "grid_score": round(grid_score, 4),
            "construction_reason": reason,
            "crosses_caption_boundary": False,
            "ownership_enabled": False,
        })

    return results


def _score_dense_parent_candidate_against_local_ownership(
    parent: dict,
    *,
    owned_asset_ids: set[str],
    unresolved_asset_ids: set[str],
) -> dict:
    asset_block_ids = {str(bid) for bid in parent.get("asset_block_ids", [])}
    unresolved_absorbed = asset_block_ids & unresolved_asset_ids
    already_owned = asset_block_ids & owned_asset_ids
    total_asset_count = max(1, len(asset_block_ids))
    coverage_gain = len(unresolved_absorbed)
    leftover_mass_absorbed = len(unresolved_absorbed)
    unresolved_reduction_ratio = round(len(unresolved_absorbed) / total_asset_count, 4)
    return {
        "coverage_gain": coverage_gain,
        "leftover_mass_absorbed": leftover_mass_absorbed,
        "unresolved_reduction_ratio": unresolved_reduction_ratio,
        "already_owned_count": len(already_owned),
        "total_parent_asset_count": total_asset_count,
    }


def _should_suppress_panel_title_candidate(
    block: dict,
    *,
    page_has_numbered_legend: bool,
    visual_envelopes: list[dict],
    page_width: float,
) -> bool:
    """Structurally demote short unnumbered text that looks like a caption
    but is actually a panel title inside a figure visual envelope.

    Signals (all required):
    1. no figure number
    2. short text span (<= 50 chars)
    3. inside a likely visual parent envelope (overlaps a candidate_group
       or composite_parent bbox on the same page)
    4. page already has a strong numbered figure caption
    5. title-like geometry (narrow block or small text relative to page)

    No literal string blacklist.
    """
    if not page_has_numbered_legend:
        return False
    text = str(block.get("text") or "").strip()
    if _extract_figure_number(text) is not None:
        return False
    if len(text) > 50:
        return False
    if len(text.split()) > 4:
        return False
    bbox = block.get("bbox") or block.get("block_bbox") or [0, 0, 0, 0]
    if len(bbox) < 4:
        return False
    block_width = bbox[2] - bbox[0]
    block_page = int(block.get("page", 0) or 0)
    page_envelopes = [
        env
        for env in visual_envelopes
        if int(env.get("page", 0) or 0) == block_page
    ]
    inside_envelope = False
    for env in page_envelopes:
        eb = env.get("cluster_bbox") or env.get("bbox") or [0, 0, 0, 0]
        if len(eb) < 4:
            continue
        x_overlap = bbox[0] < eb[2] and bbox[2] > eb[0]
        y_overlap = bbox[1] < eb[3] and bbox[3] > eb[1]
        x_near = max(0.0, eb[0] - bbox[2], bbox[0] - eb[2]) <= page_width * 0.3
        y_near = max(0.0, eb[1] - bbox[3], bbox[1] - eb[3]) <= 80
        if (x_overlap or x_near) and (y_overlap or y_near):
            inside_envelope = True
            break
    if not inside_envelope:
        return False
    if block_width > page_width * 0.7:
        return False
    return True


def _asset_vertical_side(legend: dict, group: dict) -> str:
    legend_bbox = legend.get("bbox") or legend.get("block_bbox") or [0, 0, 0, 0]
    cluster_bbox = group.get("cluster_bbox") or [0, 0, 0, 0]
    if len(legend_bbox) < 4 or len(cluster_bbox) < 4:
        return "overlap"
    if cluster_bbox[3] <= legend_bbox[1]:
        return "above"
    if cluster_bbox[1] >= legend_bbox[3]:
        return "below"
    return "overlap"


def _allow_previous_page_sequential_match(cap: dict, asset: dict) -> bool:
    cp = int(cap.get("page", 0) or 0)
    ap = int(asset.get("page", 0) or 0)
    if ap != cp - 1:
        return False
    cap_bbox = cap.get("bbox") or [0, 0, 0, 0]
    asset_bbox = asset.get("bbox") or [0, 0, 0, 0]
    if len(cap_bbox) < 4 or len(asset_bbox) < 4:
        return False
    cap_top = cap_bbox[1]
    cap_width = cap_bbox[2] - cap_bbox[0]
    asset_bottom = asset_bbox[3]
    asset_width = asset_bbox[2] - asset_bbox[0]
    page_width = float(cap.get("page_width") or asset.get("page_width") or 1200)
    page_height = float(cap.get("page_height") or asset.get("page_height") or 1600)
    strong_numbered_caption = _extract_figure_number(str(cap.get("text", ""))) is not None
    post_reference_layout = str(cap.get("zone") or "") in {"post_reference_backmatter_zone", "display_zone"}
    starts_page = cap_top <= 220
    prior_asset_near_bottom = asset_bottom >= page_height * 0.7
    wide_caption = cap_width >= page_width * 0.6
    substantial_asset = asset_width >= page_width * 0.2
    return (
        strong_numbered_caption
        and post_reference_layout
        and starts_page
        and prior_asset_near_bottom
        and wide_caption
        and substantial_asset
    )


def _partition_assets_by_caption_bands(
    page_captions: list[dict], page_assets: list[dict], page_height: float
) -> dict[str, list[dict]]:
    if len(page_captions) < 2:
        return {}

    ordered = sorted(page_captions, key=lambda cap: (cap.get("bbox") or [0, 0, 0, 0])[1])
    result: dict[str, list[dict]] = {str(cap.get("block_id", "")): [] for cap in ordered}

    cap_bboxes = [(cap.get("bbox") or [0, 0, 0, 0]) for cap in ordered]
    side_by_side = all(cap_bboxes[i][2] <= cap_bboxes[i + 1][0] for i in range(len(cap_bboxes) - 1))

    if side_by_side:
        for asset in page_assets:
            bbox = asset.get("bbox") or [0, 0, 0, 0]
            ax = (bbox[0] + bbox[2]) / 2
            best_idx = min(
                range(len(ordered)),
                key=lambda idx: abs(ax - (cap_bboxes[idx][0] + cap_bboxes[idx][2]) / 2),
            )
            result[str(ordered[best_idx].get("block_id", ""))].append(asset)
        return result

    caption_tops = [cb[1] for cb in cap_bboxes]
    asset_centers = [
        (((asset.get("bbox") or [0, 0, 0, 0])[1] + (asset.get("bbox") or [0, 0, 0, 0])[3]) / 2) for asset in page_assets
    ]
    captions_precede_assets = bool(asset_centers) and min(caption_tops) <= min(asset_centers)

    boundaries: list[float] = [0.0]
    for upper, lower in zip(ordered, ordered[1:]):
        ub = upper.get("bbox") or [0, 0, 0, 0]
        lb = lower.get("bbox") or [0, 0, 0, 0]
        boundaries.append(((ub[1] + ub[3]) / 2 + (lb[1] + lb[3]) / 2) / 2)
    boundaries.append(float(page_height))

    for asset in page_assets:
        bbox = asset.get("bbox") or [0, 0, 0, 0]
        cy = (bbox[1] + bbox[3]) / 2 if len(bbox) >= 4 else 0
        if captions_precede_assets:
            for idx, cap in enumerate(ordered):
                if boundaries[idx] <= cy < boundaries[idx + 1]:
                    result[str(cap.get("block_id", ""))].append(asset)
                    break
        else:
            assigned = False
            for cap in ordered:
                cap_bbox = cap.get("bbox") or [0, 0, 0, 0]
                cap_top = cap_bbox[1] if len(cap_bbox) >= 4 else float(page_height)
                if cy <= cap_top:
                    result[str(cap.get("block_id", ""))].append(asset)
                    assigned = True
                    break
            if not assigned:
                result[str(ordered[-1].get("block_id", ""))].append(asset)
    return result


# --- Cross-page figure matching helpers (Stage 1) ---


def _is_strong_numbered_legend(
    block: dict,
    *,
    caption_score: dict | None = None,
    anchor_supported: bool | None = None,
    caption_text_supported: bool | None = None,
) -> bool:
    if _extract_figure_number(str(block.get("text") or "")) is None:
        return False
    if not _is_formal_legend(block.get("text", ""), block):
        return False
    if _is_insufficient_legend_evidence(block):
        return False
    cs = (
        caption_score
        if caption_score is not None
        else score_figure_caption(
            block,
            nearby_media=False,
            caption_style_match=False,
            body_prose_likelihood=_looks_like_inline_figure_mention(block.get("text", ""), block),
        )
    )
    if cs.get("score", 0) < 0.4:
        return False
    if _is_validation_first_legend_candidate(block):
        a_s = anchor_supported if anchor_supported is not None else _has_anchor_supported_legend_context(block)
        c_s = caption_text_supported if caption_text_supported is not None else _has_strong_explicit_caption_text(block)
        if not a_s and not c_s:
            return False
    return True


def _is_structurally_matchable_group(group: dict, *, competing_caption_pages: set[int]) -> bool:
    if not group.get("asset_block_ids"):
        return False
    if group.get("_non_body_media"):
        return False
    cb = group.get("cluster_bbox")
    if not cb or len(cb) < 4:
        return False
    if group.get("group_type") == "page_assets" and group.get("page") in competing_caption_pages:
        return False
    return True


def _is_unowned_matchable_group(
    group: dict,
    *,
    competing_caption_pages: set[int],
    used_group_ids: set[str],
    used_asset_page_ids: set[tuple],
) -> bool:
    if not _is_structurally_matchable_group(group, competing_caption_pages=competing_caption_pages):
        return False
    if str(group.get("group_id", "")) in used_group_ids:
        return False
    g_page = group.get("page", 0)
    for bid in group.get("asset_block_ids", []):
        if bid and (g_page, bid) in used_asset_page_ids:
            return False
    return True


def _build_page_ledger(legends: list[dict], candidate_groups: list[dict]) -> dict[int, dict]:
    ledger: dict[int, dict] = {}
    for leg in legends:
        p = int(leg.get("page", 0) or 0)
        if p not in ledger:
            ledger[p] = {
                "page": p,
                "legend_count": 0,
                "numbered_legend_count": 0,
                "group_count": 0,
                "top_legend_count": 0,
                "bottom_legend_count": 0,
                "delta": 0,
            }
        ledger[p]["legend_count"] += 1
        if _extract_figure_number(str(leg.get("text") or "")) is not None:
            ledger[p]["numbered_legend_count"] += 1
    for g in candidate_groups:
        p = int(g.get("page", 0) or 0)
        if p not in ledger:
            ledger[p] = {
                "page": p,
                "legend_count": 0,
                "numbered_legend_count": 0,
                "group_count": 0,
                "top_legend_count": 0,
                "bottom_legend_count": 0,
                "delta": 0,
            }
        ledger[p]["group_count"] += 1
    for p in ledger:
        ledger[p]["delta"] = ledger[p]["legend_count"] - ledger[p]["group_count"]
    return ledger


def _build_residual_ledger(
    legends: list[dict],
    candidate_groups: list[dict],
    *,
    competing_caption_pages: set[int],
) -> dict[int, dict]:
    ledger: dict[int, dict] = {}
    for leg in legends:
        p = int(leg.get("page", 0) or 0)
        if not _is_strong_numbered_legend(leg):
            continue
        if p not in ledger:
            ledger[p] = {
                "page": p,
                "unmatched_strong_legend_count": 0,
                "unmatched_matchable_group_count": 0,
                "residual_delta": 0,
            }
        ledger[p]["unmatched_strong_legend_count"] += 1
    for g in candidate_groups:
        p = int(g.get("page", 0) or 0)
        if not _is_structurally_matchable_group(g, competing_caption_pages=competing_caption_pages):
            continue
        if p not in ledger:
            ledger[p] = {
                "page": p,
                "unmatched_strong_legend_count": 0,
                "unmatched_matchable_group_count": 0,
                "residual_delta": 0,
            }
        ledger[p]["unmatched_matchable_group_count"] += 1
    for p in ledger:
        ledger[p]["residual_delta"] = (
            ledger[p]["unmatched_strong_legend_count"] - ledger[p]["unmatched_matchable_group_count"]
        )
    return ledger


def _residual_group_surplus(pages: list[int], residual_ledger: dict[int, dict]) -> int:
    return sum(
        max(
            0,
            residual_ledger[p]["unmatched_matchable_group_count"] - residual_ledger[p]["unmatched_strong_legend_count"],
        )
        for p in pages
        if p in residual_ledger
    )


def _residual_legend_surplus(pages: list[int], residual_ledger: dict[int, dict]) -> int:
    return sum(
        max(
            0,
            residual_ledger[p]["unmatched_strong_legend_count"] - residual_ledger[p]["unmatched_matchable_group_count"],
        )
        for p in pages
        if p in residual_ledger
    )


def _grouped_asset_page_ids(candidate_groups: list[dict]) -> set[tuple[int, str]]:
    return {
        (int(group.get("page", 0) or 0), str(bid))
        for group in candidate_groups
        for bid in group.get("asset_block_ids", [])
        if bid is not None
    }


def _recompute_final_unmatched_assets(
    assets: list[dict],
    used_asset_page_ids: set[tuple],
    unresolved_clusters: list[dict],
) -> list[dict]:
    unresolved_asset_page_ids: set[tuple[int, str]] = set()
    for cluster in unresolved_clusters:
        page = int(cluster.get("page", 0) or 0)
        for bid in cluster.get("media_block_ids", []):
            if bid is not None:
                unresolved_asset_page_ids.add((page, str(bid)))

    final_unmatched: list[dict] = []
    for asset in assets:
        page = int(asset.get("page", 0) or 0)
        bid = asset.get("block_id", "")
        if not bid:
            final_unmatched.append(asset)
            continue
        page_bid = (page, str(bid))
        if page_bid in used_asset_page_ids or page_bid in unresolved_asset_page_ids:
            continue
        final_unmatched.append(asset)
    return final_unmatched


def _reserve_cross_page_objects(
    legends: list[dict],
    candidate_groups: list[dict],
    residual_ledger: dict[int, dict],
    *,
    competing_caption_pages: set[int],
    sidecar_pages: set[int],
    used_group_ids: set[str] | None = None,
    used_asset_page_ids: set[tuple] | None = None,
) -> tuple[set[str], set[str]]:
    reserved_legend_ids: set[tuple[int, str]] = set()
    reserved_group_ids: set[str] = set()

    legends_by_page: dict[int, list[dict]] = {}
    for leg in legends:
        p = int(leg.get("page", 0) or 0)
        legends_by_page.setdefault(p, []).append(leg)

    groups_by_page: dict[int, list[dict]] = {}
    for g in candidate_groups:
        p = int(g.get("page", 0) or 0)
        groups_by_page.setdefault(p, []).append(g)

    used_gids = used_group_ids or set()
    used_aids = used_asset_page_ids or set()

    for page, entry in residual_ledger.items():
        if page in sidecar_pages:
            continue
        if entry["residual_delta"] > 0:
            k = min(entry["residual_delta"], _residual_group_surplus([page - 1, page - 2], residual_ledger))
            if k <= 0:
                continue
            page_legends = sorted(
                [l for l in legends_by_page.get(page, []) if _is_strong_numbered_legend(l)],
                key=lambda l: (l.get("bbox") or [0, 0, 0, 0])[1],
            )
            for leg in page_legends[:k]:
                reserved_legend_ids.add((int(leg.get("page", 0) or 0), str(leg.get("block_id", ""))))
        if entry["residual_delta"] < 0:
            k = min(-entry["residual_delta"], _residual_legend_surplus([page + 1, page + 2], residual_ledger))
            if k <= 0:
                continue
            page_groups = sorted(
                [
                    g
                    for g in groups_by_page.get(page, [])
                    if _is_unowned_matchable_group(
                        g,
                        competing_caption_pages=competing_caption_pages,
                        used_group_ids=used_gids,
                        used_asset_page_ids=used_aids,
                    )
                ],
                key=lambda g: (g.get("cluster_bbox") or [0, 0, 0, 0])[3],
                reverse=True,
            )
            for grp in page_groups[:k]:
                reserved_group_ids.add(str(grp.get("group_id", "")))

    return reserved_legend_ids, reserved_group_ids


def _reserved_legend_block_ids(reserved_legend_ids: set[tuple[int, str]]) -> set[str]:
    return {block_id for _, block_id in reserved_legend_ids}


INTERRUPTION_ROLES = {
    "section_heading",
    "subsection_heading",
    "sub_subsection_heading",
    "table_caption",
    "table_html",
    "reference_heading",
    "reference_item",
    "backmatter_heading",
    "backmatter_body",
}


def _has_strong_interruption(page: int, structured_blocks: list[dict]) -> bool:
    body_count = sum(
        1 for b in structured_blocks if int(b.get("page", 0) or 0) == page and b.get("role") == "body_paragraph"
    )
    if body_count >= 3:
        return True
    return any(
        int(b.get("page", 0) or 0) == page and b.get("role", "") in INTERRUPTION_ROLES for b in structured_blocks
    )


def _settle_cross_page_reserved_objects(
    reserved_legend_ids: set[tuple[int, str]],
    reserved_group_ids: set[str],
    legends: list[dict],
    candidate_groups: list[dict],
    structured_blocks: list[dict],
    matched_figures: list[dict],
    ambiguous_figures: list[dict],
    unmatched_legends: list[dict],
    used_group_ids: set[str],
    used_asset_page_ids: set[tuple],
    ownership: FigureOwnershipRegistry,
    *,
    residual_ledger: dict[int, dict],
    competing_caption_pages: set[int],
    page_width: float = 1200,
) -> None:
    legends_by_id = {
        (int(leg.get("page", 0) or 0), str(leg.get("block_id", ""))): leg
        for leg in legends
    }
    groups_by_id = {str(g.get("group_id", "")): g for g in candidate_groups}

    failed_legend_ids: set[tuple[int, str]] = set()
    failed_group_ids: set[str] = set()

    # reserved legends look backward
    for lid in sorted(reserved_legend_ids):
        legend = legends_by_id.get(lid)
        if legend is None:
            continue
        leg_page = int(legend.get("page", 0) or 0)
        for pp in (leg_page - 1, leg_page - 2):
            if pp < 0:
                continue
            if pp == leg_page - 2 and _has_strong_interruption(leg_page - 1, structured_blocks):
                continue
            target_groups = sorted(
                [
                    g
                    for g in candidate_groups
                    if str(g.get("group_id", "")) not in used_group_ids and g.get("page") == pp
                ],
                key=lambda g: (g.get("cluster_bbox") or [0, 0, 0, 0])[3],
                reverse=True,
            )
            if not target_groups:
                continue
            best_group = target_groups[0]
            came_from_reserved = str(best_group.get("group_id", "")) in reserved_group_ids

            legend_text = str(legend.get("text") or "")
            fn = _extract_figure_number(legend_text)
            if fn is None:
                continue
            ns = _extract_figure_namespace(legend_text)
            fig_id = _format_figure_id(ns, fn)

            caption_score = score_figure_caption(
                legend, nearby_media=True, caption_style_match=False, body_prose_likelihood=False
            )

            group_page = int(best_group.get("page", 0) or 0)
            group_assets = [g for g in best_group.get("media_blocks", [])]

            ownership.match_group(best_group, owner_id=f"{lid[0]}:{lid[1]}", owner_family="figure")

            asset_pages = sorted({int(a.get("page", 0) or 0) for a in group_assets})
            matched_figures.append(
                {
                    "figure_id": fig_id,
                    "figure_namespace": ns,
                    "legend_block_id": legend.get("block_id", ""),
                    "page": group_page,
                    "legend_page": leg_page,
                    "asset_pages": asset_pages if asset_pages else [group_page],
                    "text": legend_text,
                    "figure_number": fn,
                    "matched_assets": [
                        {"block_id": a.get("block_id", ""), "bbox": a.get("bbox", [0, 0, 0, 0])} for a in group_assets
                    ],
                    "asset_block_ids": [str(a.get("block_id", "")) for a in group_assets if a.get("block_id")],
                    "bridge_block_ids": [],
                    "group_type": best_group.get("group_type", ""),
                    "group_evidence": best_group.get("group_evidence", []) + ["cross_page_backward"],
                    "cluster_bbox": best_group.get("cluster_bbox", [0, 0, 0, 0]),
                    "confidence": 0.5,
                    "match_score": {"score": 0.5, "decision": "matched", "evidence": ["cross_page_backward"]},
                    "flags": ["cross_page_match"],
                    "caption_score": caption_score,
                    "settlement_type": "cross_page_backward",
                }
            )
            unmatched_legends[:] = [
                u for u in unmatched_legends
                if (int(u.get("page", 0) or 0), str(u.get("block_id", ""))) != lid
            ]
            break
        else:
            failed_legend_ids.add(lid)

    # Build set of already-matched legend IDs to prevent double-matching
    _used_legend_ids: set[str] = {
        str(m.get("legend_block_id", "")) for m in matched_figures if m.get("legend_block_id")
    }

    # reserved groups look forward
    for gid in sorted(reserved_group_ids):
        if gid in used_group_ids:
            continue
        group = groups_by_id.get(gid)
        if group is None:
            continue
        group_page = int(group.get("page", 0) or 0)
        for np in (group_page + 1, group_page + 2):
            if np == group_page + 2 and _has_strong_interruption(group_page + 1, structured_blocks):
                continue
            candidate_legends = sorted(
                [
                    l
                    for l in legends
                    if str(l.get("block_id", "")) not in _used_legend_ids
                    and (int(l.get("page", 0) or 0), str(l.get("block_id", ""))) not in failed_legend_ids
                    and int(l.get("page", 0) or 0) == np
                    and _is_strong_numbered_legend(l)
                ],
                key=lambda l: (l.get("bbox") or [0, 0, 0, 0])[1],
            )
            if not candidate_legends:
                continue
            best_legend = candidate_legends[0]
            lid = str(best_legend.get("block_id", ""))

            legend_text = str(best_legend.get("text") or "")
            fn = _extract_figure_number(legend_text)
            if fn is None:
                continue
            ns = _extract_figure_namespace(legend_text)
            fig_id = _format_figure_id(ns, fn)

            caption_score = score_figure_caption(
                best_legend, nearby_media=True, caption_style_match=False, body_prose_likelihood=False
            )

            group_assets = [g for g in group.get("media_blocks", [])]
            ownership.match_group(group, owner_id=lid, owner_family="figure")

            asset_pages = sorted({int(a.get("page", 0) or 0) for a in group_assets})
            matched_figures.append(
                {
                    "figure_id": fig_id,
                    "figure_namespace": ns,
                    "legend_block_id": best_legend.get("block_id", ""),
                    "page": group_page,
                    "legend_page": np,
                    "asset_pages": asset_pages if asset_pages else [group_page],
                    "text": legend_text,
                    "figure_number": fn,
                    "matched_assets": [
                        {"block_id": a.get("block_id", ""), "bbox": a.get("bbox", [0, 0, 0, 0])} for a in group_assets
                    ],
                    "asset_block_ids": [str(a.get("block_id", "")) for a in group_assets if a.get("block_id")],
                    "bridge_block_ids": [],
                    "group_type": group.get("group_type", ""),
                    "group_evidence": group.get("group_evidence", []) + ["cross_page_forward"],
                    "cluster_bbox": group.get("cluster_bbox", [0, 0, 0, 0]),
                    "confidence": 0.5,
                    "match_score": {"score": 0.5, "decision": "matched", "evidence": ["cross_page_forward"]},
                    "flags": ["cross_page_match"],
                    "caption_score": caption_score,
                    "settlement_type": "cross_page_forward",
                }
            )
            unmatched_legends[:] = [u for u in unmatched_legends if str(u.get("block_id", "")) != lid]
            break
        else:
            failed_group_ids.add(gid)

    # Failed reserved legends
    for lid in failed_legend_ids:
        legend = legends_by_id.get(lid)
        if legend is None:
            continue
        ambiguous_figures.append(
            {
                "legend_block_id": lid[1],
                "page": legend.get("page", 0),
                "text": legend.get("text", ""),
                "figure_number": _extract_figure_number(str(legend.get("text", "") or "")),
                "hold_reason": "reserved_cross_page_no_valid_group",
            }
        )


def _resolve_figure_id_collisions(figure_inventory: dict) -> None:
    _collision_seen: dict[str, int] = {}
    for _fig in (
        *figure_inventory.get("matched_figures", []),
        *figure_inventory.get("held_figures", []),
        *figure_inventory.get("ambiguous_figures", []),
    ):
        _fig_id = _fig.get("figure_id", "")
        if not _fig_id:
            continue
        if _fig_id in _collision_seen:
            _collision_seen[_fig_id] += 1
            _fig["figure_id"] = f"figure_{'s' * _collision_seen[_fig_id]}{_fig_id.removeprefix('figure_')}"
        else:
            _collision_seen[_fig_id] = 0


def _coerce_int_figure_number(value) -> int | None:
    if isinstance(value, bool):
        return None
    if isinstance(value, int):
        return value
    if isinstance(value, float) and value.is_integer():
        return int(value)
    if isinstance(value, str) and value.strip().isdigit():
        return int(value.strip())
    return None


def _resolve_legend_bbox(
    matched_item: dict,
    structured_blocks: list[dict],
    inventory: dict,
) -> list[float] | None:
    legend_bbox = matched_item.get("legend_bbox")
    if legend_bbox:
        return legend_bbox
    legend_block_id = str(matched_item.get("legend_block_id") or "")
    if legend_block_id:
        legend_page = matched_item.get("legend_page")
        for block in structured_blocks:
            if str(block.get("block_id") or "") == legend_block_id and block.get("page") == legend_page:
                return block.get("bbox")
        for entry in inventory.get("figure_legends", []):
            if str(entry.get("block_id") or "") == legend_block_id:
                return entry.get("bbox")
    return None


def _infer_missing_main_figure_numbers(
    inventory: dict,
    structured_blocks: list[dict],
) -> dict:
    matched = inventory.get("matched_figures", [])
    known_set: set[int] = set()
    for item in matched:
        num = _coerce_int_figure_number(item.get("figure_number"))
        if num is None:
            continue
        marker = _extract_figure_marker(str(item.get("text", "")))
        if marker["namespace"] != "main" or marker["has_s_prefix"]:
            continue
        if num in known_set:
            inventory["figure_number_inference"] = {
                "status": "skipped",
                "method": "leading_gap",
                "reason": "duplicate_known_main_numbers",
                "eligible_unknown_count": 0,
                "known_main_numbers": [],
                "inferred_figure_number": None,
            }
            return inventory
        known_set.add(num)

    eligible: list[tuple[dict, list[float]]] = []
    for item in matched:
        num = _coerce_int_figure_number(item.get("figure_number"))
        if num is not None:
            continue
        marker = _extract_figure_marker(str(item.get("text", "")))
        if marker["namespace"] != "main" or marker["has_s_prefix"]:
            continue
        legend_block_id = str(item.get("legend_block_id") or "")
        if not legend_block_id.strip():
            continue
        asset_block_ids = item.get("asset_block_ids", [])
        if not isinstance(asset_block_ids, list) or not asset_block_ids:
            continue
        settlement_type = item.get("settlement_type", "")
        if settlement_type not in {"same_page", "group_sequential", "cross_page_forward",
                                   "cross_page_backward", "composite_parent"}:
            continue
        item_text = str(item.get("text", ""))
        legend_text = ""
        for entry in inventory.get("figure_legends", []):
            if str(entry.get("block_id", "")) == str(legend_block_id):
                legend_text = str(entry.get("text", ""))
                break
        combined = item_text + " " + legend_text
        if _has_frontmatter_visual_veto(combined):
            continue
        legend_bbox = _resolve_legend_bbox(item, structured_blocks, inventory)
        if legend_bbox is None:
            continue
        eligible.append((item, legend_bbox))

    if not eligible:
        inventory["figure_number_inference"] = {
            "status": "skipped",
            "method": "leading_gap",
            "reason": "no_eligible_unknowns",
            "eligible_unknown_count": 0,
            "known_main_numbers": sorted(known_set),
            "inferred_figure_number": None,
        }
        return inventory

    if not known_set or min(known_set) != 2:
        inventory["figure_number_inference"] = {
            "status": "skipped",
            "method": "leading_gap",
            "reason": "known_min_not_2",
            "eligible_unknown_count": len(eligible),
            "known_main_numbers": sorted(known_set),
            "inferred_figure_number": None,
        }
        return inventory

    if len(eligible) != 1:
        inventory["figure_number_inference"] = {
            "status": "skipped",
            "method": "leading_gap",
            "reason": "multiple_eligible_unknowns",
            "eligible_unknown_count": len(eligible),
            "known_main_numbers": sorted(known_set),
            "inferred_figure_number": None,
        }
        return inventory

    unknown_item, unknown_bbox = eligible[0]
    first_known = None
    for item in matched:
        num = _coerce_int_figure_number(item.get("figure_number"))
        if num == min(known_set):
            first_known = item
            break
    if first_known is None:
        inventory["figure_number_inference"] = {
            "status": "skipped",
            "method": "leading_gap",
            "reason": "first_known_not_found",
            "eligible_unknown_count": len(eligible),
            "known_main_numbers": sorted(known_set),
            "inferred_figure_number": None,
        }
        return inventory

    def _fig_order_key(_item, _bbox):
        return (
            min(_item.get("asset_pages") or [_item.get("page", 1)]),
            _item.get("legend_page") or _item.get("page", 1),
            _bbox[1],
            _bbox[0],
        )

    first_known_bbox = _resolve_legend_bbox(first_known, structured_blocks, inventory)
    if first_known_bbox is None:
        inventory["figure_number_inference"] = {
            "status": "skipped",
            "method": "leading_gap",
            "reason": "first_known_bbox_unresolvable",
            "eligible_unknown_count": len(eligible),
            "known_main_numbers": sorted(known_set),
            "inferred_figure_number": None,
        }
        return inventory

    unknown_key = _fig_order_key(unknown_item, unknown_bbox)
    first_known_key = _fig_order_key(first_known, first_known_bbox)
    if unknown_key >= first_known_key:
        inventory["figure_number_inference"] = {
            "status": "skipped",
            "method": "leading_gap",
            "reason": "unknown_not_before_first_known",
            "eligible_unknown_count": len(eligible),
            "known_main_numbers": sorted(known_set),
            "inferred_figure_number": None,
        }
        return inventory

    all_items: list[dict] = list(matched) + inventory.get("held_figures", []) + inventory.get("ambiguous_figures", [])
    for noise in all_items:
        if noise is unknown_item or noise is first_known:
            continue
        noise_bbox = _resolve_legend_bbox(noise, structured_blocks, inventory)
        if noise_bbox is None:
            inventory["figure_number_inference"] = {
                "status": "skipped",
                "method": "leading_gap",
                "reason": "intervening_unknown_unorderable",
                "eligible_unknown_count": len(eligible),
                "known_main_numbers": sorted(known_set),
                "inferred_figure_number": None,
            }
            return inventory
        noise_key = _fig_order_key(noise, noise_bbox)
        if unknown_key < noise_key < first_known_key:
            inventory["figure_number_inference"] = {
                "status": "skipped",
                "method": "leading_gap",
                "reason": "intervening_items_between",
                "eligible_unknown_count": len(eligible),
                "known_main_numbers": sorted(known_set),
                "inferred_figure_number": None,
            }
            return inventory

    unknown_item["figure_number"] = 1
    unknown_item["figure_id"] = _format_figure_id("main", 1)
    unknown_item["figure_namespace"] = "main"
    unknown_item["number_inference"] = {
        "status": "accepted",
        "method": "leading_gap",
        "inferred_number": 1,
        "known_numbers": sorted(known_set),
    }
    for legend in inventory.get("figure_legends", []):
        if str(legend.get("block_id", "")) == str(unknown_item.get("legend_block_id", "")):
            legend["inferred_figure_number"] = 1
            legend["figure_number_source"] = "sequence_gap_inference"
            break

    inventory["figure_number_inference"] = {
        "status": "accepted",
        "method": "leading_gap",
        "reason": "accepted",
        "eligible_unknown_count": len(eligible),
        "known_main_numbers": sorted(known_set),
        "inferred_figure_number": 1,
    }
    return inventory


def build_figure_inventory(structured_blocks: list[dict], page_width: float = 1200) -> dict[str, Any]:
    legends: list[dict] = []
    held_figures: list[dict] = []
    rejected_legends: list[dict] = []
    assets: list[dict] = []
    unmatched_legends: list[dict] = []
    unmatched_assets: list[dict] = []
    matched_figures: list[dict] = []
    unresolved_clusters: list[dict] = []
    ambiguous_figures: list[dict] = []
    local_pairing_hypotheses: list[dict] = []
    used_group_ids: set[str] = set()
    used_asset_page_ids: set[tuple[int, str]] = set()
    ownership = FigureOwnershipRegistry(used_group_ids=used_group_ids, used_asset_page_ids=used_asset_page_ids)

    def _collect_bridge_blocks(page: int) -> list[dict]:
        bridges: list[dict] = []
        for block in structured_blocks:
            if int(block.get("page", 0) or 0) != page:
                continue
            if not block.get("bridge_eligible"):
                continue
            if str(block.get("layout_region") or "") != "display_zone":
                continue
            bridges.append(block)
        return bridges

    for block in structured_blocks:
        if block.get("page_width"):
            page_width = float(block["page_width"])

    for block in structured_blocks:
        role = block.get("role", "")
        if block.get("_non_body_media") or role == "non_body_insert":
            continue
        # Skip single-letter panel labels (A, B, (C), A.) in figure legends
        if _PANEL_LABEL_PATTERN.match(str(block.get("text", "")).strip()):
            continue
        # Skip body_paragraph that was demoted from figure_caption_candidate —
        # candidate resolution already judged it as narrative prose, not a legend.
        # Re-activating it via geometry/style signals causes body text to leak
        # into legend matching (e.g. YGH7VEX6 Figure 11).
        if role == "body_paragraph" and str(block.get("seed_role") or "") in {
            "figure_caption_candidate", "figure_caption"
        }:
            block["_rejected_legend"] = {
                "reason": "demoted_body_paragraph",
                "role": role,
                "seed_role": str(block.get("seed_role") or ""),
            }
            rejected_legends.append(block)
            continue
        is_validation_first_candidate = _is_validation_first_legend_candidate(block)
        if role in ("figure_caption", "figure_caption_candidate") or is_validation_first_candidate:
            if role == "figure_caption_candidate" and (
                str(block.get("zone") or "") == "body_zone"
                or (
                    str(block.get("zone") or "") != "display_zone"
                    and str(block.get("style_family") or "") != "legend_like"
                    and _looks_like_figure_narrative_prose(block.get("text", ""))
                    and _extract_figure_number(block.get("text", "")) is None
                )
            ):
                continue
            if not _is_formal_legend(block.get("text", ""), block, page_width):
                block["caption_score"] = score_figure_caption(
                    block,
                    nearby_media=False,
                    caption_style_match=False,
                    body_prose_likelihood=_looks_like_inline_figure_mention(block.get("text", ""), block),
                )
                rejected_legends.append(block)
            else:
                legends.append(block)
        elif role == "figure_asset":
            assets.append(block)
        elif role == "media_asset":
            raw_label = str(block.get("raw_label", "")).strip()
            if (
                raw_label in {"image", "chart", "figure_title", "figure"}
                or not raw_label
                or raw_label == "table"
                and "<img" in str(block.get("text") or "").lower()
            ):
                assets.append(block)

    # --- P3A: annotate asset family hints ---
    for asset in assets:
        raw_label = str(asset.get("raw_label", "")).strip().lower()
        if raw_label in {"image", "chart", "figure_title", "figure"}:
            asset["asset_family_hint"] = "figure_like"
            asset["asset_family_confidence"] = 0.70
            asset["asset_family_evidence"] = [f"raw_label:{raw_label}"]
        elif raw_label == "table":
            asset["asset_family_hint"] = "table_like"
            asset["asset_family_confidence"] = 0.70
            asset["asset_family_evidence"] = ["raw_label:table"]
        else:
            asset["asset_family_hint"] = "ambiguous"
            asset["asset_family_confidence"] = 0.35
            asset["asset_family_evidence"] = ["no_label_signal"]

    numbered_legends = [leg for leg in legends if _extract_figure_number(leg.get("text", "")) is not None]
    unnumbered_legends = [leg for leg in legends if _extract_figure_number(leg.get("text", "")) is None]
    ordered_legends = numbered_legends + unnumbered_legends
    bundle_source_legend_ids = _identify_bundle_source_legend_ids(numbered_legends, assets)
    legend_sort_order = {
        (int(legend.get("page", 0) or 0), str(legend.get("block_id", ""))): index
        for index, legend in enumerate(ordered_legends)
    }

    # Deduplicate numbered legends by family, but treat caption-list / bundle-source
    # pages as lower-priority duplicates when a stronger real legend instance exists.
    _dedup_map: dict[tuple[str, int], dict] = {}
    _same_number_distinct_keys: set[tuple[str, int, int, str]] = set()
    for legend in ordered_legends:
        text = legend.get("text", "")
        fn = _extract_figure_number(text)
        if fn is None:
            continue
        ns = _extract_figure_namespace(text)
        key = (ns, fn)
        if key not in _dedup_map:
            _dedup_map[key] = legend
        else:
            existing = _dedup_map[key]
            existing_body = _normalized_caption_body(str(existing.get("text", "")))
            new_body = _normalized_caption_body(text)
            existing_is_bundle = str(existing.get("block_id", "")) in bundle_source_legend_ids
            new_is_bundle = str(legend.get("block_id", "")) in bundle_source_legend_ids

            # Always determine winner/loser via dedup priority
            if _legend_dedup_priority(
                legend,
                bundle_source_legend_ids=bundle_source_legend_ids,
                assets=assets,
            ) > _legend_dedup_priority(
                existing,
                bundle_source_legend_ids=bundle_source_legend_ids,
                assets=assets,
            ):
                winner, loser = legend, existing
            else:
                winner, loser = existing, legend

            if (
                existing_body
                and new_body
                and existing_body != new_body
                and not existing_is_bundle
                and not new_is_bundle
            ):
                _dedup_map[key] = winner
                _same_number_distinct_keys.add(
                    (ns, fn, int(loser.get("page", 0) or 0), str(loser.get("block_id", "")))
                )
            else:
                _dedup_map[key] = winner

    emitted_winner_keys: set[tuple[str, int]] = set()
    deduped_legends: list[dict] = []
    deduped_legend_ids: list[dict] = []
    for legend in ordered_legends:
        text = legend.get("text", "")
        fn = _extract_figure_number(text)
        if fn is not None:
            ns = _extract_figure_namespace(text)
            key = (ns, fn)
            _distinct_check = (
                ns,
                fn,
                int(legend.get("page", 0) or 0),
                str(legend.get("block_id", "")),
            )
            if _distinct_check in _same_number_distinct_keys:
                deduped_legends.append(legend)
                deduped_legend_ids.append(
                    {
                        "page": legend.get("page"),
                        "block_id": legend.get("block_id", ""),
                        "dedup_reason": "same_number_distinct_caption_text",
                    }
                )
                continue
            kept = _dedup_map[key]
            current_id = str(legend.get("block_id", ""))
            kept_id = str(kept.get("block_id", ""))
            current_page = int(legend.get("page", 0) or 0)
            kept_page = int(kept.get("page", 0) or 0)
            if key in emitted_winner_keys or current_id != kept_id or current_page != kept_page:
                deduped_legend_ids.append(
                    {
                        "page": legend.get("page"),
                        "block_id": legend.get("block_id", ""),
                        "dedup_reason": (
                            "bundle_source_duplicate_loser"
                            if str(legend.get("block_id", "")) in bundle_source_legend_ids
                            and str(kept.get("block_id", "")) != str(legend.get("block_id", ""))
                            else "duplicate_loser"
                        ),
                        "kept_block_id": kept.get("block_id", ""),
                        "kept_page": kept.get("page"),
                    }
                )
                if key in emitted_winner_keys:
                    continue
                if current_id != kept_id or current_page != kept_page:
                    emitted_winner_keys.add(key)
                    deduped_legends.append(kept)
                    continue
            emitted_winner_keys.add(key)
            deduped_legends.append(kept)
        else:
            deduped_legends.append(legend)
    deduped_legends.sort(
        key=lambda legend: (
            legend_sort_order.get((int(legend.get("page", 0) or 0), str(legend.get("block_id", ""))), 10**9),
            int(legend.get("page", 0) or 0),
            (legend.get("bbox") or [0, 0, 0, 0])[1],
            (legend.get("bbox") or [0, 0, 0, 0])[0],
        )
    )
    ordered_legends = deduped_legends

    candidate_groups = _build_candidate_figure_groups_from_assets(
        assets,
        structured_blocks,
        ordered_legends,
        page_width=page_width,
    )

    composite_parent_candidates = _build_composite_parent_figure_groups_visual_only(
        candidate_groups,
        assets,
        structured_blocks,
        page_width,
    )

    # --- Dense composite parent construction before legend loop so
    # dense parents participate in arbitration ---
    _numbered_pages_early = {
        int(leg.get("page", 0) or 0)
        for leg in legends
        if _extract_figure_number(str(leg.get("text") or "")) is not None
    }
    _early_dense_parents = _build_dense_composite_parent_candidates(
        candidate_groups,
        [],
        _numbered_pages_early,
        page_width,
    )
    _parent_dict = {
        str(p.get("group_id", "")): p
        for p in composite_parent_candidates
        if str(p.get("group_id", ""))
    }
    for p in _early_dense_parents:
        pid = str(p.get("group_id", ""))
        if pid:
            _parent_dict[pid] = p
    composite_parent_candidates = list(_parent_dict.values())

    # --- Panel-title suppression: demote short unnumbered text inside visual
    # envelopes from formal legend matching on pages with numbered captions ---
    _numbered_pages = {
        int(leg.get("page", 0) or 0)
        for leg in legends
        if _extract_figure_number(str(leg.get("text") or "")) is not None
    }
    visual_envelopes: list[dict] = list(candidate_groups) + list(composite_parent_candidates)
    suppressed_caption_candidates: list[dict] = []
    remaining_legends: list[dict] = []
    for leg in legends:
        if _should_suppress_panel_title_candidate(
            leg,
            page_has_numbered_legend=int(leg.get("page", 0) or 0) in _numbered_pages,
            visual_envelopes=visual_envelopes,
            page_width=page_width,
        ):
            suppressed_caption_candidates.append(
                {
                    "block_id": leg.get("block_id", ""),
                    "page": leg.get("page"),
                    "text": leg.get("text", ""),
                    "suppression_reason": "panel_title_inside_visual_envelope",
                    "retained_as": "embedded_figure_text",
                }
            )
        else:
            remaining_legends.append(leg)
    legends = remaining_legends
    ordered_legends = [
        leg for leg in ordered_legends
        if not _should_suppress_panel_title_candidate(
            leg,
            page_has_numbered_legend=int(leg.get("page", 0) or 0) in _numbered_pages,
            visual_envelopes=visual_envelopes,
            page_width=page_width,
        )
    ]

    # Gate: suppress page_assets groups on pages with competing captions
    # so one big group doesn't swallow assets meant for multiple figures.
    # Only count legends with a real figure number — sub-panel labels
    # ("Tensile strength", "RND") are not captions and should not gate.
    _page_captions_for_gate: dict[int, set[str]] = {}
    for legend in ordered_legends:
        fid = legend.get("block_id")
        if fid and _extract_figure_number(str(legend.get("text") or "")) is not None:
            _page_captions_for_gate.setdefault(legend.get("page", 0), set()).add(str(fid))
    _competing_caption_pages = {p for p, ids in _page_captions_for_gate.items() if len(ids) > 1}
    candidate_groups = [
        g
        for g in candidate_groups
        if not (g.get("group_type") == "page_assets" and g.get("page") in _competing_caption_pages)
    ]
    _candidate_groups_by_id = {
        str(group.get("group_id", "")): group
        for group in candidate_groups
        if str(group.get("group_id", ""))
    }

    # --- Stage 1: page ledger, reservation ---
    _ledger = _build_page_ledger(ordered_legends, candidate_groups)
    _residual_ledger = _build_residual_ledger(
        ordered_legends, candidate_groups, competing_caption_pages=_competing_caption_pages
    )
    _legends_by_page: dict[int, list[dict]] = {}
    for leg in ordered_legends:
        _legends_by_page.setdefault(int(leg.get("page", 0) or 0), []).append(leg)
    _sidecar_pages: set[int] = set()
    for sp, spl in _legends_by_page.items():
        if len(_same_page_narrow_caption_column(spl, page_width)) >= 2:
            _sidecar_pages.add(sp)
    _reserved_legend_ids, _reserved_group_ids = _reserve_cross_page_objects(
        ordered_legends,
        candidate_groups,
        _residual_ledger,
        competing_caption_pages=_competing_caption_pages,
        sidecar_pages=_sidecar_pages,
    )
    # --- end Stage 1 reservation ---

    # Pre-compute page context for page_assets safety gate
    _page_blocks_by_page: dict[int, list[dict]] = {}
    for block in structured_blocks:
        bp = int(block.get("page", 0) or 0)
        _page_blocks_by_page.setdefault(bp, []).append(block)

    _page_height_by_page: dict[int, float] = {}
    for page, page_blocks_on_page in _page_blocks_by_page.items():
        explicit = max(
            (float(b.get("page_height") or 0)) for b in page_blocks_on_page
        )
        _page_height_by_page[page] = explicit if explicit > 0 else _estimate_page_height(page_blocks_on_page)

    _numbered_legend_count_by_page: dict[int, int] = {}
    for leg in ordered_legends:
        lp = int(leg.get("page", 0) or 0)
        if _extract_figure_number(str(leg.get("text") or "")) is not None:
            _numbered_legend_count_by_page[lp] = _numbered_legend_count_by_page.get(lp, 0) + 1

    page_caption_index = _formal_figure_caption_blocks(structured_blocks)
    for legend in ordered_legends:
        legend_id = str(legend.get("block_id", ""))
        legend_reserved_for_cross_page = (int(legend.get("page", 0) or 0), legend_id) in _reserved_legend_ids
        legend_page = legend.get("page", 0)
        legend_text = str(legend.get("text") or "")
        ns = _extract_figure_namespace(legend_text)
        fig_num = _extract_figure_number(legend_text)
        # Text may be empty (stored as [] in raw block) but marker_signature
        # still carries the figure number.
        if fig_num is None:
            ms = legend.get("marker_signature") or {}
            if ms.get("type") == "figure_number":
                try:
                    fig_num = int(float(ms.get("number", 0) or 0))
                except (ValueError, TypeError):
                    pass
        is_validation_first_candidate = _is_validation_first_legend_candidate(legend)
        is_weak_truncated = _is_insufficient_legend_evidence(legend)

        body_prose_likelihood = _looks_like_inline_figure_mention(legend_text, legend)

        caption_score = score_figure_caption(
            legend,
            nearby_media=any(a.get("page", 0) == legend_page for a in assets),
            caption_style_match=_caption_style_match(legend, structured_blocks),
            body_prose_likelihood=body_prose_likelihood,
        )

        candidates = []
        anchor_supported = _has_anchor_supported_legend_context(legend)
        caption_text_supported = _has_strong_explicit_caption_text(legend)
        family_supported = is_validation_first_candidate and str(legend.get("style_family") or "") == "legend_like"
        zone_supported = is_validation_first_candidate and str(legend.get("zone") or "") in {
            "body_zone",
            "display_zone",
        }

        # --- P1B: composite parent arbitration (before same-page candidate scan) ---
        _parent_accepted = False
        _parent_consumed_assets: list[dict] = []
        _parent_consumed_asset_ids: list[str] = []

        if fig_num is not None:
            _page_parents = [
                cp for cp in composite_parent_candidates
                if cp.get("page") == legend_page
                and not cp.get("ownership_consumed", False)
                and not (
                    cp.get("parent_subtype") == "dense_composite"
                    and len(_page_captions_for_gate.get(legend_page, set())) > 1
                )
            ]
            if _page_parents:
                # Prefer the parent with highest confidence
                _page_parents.sort(key=lambda cp: cp.get("parent_confidence", 0), reverse=True)
                _best_parent = _page_parents[0]
                _parent_conf = _best_parent.get("parent_confidence", 0)

                # Competing caption veto: if this page has >1 numbered legend,
                # do not promote a composite parent.
                _page_num_legends = _page_captions_for_gate.get(legend_page, set())
                _has_competing_caption = len(_page_num_legends) > 1

                _matching_child_groups = [
                    _candidate_groups_by_id[child_gid]
                    for child_gid in _best_parent.get("child_group_ids", [])
                    if child_gid in _candidate_groups_by_id
                    and str(
                        (
                            _candidate_groups_by_id[child_gid].get("assist", {}) or {}
                        ).get("best_caption_band_id") or ""
                    )
                    == legend_id
                ]
                _is_dense = _best_parent.get("parent_subtype") == "dense_composite"
                _all_child_groups = [
                    _candidate_groups_by_id[child_gid]
                    for child_gid in _best_parent.get("child_group_ids", [])
                    if child_gid in _candidate_groups_by_id
                ]
                # For dense parents with a single caption, consume all child groups.
                # With competing captions, keep caption-band scoping.
                _effective_child_groups = (
                    _all_child_groups if (_is_dense and not _has_competing_caption)
                    else (_matching_child_groups or _all_child_groups)
                )
                _effective_child_group_ids = [
                    str(group.get("group_id", "")) for group in _effective_child_groups if str(group.get("group_id", ""))
                ]
                _effective_asset_ids = list(
                    dict.fromkeys(
                        bid
                        for group in _effective_child_groups
                        for bid in group.get("asset_block_ids", [])
                    )
                )
                _band_scoped_parent = len(_matching_child_groups) >= 2
                _dense_parent_unresolved_count = int(_best_parent.get("unresolved_child_count", 0) or 0)
                _dense_parent_fragment_count = int(_best_parent.get("fragment_count", 0) or 0)
                _dense_parent_single_group_ok = (
                    _best_parent.get("parent_subtype") == "dense_composite"
                    and len(_effective_child_groups) == 1
                    and _dense_parent_fragment_count >= 4
                )

                if (
                    _parent_conf >= 0.60
                    and (not _has_competing_caption or _band_scoped_parent)
                    and (len(_effective_child_groups) >= 2 or _dense_parent_single_group_ok)
                ):
                    _parent_accepted = True
                    _parent_consumed_asset_ids = _effective_asset_ids
                    _best_parent["ownership_consumed"] = True

                    for bid in _parent_consumed_asset_ids:
                        asset = next((a for a in assets if a.get("block_id") == bid), None)
                        if asset:
                            _parent_consumed_assets.append(_project_asset_record(asset))
                            ownership.mark_assets_owned(
                                [(int(asset.get("page", 0) or 0), bid)],
                                owner_id=str(legend.get("block_id", "")),
                                owner_family="figure",
                            )

                    # Consume child groups
                    for child_gid in _effective_child_group_ids:
                        used_group_ids.add(child_gid)

        if _parent_accepted:
            _parent_fig_id = _format_figure_id(ns, fig_num)
            matched_figures.append(
                {
                    "figure_id": _parent_fig_id,
                    "figure_namespace": ns,
                    "legend_block_id": legend.get("block_id", ""),
                    "page": legend_page,
                    "text": str(legend.get("text", "")),
                    "figure_number": fig_num,
                    "matched_assets": _parent_consumed_assets,
                    "asset_block_ids": _parent_consumed_asset_ids,
                    "bridge_block_ids": [],
                    "caption_score": caption_score,
                    "match_score": {
                        "score": _parent_conf,
                        "decision": "matched",
                        "evidence": ["composite_parent"],
                    },
                    "confidence": _parent_conf,
                    "flags": ["composite_parent_match"],
                    "legend_page": legend_page,
                    "asset_pages": [legend_page],
                    "settlement_type": "composite_parent",
                }
            )
            unmatched_legends = [l for l in unmatched_legends if l.get("block_id") != legend.get("block_id")]
            continue  # skip normal same-page candidate scan for this legend
        # --- end P1B composite parent arbitration ---

        for gi, group in enumerate(candidate_groups):
            if group.get("page") != legend_page:
                continue
            group_id = str(group.get("group_id", ""))
            assist = group.get("assist", {}) or {}
            assist_band_id = str(assist.get("best_caption_band_id") or "")
            if (
                not legend_reserved_for_cross_page
                and legend_page in _competing_caption_pages
                and assist_band_id
                and assist_band_id != legend_id
            ):
                continue
            g_asset_block_ids = set(group.get("asset_block_ids", []))
            g_page = group.get("page", 0)
            g_qual = {(g_page, bid) for bid in g_asset_block_ids}
            group_reserved_for_cross_page = group_id in _reserved_group_ids
            if not group_reserved_for_cross_page and not ownership.can_consume_assets(list(g_qual)):
                continue
            # --- P3B/P3C: strong table-like veto in figure matcher ---
            _group_asset_ids = group.get("asset_block_ids", [])
            _strong_table_like = False
            if _group_asset_ids:
                _hint_map = {str(a.get("block_id", "")): a for a in assets}
                _table_like_count = sum(
                    1 for bid in _group_asset_ids
                    if _hint_map.get(str(bid), {}).get("asset_family_hint") == "table_like"
                    and float(_hint_map.get(str(bid), {}).get("asset_family_confidence", 0) or 0) >= 0.70
                )
                if _table_like_count >= len(_group_asset_ids):
                    _strong_table_like = True
            if _strong_table_like:
                continue
            # --- end P3B/P3C veto ---
            match_score = _score_legend_to_group(
                legend,
                group,
                caption_score=caption_score,
                page_width=page_width,
                page_blocks=_page_blocks_by_page.get(legend_page, []),
                page_height=_page_height_by_page.get(legend_page, 0.0),
                page_numbered_legend_count=_numbered_legend_count_by_page.get(legend_page, 0),
                anchor_supported=anchor_supported,
                caption_text_supported=caption_text_supported,
                family_supported=family_supported,
                zone_supported=zone_supported,
            )
            if match_score["decision"] != "rejected":
                hypothesis = _make_local_pairing_hypothesis(
                    legend,
                    group,
                    mode=_infer_local_pairing_mode(legend, group, page_width=page_width),
                    local_score=match_score.get("score", 0.0),
                    evidence=match_score.get("evidence", []),
                )
                if legend_reserved_for_cross_page or group_id in _reserved_group_ids:
                    _mark_hypothesis_conflict(hypothesis, "reserved_same_page_commit_deferred")
                local_pairing_hypotheses.append(hypothesis)
                candidates.append((gi, group, match_score))
        candidates.sort(key=lambda item: item[2]["score"], reverse=True)

        matched_assets = []
        region_match = None
        ambiguous = False
        defer_reserved_same_page_commit = False

        if candidates:
            top_score = candidates[0][2]["score"]
            close = [item for item in candidates if top_score - item[2]["score"] < 0.15]
            if top_score < 0.4:
                matched_assets = []
            elif len(close) > 1:
                close_sides = {_asset_vertical_side(legend, g) for _, g, _ in close}
                if "above" in close_sides and "below" in close_sides and fig_num is not None:
                    # If one candidate is the page_assets group (all assets),
                    # don't tie-break — it's the canonical match.
                    page_assets_candidate = next(
                        (item for item in close if item[1].get("group_type") == "page_assets"), None
                    )
                    if page_assets_candidate is not None:
                        close = [page_assets_candidate]
                    else:
                        # Caption sandwiched between two asset groups:
                        # prefer above (standard: image above, caption below).
                        above_only = [item for item in close if _asset_vertical_side(legend, item[1]) == "above"]
                        if above_only:
                            close = above_only
                        else:
                            ambiguous_figures.append(
                                {
                                    "legend_block_id": legend.get("block_id", ""),
                                    "page": legend_page,
                                    "text": legend_text,
                                    "figure_number": fig_num,
                                    "caption_score": caption_score,
                                    "candidates": [
                                        {
                                            "asset_block_id": g.get("media_blocks", [{}])[0].get("block_id", ""),
                                            "group_type": g.get("group_type", ""),
                                            "match_score": s,
                                        }
                                        for _, g, s in close
                                    ],
                                    "hold_reason": "close_asset_tie",
                                }
                            )
                            ambiguous = True
                            matched_assets = []
                            close = []
                if not close:
                    pass
                else:
                    legend_bb = legend.get("bbox") or legend.get("block_bbox") or [0, 0, 0, 0]
                    lcx = (legend_bb[0] + legend_bb[2]) / 2 if len(legend_bb) >= 4 else 0
                    best = close[0]
                    best_delta = abs(
                        lcx
                        - (
                            (
                                best[1].get("cluster_bbox", [0, 0, 0, 0])[0]
                                + best[1].get("cluster_bbox", [0, 0, 0, 0])[2]
                            )
                            / 2
                        )
                    )
                    best_orig_score = best[2]["score"]
                    for ci, cg, cs in close:
                        cb = cg.get("cluster_bbox", [0, 0, 0, 0])
                        acx = (cb[0] + cb[2]) / 2 if len(cb) >= 4 else 0
                        delta = abs(lcx - acx)
                        ca_col_ok = delta + 20 < best_delta and cs["score"] >= best_orig_score - 0.01
                        if ca_col_ok:
                            best = (ci, cg, cs)
                            best_delta = delta
                    if best[2].get("decision") == "matched":
                        best_gi, best_group, best_score = best
                        if legend_reserved_for_cross_page or str(best_group.get("group_id", "")) in _reserved_group_ids:
                            defer_reserved_same_page_commit = True
                            matched_assets = []
                            region_match = None
                        else:
                            matched_assets = best_group.get("media_blocks", [])
                            g_page = best_group.get("page", 0)
                            ownership.match_group(best_group, owner_id=str(legend.get("block_id", "")), owner_family="figure")
                            matched_assets = _expand_matched_assets_locally(
                                legend,
                                matched_assets,
                                assets,
                                ownership,
                                page_caption_index.get(g_page, []),
                            )
                            region_match = {
                                "media_blocks": matched_assets,
                                "match_score": best_score,
                                "group_type": best_group.get("group_type", ""),
                                "group_evidence": best_group.get("group_evidence", []),
                            }
                            if len(matched_assets) > 1:
                                region_match["cluster_bbox"] = best_group.get("cluster_bbox", [0, 0, 0, 0])
                    else:
                        if (
                            fig_num is None
                            and str(legend.get("role") or "") in ("figure_caption", "figure_caption_candidate")
                            and len(legend_text) < 80
                        ):
                            unmatched_legends.append(legend)
                            continue
                        ambiguous_figures.append(
                            {
                                "legend_block_id": legend.get("block_id", ""),
                                "page": legend_page,
                                "caption_score": caption_score,
                                "figure_number": fig_num,
                                "candidates": [
                                    {
                                        "asset_block_id": g.get("media_blocks", [{}])[0].get("block_id", ""),
                                        "group_type": g.get("group_type", ""),
                                        "match_score": s,
                                    }
                                    for _, g, s in close
                                ],
                                "hold_reason": "close_unconfirmed_match",
                            }
                        )
                        ambiguous = True
                        matched_assets = []
            else:
                best_gi, best_group, best_score = candidates[0]
                if best_score["decision"] == "matched":
                    if legend_reserved_for_cross_page or str(best_group.get("group_id", "")) in _reserved_group_ids:
                        defer_reserved_same_page_commit = True
                        matched_assets = []
                        region_match = None
                    else:
                        matched_assets = best_group.get("media_blocks", [])
                        g_page = best_group.get("page", 0)
                        ownership.match_group(best_group, owner_id=str(legend.get("block_id", "")), owner_family="figure")
                        matched_assets = _expand_matched_assets_locally(
                            legend,
                            matched_assets,
                            assets,
                            ownership,
                            page_caption_index.get(g_page, []),
                        )
                        region_match = {
                            "media_blocks": matched_assets,
                            "match_score": best_score,
                            "group_type": best_group.get("group_type", ""),
                            "group_evidence": best_group.get("group_evidence", []),
                        }
                        if len(matched_assets) > 1:
                            region_match["cluster_bbox"] = best_group.get("cluster_bbox", [0, 0, 0, 0])
                else:
                    if fig_num is None and str(legend.get("role") or "") in (
                        "figure_caption",
                        "figure_caption_candidate",
                    ):
                        unmatched_legends.append(legend)
                        continue
                    ambiguous_figures.append(
                        {
                            "legend_block_id": legend.get("block_id", ""),
                            "page": legend_page,
                            "caption_score": caption_score,
                            "figure_number": fig_num,
                            "candidates": [
                                {
                                    "asset_block_id": best_group.get("media_blocks", [{}])[0].get("block_id", ""),
                                    "group_type": best_group.get("group_type", ""),
                                    "match_score": best_score,
                                }
                            ],
                            "hold_reason": "single_unconfirmed_match",
                        }
                    )
                    ambiguous = True
                    ambiguous = True

        is_legend_only = len(matched_assets) == 0

        if defer_reserved_same_page_commit:
            continue

        if caption_score.get("score", 0.0) < 0.4:
            unmatched_legends.append(legend)
            continue

        if is_weak_truncated and is_validation_first_candidate and is_legend_only:
            held_figures.append(
                {
                    "figure_id": f"held_figure_{len(held_figures) + 1:03d}",
                    "legend_block_id": legend.get("block_id", ""),
                    "page": legend_page,
                    "text": legend_text,
                    "figure_number": fig_num,
                    "hold_reason": "insufficient_legend_evidence",
                    "zone": legend.get("zone"),
                    "style_family": legend.get("style_family"),
                    "marker_signature": legend.get("marker_signature") or {},
                    "caption_score": caption_score,
                }
            )
            unmatched_legends.append(legend)
            continue

        # When multiple truncated legends ("Fig. 4" only) share a page,
        # they are sub-panel labels on a multi-figure gallery — skip them
        # and let the shared caption claim all assets.
        if is_weak_truncated:
            page_truncated_count = sum(
                1
                for leg in ordered_legends
                if leg.get("page", 0) == legend_page and _is_insufficient_legend_evidence(leg)
            )
            if page_truncated_count >= 2:
                unmatched_legends.append(legend)
                continue
            # Single truncated legend on page: match normally with candidates
            if is_legend_only:
                weak_candidates = [
                    {
                        "asset_block_id": g.get("media_blocks", [{}])[0].get("block_id", ""),
                        "group_type": g.get("group_type", ""),
                        "match_score": s,
                    }
                    for _, g, s in (candidates or [])
                ]
                ambiguous_figures.append(
                    {
                        "legend_block_id": legend.get("block_id", ""),
                        "page": legend_page,
                        "text": legend_text,
                        "figure_number": fig_num,
                        "caption_score": caption_score,
                        "candidates": weak_candidates,
                        "hold_reason": "ambiguous_truncated_legend",
                        "zone": legend.get("zone"),
                        "style_family": legend.get("style_family"),
                        "marker_signature": legend.get("marker_signature") or {},
                    }
                )
                unmatched_legends.append(legend)
                continue
            # Single truncated legend with matched assets — proceed to confirmation

        if is_legend_only:
            if legend_reserved_for_cross_page:
                continue
            if fig_num is None and str(legend.get("role") or "") in ("figure_caption_candidate", "figure_caption"):
                unmatched_legends.append(legend)
                continue
            weak_entry = {
                "legend_block_id": legend.get("block_id", ""),
                "page": legend_page,
                "text": legend_text,
                "figure_number": fig_num,
                "caption_score": caption_score,
                "candidates": [],
                "hold_reason": ("ambiguous_truncated_legend" if is_weak_truncated else "no_asset_match"),
                "zone": legend.get("zone"),
                "style_family": legend.get("style_family"),
                "marker_signature": legend.get("marker_signature") or {},
            }
            if not ambiguous:
                ambiguous_figures.append(weak_entry)
            unmatched_legends.append(legend)
            continue

        fig_id = _format_figure_id(ns, fig_num) if fig_num else f"figure_unknown_{len(matched_figures):03d}"
        match_score = (
            region_match["match_score"]
            if region_match is not None
            else {
                "score": 0.0,
                "decision": "rejected",
                "evidence": ["missing_region_match"],
            }
        )
        entry = {
            "figure_id": fig_id,
            "figure_namespace": ns,
            "legend_block_id": legend.get("block_id", ""),
            "page": legend_page,
            "text": legend_text,
            "figure_number": fig_num,
                "matched_assets": [_project_asset_record(a) for a in matched_assets],
            "group_type": region_match.get("group_type", "") if region_match is not None else "",
            "group_evidence": region_match.get("group_evidence", []) if region_match is not None else [],
            "confidence": match_score["score"],
            "match_score": match_score,
            "flags": [],
            "caption_score": caption_score,
            "legend_page": legend_page,
            "asset_pages": sorted({int(a.get("page", 0) or 0) for a in matched_assets}) or [legend_page],
            "settlement_type": "same_page",
        }
        local_bridges = _collect_bridge_blocks(int(legend_page or 0))
        entry["asset_block_ids"] = [str(a.get("block_id", "")) for a in matched_assets if a.get("block_id")]
        entry["bridge_block_ids"] = [str(b.get("block_id", "")) for b in local_bridges if b.get("block_id")]
        if len(matched_assets) > 1:
            entry["cluster_bbox"] = _cluster_bbox([a.get("bbox", [0, 0, 0, 0]) for a in matched_assets])
            # Push cluster_bbox top down past figure_inner_text blocks (panel labels)
            # to exclude (A), (B) etc. from the cropped figure image.
            leg_page = int(legend_page or 0)
            for ib in structured_blocks:
                if int(ib.get("page", 0) or 0) != leg_page or ib.get("role") != "figure_inner_text":
                    continue
                ib_bbox = ib.get("bbox") or ib.get("block_bbox") or [0, 0, 0, 0]
                cb = entry["cluster_bbox"]
                if len(ib_bbox) >= 4 and len(cb) >= 4:
                    # Horizontal overlap: inner text sits in the figure's column
                    x_overlap = ib_bbox[0] < cb[2] and ib_bbox[2] > cb[0]
                    # Text starts above cluster top and extends into it
                    is_above = ib_bbox[1] < cb[1] and ib_bbox[3] > cb[1]
                    if x_overlap and is_above:
                        cb[1] = max(cb[1], ib_bbox[3])
        matched_figures.append(entry)

    for _i, asset in enumerate(assets):
        asset_page = asset.get("page")
        asset_bid = asset.get("block_id", "")
        if not asset_bid or (asset_page, asset_bid) not in used_asset_page_ids:
            unmatched_assets.append(asset)

    # --- Stage 1: primary cross-page settlement before legacy fallback ---
    _settle_cross_page_reserved_objects(
        _reserved_legend_ids,
        _reserved_group_ids,
        ordered_legends,
        candidate_groups,
        structured_blocks,
        matched_figures,
        ambiguous_figures,
        unmatched_legends,
        used_group_ids,
        used_asset_page_ids,
        ownership,
        residual_ledger=_residual_ledger,
        competing_caption_pages=_competing_caption_pages,
        page_width=page_width,
    )
    # Rebuild unmatched_assets after cross-page settlement consumed some
    unmatched_assets.clear()
    for _a in assets:
        _ap = _a.get("page", 0)
        _bid = _a.get("block_id", "")
        if _bid and (_ap, _bid) not in used_asset_page_ids:
            unmatched_assets.append(_a)
    # Handle failed reserved groups: multi-asset -> unresolved_clusters, single -> unmatched_assets
    _failed_reserved_groups = [
        g
        for g in candidate_groups
        if str(g.get("group_id", "")) in _reserved_group_ids and str(g.get("group_id", "")) not in used_group_ids
    ]
    for _fg in _failed_reserved_groups:
        _fg_type = _fg.get("group_type", "")
        _fg_page = int(_fg.get("page", 0) or 0)
        _fg_bids = [str(b.get("block_id", "")) for b in _fg.get("media_blocks", []) if b.get("block_id")]
        if _fg_type == "single_asset" or len(_fg.get("media_blocks", [])) <= 1:
            # single-asset reserved group failure -> remain in unmatched_assets
            pass
        else:
            # multi-asset reserved group failure -> unresolved_clusters
            unresolved_clusters.append(
                {
                    "cluster_id": f"unresolved_cluster_{len(unresolved_clusters) + 1:03d}",
                    "media_block_ids": _fg_bids,
                    "cluster_bbox": _fg.get("cluster_bbox", [0, 0, 0, 0]),
                    "page": _fg_page,
                    "hold_reason": "reserved_cross_page_no_valid_legend",
                }
            )
            # Remove these bids from unmatched_assets
            unmatched_assets = [
                ua
                for ua in unmatched_assets
                if str(ua.get("block_id", "")) not in _fg_bids or ua.get("page", 0) != _fg_page
            ]
    # --- end Stage 1 cross-page settlement ---

    # Sidecar fallback: for pages with narrow same-column formal captions,
    # override normal figure matching with caption-band partitioning.
    # The normal spatial matcher cannot reliably assign assets to narrow
    # sidecar captions -- the visible image/caption pairing is column-based,
    # not gap/overlap based.
    # Uses the `deduped_legends` list (already filtered through the full multi-evidence
    # legend recognition pipeline + figure-number dedup) instead of raw caption index.
    _sidecar_by_page: dict[int, list[dict]] = {}
    for leg in deduped_legends:
        _sidecar_by_page.setdefault(int(leg.get("page", 0) or 0), []).append(leg)
    for sidecar_page, sidecar_legends in _sidecar_by_page.items():
        narrow_set = _same_page_narrow_caption_column(sidecar_legends, page_width)
        if len(narrow_set) < 2:
            continue
        # Trigger sidecar when any narrow-caption legend is unresolved, or
        # when the normal spatial matching violates vertical ordering (top
        # caption should own top assets, bottom caption bottom assets).
        nid_set = {str(cap.get("block_id", "")) for cap in narrow_set}
        page_unmatched = any(str(l.get("block_id", "")) in nid_set for l in unmatched_legends)
        page_ambiguous = any(str(af.get("legend_block_id", "")) in nid_set for af in ambiguous_figures)

        def _median_y(items: list[dict]) -> float:
            ys = sorted((a.get("bbox") or [0, 0, 0, 0])[1] for a in items)
            return float(ys[len(ys) // 2]) if ys else 0.0

        page_narrow_matched = [mf for mf in matched_figures if str(mf.get("legend_block_id", "")) in nid_set]
        page_unresolved_legend_ids = {
            str(item.get("block_id", ""))
            for item in unmatched_legends
            if str(item.get("block_id", "")) in nid_set
        } | {
            str(item.get("legend_block_id", ""))
            for item in ambiguous_figures
            if str(item.get("legend_block_id", "")) in nid_set
        }
        protected_by_legend_id = {
            str(mf.get("legend_block_id", "")): mf
            for mf in page_narrow_matched
            if str(mf.get("legend_block_id", ""))
            and _has_protected_figure_ownership(mf, unresolved_legend_ids=page_unresolved_legend_ids)
        }
        protected_asset_page_ids = {
            (int(a.get("page", mf.get("page", sidecar_page)) or 0), str(a.get("block_id", "")))
            for mf in protected_by_legend_id.values()
            for a in mf.get("matched_assets", [])
            if a.get("block_id") not in (None, "")
        }
        reclaimable_asset_page_ids = {
            (int(a.get("page", mf.get("page", sidecar_page)) or 0), str(a.get("block_id", "")))
            for mf in page_narrow_matched
            for a in mf.get("matched_assets", [])
            if a.get("block_id") not in (None, "")
            and str(mf.get("legend_block_id", "")) not in protected_by_legend_id
        }
        violation = len(page_narrow_matched) != len(narrow_set)
        if not violation and len(page_narrow_matched) > 1:
            ordered = sorted(page_narrow_matched, key=lambda mf: int(mf.get("figure_number", 0) or 0))
            asset_ys = [_median_y(mf.get("matched_assets", [])) for mf in ordered]
            for i in range(len(asset_ys) - 1):
                if not (asset_ys[i] <= asset_ys[i + 1]):
                    violation = True
                    break
        if not (page_unmatched or page_ambiguous or violation):
            continue
        page_assets_list = [a for a in assets if a.get("page") == sidecar_page and not a.get("_non_body_media")]
        if not page_assets_list:
            continue
        hypothesis_protected_asset_page_ids = {
            tuple(asset_id)
            for hypothesis in local_pairing_hypotheses
            if hypothesis.get("legend_block_id") not in nid_set
            and hypothesis.get("mode") in {"caption_below", "caption_above"}
            and float(hypothesis.get("local_score", 0.0) or 0.0) >= 0.7
            for asset_id in hypothesis.get("would_consume_asset_ids", [])
        }
        grouped_asset_page_ids = _grouped_asset_page_ids(candidate_groups)
        sidecar_page_height = float(
            max((b.get("bbox") or [0, 0, 0, 0])[3] for b in structured_blocks if b.get("page") == sidecar_page) or 1600
        )
        band_map = _partition_assets_by_caption_bands(narrow_set, page_assets_list, sidecar_page_height)
        sidecar_promoted: list[dict] = []
        sidecar_consumed_ids: set[tuple] = set()
        page_narrow_matched_by_legend = {
            str(mf.get("legend_block_id", "")): mf
            for mf in page_narrow_matched
            if str(mf.get("legend_block_id", ""))
        }
        for cap in narrow_set:
            lid = str(cap.get("block_id", ""))
            cap_text = str(cap.get("text") or "")
            fig_num = _extract_figure_number(cap_text)
            cap_ns = _extract_figure_namespace(cap_text)
            current_match = page_narrow_matched_by_legend.get(lid)
            cap_is_unresolved = any(str(l.get("block_id", "")) == lid for l in unmatched_legends) or any(
                str(af.get("legend_block_id", "")) == lid for af in ambiguous_figures
            )
            if lid in protected_by_legend_id:
                continue
            if not violation and current_match is not None and not cap_is_unresolved:
                continue
            raw_band_assets = [
                asset
                for asset in band_map.get(lid, [])
                if (int(asset.get("page", 0) or 0), str(asset.get("block_id", ""))) not in protected_asset_page_ids
                and (int(asset.get("page", 0) or 0), str(asset.get("block_id", ""))) not in hypothesis_protected_asset_page_ids
            ]
            # In sidecar mode _partition_assets_by_caption_bands is the
            # ownership unit.  Row-coupled geometry is too strict for
            # AJR-style caption-column layouts and can drop far-column
            # panels within the same caption band.
            band_assets = raw_band_assets
            if not band_assets:
                continue
            eligible_asset_ids = _fallback_eligible_asset_page_ids(
                [
                    (int(asset.get("page", 0) or 0), str(asset.get("block_id", "")))
                    for asset in band_assets
                    if asset.get("block_id") not in (None, "")
                ],
                used_asset_page_ids=used_asset_page_ids - reclaimable_asset_page_ids,
                blocked_asset_page_ids=set(),
                grouped_asset_page_ids=grouped_asset_page_ids,
                allow_grouped=True,
            )
            if not eligible_asset_ids:
                continue
            eligible_asset_id_set = set(eligible_asset_ids)
            band_assets = [
                asset
                for asset in band_assets
                if (int(asset.get("page", 0) or 0), str(asset.get("block_id", ""))) in eligible_asset_id_set
            ]
            if not band_assets:
                continue
            for ba in band_assets:
                ap = ba.get("page", 0)
                bid = ba.get("block_id", "")
                if bid is not None:
                    sidecar_consumed_ids.add((ap, bid))
            fig_id = (
                _format_figure_id(cap_ns, fig_num)
                if fig_num
                else f"figure_sidecar_{len(matched_figures) + len(sidecar_promoted):03d}"
            )
            sidecar_entry = {
                "figure_id": fig_id,
                "legend_block_id": cap.get("block_id", ""),
                "page": sidecar_page,
                "text": cap_text,
                "figure_number": fig_num,
                "matched_assets": [
                    {"block_id": a.get("block_id", ""), "bbox": a.get("bbox", [0, 0, 0, 0])} for a in band_assets
                ],
                "asset_block_ids": [str(a.get("block_id", "")) for a in band_assets if a.get("block_id") is not None],
                "group_type": "sidecar_partition",
                "group_evidence": ["same_page", "narrow_caption_column", "sidecar_fallback"],
                "confidence": 0.5,
                "match_score": {"score": 0.5, "decision": "matched", "evidence": ["sidecar_fallback"]},
                "flags": ["sidecar_match"],
                "caption_score": score_figure_caption(
                    cap, nearby_media=True, caption_style_match=False, body_prose_likelihood=False
                ),
                "legend_page": sidecar_page,
                "asset_pages": [sidecar_page],
                "settlement_type": "sidecar",
            }
            local_pairing_hypotheses.append(
                _make_local_pairing_hypothesis(
                    cap,
                    {
                        "group_id": f"sidecar_partition:{sidecar_page}:{lid}",
                        "page": sidecar_page,
                        "media_blocks": band_assets,
                    },
                    mode="caption_sidecar",
                    local_score=0.5,
                    evidence=["same_row_alignment", "narrow_caption_column", "sidecar_fallback"],
                )
            )
            if len(band_assets) > 1:
                sidecar_entry["cluster_bbox"] = _cluster_bbox([a.get("bbox", [0, 0, 0, 0]) for a in band_assets])
            sidecar_promoted.append(sidecar_entry)
        if not sidecar_promoted:
            continue
        reclaimable_group_ids = {
            str(group.get("group_id", ""))
            for group in candidate_groups
            if str(group.get("group_id", ""))
            and all(
                aid in reclaimable_asset_page_ids
                for aid in [
                    _asset_page_id(group.get("page", 0), bid)
                    for bid in group.get("asset_block_ids", [])
                    if bid is not None
                ]
                if aid is not None
            )
        }
        for asset_id in reclaimable_asset_page_ids:
            used_asset_page_ids.discard(asset_id)
        for group_id in reclaimable_group_ids:
            used_group_ids.discard(group_id)
        promoted_legend_ids = {str(entry.get("legend_block_id", "")) for entry in sidecar_promoted}
        matched_figures = [
            mf for mf in matched_figures if str(mf.get("legend_block_id", "")) not in promoted_legend_ids
        ]
        for legend in list(legends):
            if str(legend.get("block_id", "")) in promoted_legend_ids:
                ambiguous_figures[:] = [
                    af
                    for af in ambiguous_figures
                    if str(af.get("legend_block_id", "")) != str(legend.get("block_id", ""))
                ]
                unmatched_legends[:] = [
                    ul for ul in unmatched_legends if str(ul.get("block_id", "")) != str(legend.get("block_id", ""))
                ]
        matched_figures.extend(sidecar_promoted)
        ownership.mark_assets_owned(list(sidecar_consumed_ids), owner_id=f"sidecar_page_{sidecar_page}", owner_family="figure")

    # Preproof legend-bundling: when a page packs 3+ figure captions
    # with zero same-page assets, match them 1:1 by page order to
    # subsequent pages that each hold unclaimed assets.
    if unmatched_legends and unmatched_assets:
        # ponytail: only multi-asset clusters are protected from bundle consumption;
        # single-asset groups on pages without same-page captions are orphans that
        # the bundle fallback should be allowed to consume.
        cluster_asset_page_ids = {
            (int(group.get("page", 0) or 0), str(bid))
            for group in candidate_groups
            if group.get("group_type") == "distance_cluster"
            for bid in group.get("asset_block_ids", [])
            if bid is not None
        }
        page_captions: dict[int, list[dict]] = {}
        for leg in unmatched_legends:
            cp = int(leg.get("page", 0) or 0)
            if _extract_figure_number(str(leg.get("text", ""))) is not None:
                page_captions.setdefault(cp, []).append(leg)
        for cp, caps in sorted(page_captions.items()):
            has_bundle_source = any(
                str(cap.get("block_id", "")) in bundle_source_legend_ids
                for cap in caps
            )
            if len(caps) < 3 and not has_bundle_source:
                continue
            page_has_assets = any(a.get("page", 0) == cp for a in unmatched_assets)
            if page_has_assets:
                continue
            caps_sorted = sorted(caps, key=lambda b: (b.get("bbox") or [0, 0, 0, 0])[1])
            # Collect subsequent pages with unclaimed assets and no captions
            asset_pages: dict[int, list[dict]] = {}
            for ast in unmatched_assets:
                ap = int(ast.get("page", 0) or 0)
                bid = ast.get("block_id", "")
                if (ap, str(bid)) in cluster_asset_page_ids:
                    continue
                if ap <= cp:
                    continue
                if bid and (ap, bid) in used_asset_page_ids:
                    continue
                asset_pages.setdefault(ap, []).append(ast)
            page_order = sorted(asset_pages.keys())
            # Validate: no body/table blocks between legend page and first asset page,
            # and each asset page is free of competing body/table text.
            _NON_PURE_ROLES = {
                "body_paragraph",
                "section_heading",
                "subsection_heading",
                "table_caption",
                "table_asset",
                "table_html",
                "backmatter_heading",
                "backmatter_body",
                "reference_item",
            }
            intervening_pages = set(range(cp + 1, page_order[0])) if page_order else set()
            intervening_body = any(
                b.get("page", 0) in intervening_pages and b.get("role", "") in _NON_PURE_ROLES
                for b in structured_blocks
            )
            if intervening_body:
                continue
            valid_pages = []
            for ap in page_order:
                page_has_body = any(
                    b.get("page", 0) == ap and b.get("role", "") in _NON_PURE_ROLES for b in structured_blocks
                )
                if not page_has_body:
                    valid_pages.append(ap)
            if len(valid_pages) < len(caps_sorted):
                caps_sorted = caps_sorted[: len(valid_pages)]
            if not valid_pages:
                continue
            # Match captions to validated asset pages in order
            for idx, cap in enumerate(caps_sorted):
                if idx >= len(valid_pages):
                    break
                ap = valid_pages[idx]
                page_assets = asset_pages[ap]
                if not page_assets:
                    continue
                fn = _extract_figure_number(str(cap.get("text", "")))
                cap_ns = _extract_figure_namespace(str(cap.get("text", "")))
                fig_id = _format_figure_id(cap_ns, fn)
                cap_score = score_figure_caption(
                    cap, nearby_media=True, caption_style_match=False, body_prose_likelihood=False
                )
                consumed = []
                for ast in page_assets:
                    bid = ast.get("block_id", "")
                    if bid:
                        ownership.mark_assets_owned([(ap, bid)], owner_id=str(cap.get("block_id", "")), owner_family="figure")
                consumed.append(_project_asset_record(ast))
                unmatched_legends = [l for l in unmatched_legends if l.get("block_id") != cap.get("block_id")]
                cp_page = int(cap.get("page", 0) or 0)
                matched_figures.append(
                    {
                        "figure_id": fig_id,
                        "figure_namespace": cap_ns,
                        "legend_block_id": cap.get("block_id", ""),
                        "page": ap,
                        "text": str(cap.get("text", "")),
                        "figure_number": fn,
                        "matched_assets": consumed,
                        "asset_block_ids": [c["block_id"] for c in consumed],
                        "bridge_block_ids": [],
                        "caption_score": cap_score,
                        "match_score": {"score": 0.3, "decision": "matched", "evidence": ["legend_bundle_fallback"]},
                        "confidence": 0.3,
                        "flags": ["legend_bundle_match"],
                        "legend_page": cp_page,
                        "asset_pages": [ap],
                        "settlement_type": "legend_bundle",
                    }
                )
            # De-dup ambiguous_figures: remove entries whose legend_block_id
            # was already matched by the bundle pass.
            bundle_legend_ids = {
                m["legend_block_id"] for m in matched_figures if "legend_bundle_match" in m.get("flags", [])
            }
            ambiguous_figures[:] = [a for a in ambiguous_figures if a.get("legend_block_id") not in bundle_legend_ids]

    # === Group-aware sequential fallback ===
    # Consume unmatched distance_clusters that no same-page legend claimed.
    # Inserted AFTER preproof fallback, BEFORE old single-asset sequential fallback.
    local_asset_by_page_id: dict[tuple[int, str], dict] = {}
    for block in _filter_figure_assets(assets):
        local_asset_by_page_id[(int(block.get("page", 0) or 0), str(block.get("block_id", "")))] = block

    # ponytail: also include single_asset groups per Option 1 — group-aware fallback
    # handles them so old sequential fallback only sees true bare assets.
    unmatched_groups = [
        g
        for g in candidate_groups
        if str(g.get("group_id", "")) not in used_group_ids
        and g.get("group_type") in {"distance_cluster", "single_asset"}
        and not any(
            (int(g.get("page", 0) or 0), str(bid)) in used_asset_page_ids
            for bid in g.get("asset_block_ids", [])
            if bid is not None
        )
    ]
    unmatched_groups.sort(
        key=lambda g: (
            int(g.get("page", 0) or 0),
            (g.get("cluster_bbox") or [0, 0, 0, 0])[1],
        )
    )

    for legend in list(unmatched_legends):
        lg_page = int(legend.get("page", 0) or 0)
        cap_text = str(legend.get("text", "") or "")
        fn = _extract_figure_number(cap_text)
        if fn is None:
            continue
        cap_ns = _extract_figure_namespace(cap_text)
        fig_id = _format_figure_id(cap_ns, fn)

        # Collect candidate groups: prefer same-page, then next-page, then previous-page
        same_page = [g for g in unmatched_groups if g["page"] == lg_page]
        next_page = [g for g in unmatched_groups if g["page"] == lg_page + 1]
        prev_page = [g for g in unmatched_groups if g["page"] == lg_page - 1]

        best_group = None
        if same_page:
            scored = []
            for sg in same_page:
                sg_score = _score_legend_to_group(
                    legend,
                    sg,
                    caption_score=score_figure_caption(
                        legend,
                        nearby_media=True,
                        caption_style_match=False,
                        body_prose_likelihood=False,
                    ),
                    page_width=page_width,
                    page_blocks=_page_blocks_by_page.get(lg_page, []),
                    page_height=_page_height_by_page.get(lg_page, 0.0),
                    page_numbered_legend_count=_numbered_legend_count_by_page.get(lg_page, 0),
                )
                if sg_score.get("decision") == "matched" and sg_score.get("score", 0.0) >= 0.5:
                    scored.append((sg, sg_score.get("score", 0.0)))
            if scored:
                scored.sort(key=lambda x: x[1], reverse=True)
                best_group = scored[0][0]
        if best_group is None and next_page:
            best_group = next_page[0]
        if best_group is None and prev_page:
            first_bid = str(prev_page[0]["asset_block_ids"][0]) if prev_page[0]["asset_block_ids"] else ""
            first_asset = local_asset_by_page_id.get((prev_page[0]["page"], first_bid))
            if first_asset and _allow_previous_page_sequential_match(legend, first_asset):
                best_group = prev_page[0]

        if best_group is None:
            continue

        group_page = int(best_group.get("page", 0) or 0)
        caption_score = score_figure_caption(
            legend,
            nearby_media=True,
            caption_style_match=False,
            body_prose_likelihood=False,
        )

        group_assets = []
        for bid in best_group.get("asset_block_ids", []):
            if bid is None:
                continue
            asset = local_asset_by_page_id.get((group_page, str(bid)))
            if asset:
                group_assets.append(asset)
                ownership.mark_assets_owned([(group_page, str(bid))], owner_id=str(legend.get("block_id", "")), owner_family="figure")

        if not group_assets:
            continue

        matched_figures.append(
            {
                "figure_id": fig_id,
                "figure_namespace": cap_ns,
                "legend_block_id": legend.get("block_id", ""),
                "page": group_page,
                "text": cap_text,
                "figure_number": fn,
                    "matched_assets": [_project_asset_record(a) for a in group_assets],
                "asset_block_ids": [str(a.get("block_id", "")) for a in group_assets],
                "bridge_block_ids": [],
                "group_type": best_group.get("group_type", ""),
                "group_evidence": best_group.get("group_evidence", []) + ["group_sequential_fallback"],
                "cluster_bbox": best_group.get("cluster_bbox", [0, 0, 0, 0]),
                "confidence": 0.45,
                "match_score": {
                    "score": 0.45,
                    "decision": "matched",
                    "evidence": ["group_sequential_fallback"],
                },
                "flags": ["group_sequential_match"],
                "caption_score": caption_score,
                "legend_page": int(legend.get("page", 0) or 0),
                "asset_pages": sorted({int(a.get("page", 0) or 0) for a in group_assets}),
                "settlement_type": "group_sequential",
            }
        )

        ownership.match_group(best_group, owner_id=str(legend.get("block_id", "")), owner_family="figure")
        unmatched_legends.remove(legend)
        ambiguous_figures[:] = [
            af for af in ambiguous_figures if str(af.get("legend_block_id", "")) != str(legend.get("block_id", ""))
        ]

    # === End group-aware fallback ===

    # Sequential fallback: match unmatched captions to remaining assets in reading order.
    # Captions and figures often appear on different pages — humans match them by
    # sequential reading order, not spatial proximity. This is a necessary tradeoff.
    # Run BEFORE cluster building so sequential matching gets first pick of assets.
    # Stage 1 restriction: sequential fallback may not consume assets that belong
    # to any candidate group. Filter the input to the loop but keep unmatched_assets for output.
    _grouped_asset_ids = _grouped_asset_page_ids(candidate_groups)
    _ungrouped_unmatched = [
        ua
        for ua in unmatched_assets
        if (int(ua.get("page", 0) or 0), str(ua.get("block_id", ""))) not in _grouped_asset_ids
    ]
    if unmatched_legends and _ungrouped_unmatched:
        sorted_caps = sorted(
            unmatched_legends,
            key=lambda b: (b.get("page", 0) or 0, (b.get("bbox") or [0, 0, 0, 0])[1]),
        )
        sorted_asts = sorted(
            _ungrouped_unmatched,
            key=lambda b: (b.get("page", 0) or 0, (b.get("bbox") or [0, 0, 0, 0])[1]),
        )
        ai = 0
        seq_matched: list[tuple[dict, dict]] = []
        for cap in sorted_caps:
            cap_text = cap.get("text", "")
            fn = _extract_figure_number(cap_text)
            if fn is None:
                continue
            cap_ns = _extract_figure_namespace(cap_text)

            cp = cap.get("page", 0) or 0
            cap_bbox = cap.get("bbox") or [0, 0, 0, 0]
            cap_top = cap_bbox[1] if len(cap_bbox) >= 4 else 0
            previous_page_asset = None
            future_page_asset = None
            scan_index = ai

            while scan_index < len(sorted_asts):
                asset = sorted_asts[scan_index]
                ap = asset.get("page", 0) or 0
                asset_bid = asset.get("block_id", "")
                if asset_bid and (ap, asset_bid) in used_asset_page_ids:
                    scan_index += 1
                    continue
                if ap == cp - 1:
                    previous_page_asset = asset
                    scan_index += 1
                    continue
                if ap >= cp:
                    if ap > cp:
                        future_page_asset = asset
                    break
                scan_index += 1

            asset = None
            if (
                asset is None
                and previous_page_asset is not None
                and _allow_previous_page_sequential_match(cap, previous_page_asset)
            ):
                asset = previous_page_asset
            if asset is None and future_page_asset is not None:
                fap = future_page_asset.get("page", 0) or 0
                if fap >= cp:
                    asset = future_page_asset
            if asset is None:
                continue

            asset_bid = asset.get("block_id", "")
            asset_page = asset.get("page", 0)
            if asset_bid is not None and (asset_page, asset_bid) in used_asset_page_ids:
                continue
            while ai < len(sorted_asts):
                ai_asset = sorted_asts[ai]
                ai += 1
                if ai_asset.get("block_id", "") == asset_bid and ai_asset.get("page", 0) == asset_page:
                    break
            fig_id = _format_figure_id(cap_ns, fn)
            caption_score = score_figure_caption(
                cap, nearby_media=True, caption_style_match=False, body_prose_likelihood=False
            )
            seq_matched.append((cap, asset))
            matched_figures.append(
                {
                    "figure_id": fig_id,
                    "figure_namespace": cap_ns,
                    "legend_block_id": cap.get("block_id", ""),
                    "page": asset.get("page", 0),
                    "text": cap.get("text", ""),
                    "figure_number": fn,
                    "matched_assets": [_project_asset_record(asset)],
                    "group_type": "",
                    "group_evidence": [],
                    "confidence": 0.35,
                    "match_score": {"score": 0.35, "decision": "matched", "evidence": ["sequential_fallback"]},
                    "flags": ["sequential_match"],
                    "caption_score": caption_score,
                    "legend_page": int(cap.get("page", 0) or 0),
                    "asset_pages": [int(asset.get("page", 0) or 0)],
                    "settlement_type": "sequential",
                }
            )
            if asset_bid:
                ownership.mark_assets_owned([(asset_page, asset_bid)], owner_id=str(cap.get("block_id", "")), owner_family="figure")
        for cap, asset in seq_matched:
            unmatched_legends[:] = [l for l in unmatched_legends if l is not cap]
            if int(asset.get("page", 0) or 0) < int(cap.get("page", 0) or 0):
                ambiguous_figures[:] = [
                    af for af in ambiguous_figures if str(af.get("legend_block_id", "")) != str(cap.get("block_id", ""))
                ]

    # Build unresolved clusters: spatial clusters of unmatched assets on
    # pages where all candidate legends were rejected (multi-panel figures
    # with axis labels or informal captions)
    if rejected_legends and unmatched_assets:
        rejected_pages = {leg.get("page") for leg in rejected_legends if leg.get("page")}
        for cluster in _media_clusters(unmatched_assets, page_width):
            if len(cluster) < 2:
                continue
            cluster_page = cluster[0].get("page", 0)
            if cluster_page not in rejected_pages:
                continue
            cluster_id = f"unresolved_cluster_{len(unresolved_clusters) + 1:03d}"
            cluster_ids = [b.get("block_id", "") for b in cluster]
            unresolved_clusters.append(
                {
                    "cluster_id": cluster_id,
                    "media_block_ids": cluster_ids,
                    "cluster_bbox": _cluster_bbox([b.get("bbox", [0, 0, 0, 0]) for b in cluster]),
                    "page": cluster_page,
                }
            )
        if unresolved_clusters:
            consumed = {bid for uc in unresolved_clusters for bid in uc["media_block_ids"]}
            unmatched_assets = [a for a in unmatched_assets if a.get("block_id", "") not in consumed]

    unmatched_assets = _recompute_final_unmatched_assets(assets, used_asset_page_ids, unresolved_clusters)

    # --- Dense composite parent candidate construction from visual fragments ---
    dense_parents = _build_dense_composite_parent_candidates(
        candidate_groups,
        unresolved_clusters,
        _numbered_pages,
        page_width,
    )
    _composite_parent_by_id = {
        str(parent.get("group_id", "")): parent
        for parent in composite_parent_candidates
        if str(parent.get("group_id", ""))
    }
    for parent in dense_parents:
        parent_id = str(parent.get("group_id", ""))
        if parent_id:
            _composite_parent_by_id[parent_id] = parent
    composite_parent_candidates = list(_composite_parent_by_id.values())

    # --- Dense page unmatched consolidation: on pages where a dense composite
    # parent candidate exists, leftover unmatched assets are grouped into
    # unresolved clusters ---
    _dense_parent_pages = {
        int(p.get("page", 0) or 0)
        for p in composite_parent_candidates
        if p.get("parent_subtype") == "dense_composite"
    }
    if unmatched_assets and _dense_parent_pages:
        for cluster in _media_clusters(unmatched_assets, page_width):
            if len(cluster) < 2:
                continue
            cluster_page = cluster[0].get("page", 0)
            if cluster_page not in _dense_parent_pages:
                continue
            cluster_id = f"unresolved_cluster_dense_{len(unresolved_clusters) + 1:03d}"
            cluster_ids = [b.get("block_id", "") for b in cluster]
            unresolved_clusters.append(
                {
                    "cluster_id": cluster_id,
                    "media_block_ids": cluster_ids,
                    "cluster_bbox": _cluster_bbox([b.get("bbox", [0, 0, 0, 0]) for b in cluster]),
                    "page": cluster_page,
                    "cluster_source": "dense_page_leftovers",
                }
            )
        if unresolved_clusters:
            consumed_dense = {bid for uc in unresolved_clusters for bid in uc["media_block_ids"]}
            unmatched_assets = [a for a in unmatched_assets if a.get("block_id", "") not in consumed_dense]

    # --- P2: page-local caption grammar validation ---
    local_pairing_hypotheses = _validate_page_local_caption_grammar(
        local_pairing_hypotheses, ordered_legends
    )

    # --- Remove blocks already matched to figures from unresolved clusters
    # (clusters are built before matching completes; dedup afterward) ---
    _owned_block_ids: set[tuple[int, str]] = set()
    for mf in matched_figures:
        mf_page = int(mf.get("page", 0) or 0)
        for bid in mf.get("asset_block_ids", []):
            _owned_block_ids.add((mf_page, str(bid)))
    if _owned_block_ids and unresolved_clusters:
        _clean_clusters: list[dict] = []
        for uc in unresolved_clusters:
            uc_page = int(uc.get("page", 0) or 0)
            mids = [str(bid) for bid in uc.get("media_block_ids", [])]
            kept = [bid for bid in mids if (uc_page, bid) not in _owned_block_ids]
            if not kept:
                continue
            uc["media_block_ids"] = kept
            _clean_clusters.append(uc)
        unresolved_clusters = _clean_clusters

    inventory = {
        "figure_legends": deduped_legends,
        "figure_assets": assets,
        "matched_figures": matched_figures,
        "held_figures": held_figures,
        "ambiguous_figures": ambiguous_figures,
        "local_pairing_hypotheses": local_pairing_hypotheses,
        "unmatched_legends": unmatched_legends,
        "unmatched_assets": unmatched_assets,
        "rejected_legends": rejected_legends,
        "unresolved_clusters": unresolved_clusters,
        "deduped_legend_ids": deduped_legend_ids,
        "same_number_distinct_legends": [
            {"figure_number": fn, "page": page, "block_id": str(bid), "reason": "same_number_different_caption_text"}
            for (_ns, fn, page, bid) in _same_number_distinct_keys
        ],
        "composite_parent_candidates": composite_parent_candidates,
        "suppressed_caption_candidates": suppressed_caption_candidates,
        "official_figure_count": len(matched_figures),
    }

    inventory = _promote_sequence_matches(inventory, structured_blocks)

    inventory = _infer_missing_main_figure_numbers(inventory, structured_blocks)

    inventory["figure_legend_completeness"] = compute_figure_legend_completeness(
        structured_blocks,
        inventory,
    )

    _resolve_figure_id_collisions(inventory)

    return inventory


def compute_figure_legend_completeness(
    structured_blocks: list[dict],
    inventory: dict[str, Any],
) -> dict[str, Any]:
    """Verify every numbered formal legend lands in an explicit outcome bucket.

    Outcomes for each numbered formal legend:
      - matched  (figure object emitted)
      - held     (insufficient evidence, waiting)
      - ambiguous (no asset or close-tie, unresolved)
      - unmatched (low caption score, not a formal legend)
      - gap      (no outcome assigned — legend was silently dropped)

    Note: unresolved_cluster is a separate inventory bucket for panel groups
    without legends; it is not a legend-level outcome.

    Returns a dict with total, accounted_for, gap_count, and per-legend
    details.  A gap_count > 0 means a formal legend was silently dropped.
    """
    figure_number_re = re.compile(
        r"(?:Figure|Fig\.?|Supplementary\s+Figure|Supplementary\s+Fig\.?|"
        r"Extended\s+Data\s+Figure|Extended\s+Data\s+Fig\.?)\s+"
        r"(?:S\.?\s*)?(\d+(?:\.\d+)?)",
        flags=re.IGNORECASE,
    )

    # Collect all legend block_ids that entered the pipeline
    legend_ids_in_pipeline: set[str] = set()
    for block in structured_blocks:
        role = block.get("role", "")
        if block.get("_non_body_media") or role == "non_body_insert":
            continue
        if _PANEL_LABEL_PATTERN.match(str(block.get("text", "")).strip()):
            continue
        is_vfc = _is_validation_first_legend_candidate(block)
        if role in ("figure_caption", "figure_caption_candidate") or is_vfc:
            bid = block.get("block_id", "")
            if bid:
                legend_ids_in_pipeline.add(bid)

    # Build lookup sets for each outcome
    matched_ids: set[str] = set()
    for mf in inventory.get("matched_figures", []):
        bid = mf.get("legend_block_id", "")
        if bid:
            matched_ids.add(bid)

    held_ids: set[str] = set()
    for hf in inventory.get("held_figures", []):
        bid = hf.get("legend_block_id", "")
        if bid:
            held_ids.add(bid)

    ambiguous_ids: set[str] = set()
    for af in inventory.get("ambiguous_figures", []):
        bid = af.get("legend_block_id", "")
        if bid:
            ambiguous_ids.add(bid)

    unmatched_ids: set[str] = set()
    for ul in inventory.get("unmatched_legends", []):
        bid = ul.get("block_id", "")
        if bid:
            unmatched_ids.add(bid)

    deduped_ids: set[str] = set()
    for item in inventory.get("deduped_legend_ids", []):
        bid = item.get("block_id", "")
        if bid:
            deduped_ids.add(bid)

    # Check every numbered legend in the pipeline
    details: list[dict[str, Any]] = []
    total = 0
    accounted_for = 0
    gap_count = 0

    for block in structured_blocks:
        role = block.get("role", "")
        if block.get("_non_body_media") or role == "non_body_insert":
            continue
        if _PANEL_LABEL_PATTERN.match(str(block.get("text", "")).strip()):
            continue
        is_vfc = _is_validation_first_legend_candidate(block)
        if role not in ("figure_caption", "figure_caption_candidate") and not is_vfc:
            continue

        text = block.get("text", "")
        m = figure_number_re.search(text)
        if m is None:
            continue

        total += 1
        bid = block.get("block_id", "")
        fig_num = None
        with contextlib.suppress(ValueError):
            fig_num = int(float(m.group(1)))
        fig_ns = _extract_figure_namespace(text)

        if bid in matched_ids:
            status = "matched"
        elif bid in held_ids:
            status = "held"
        elif bid in ambiguous_ids:
            status = "ambiguous"
        elif bid in unmatched_ids:
            status = "unmatched"
        elif bid in deduped_ids:
            status = "deduped_duplicate"
        else:
            status = "gap"

        if status != "gap":
            accounted_for += 1
        else:
            gap_count += 1

        details.append(
            {
                "block_id": bid,
                "figure_number": fig_num,
                "figure_namespace": fig_ns,
                "status": status,
                "page": block.get("page"),
            }
        )

    return {
        "total": total,
        "accounted_for": accounted_for,
        "gap_count": gap_count,
        "details": details,
    }


def _promote_sequence_matches(figure_inventory: dict, blocks: list[dict]) -> dict:
    """Promote eligible ambiguous figures to sequence_match when
    contiguous figure numbers suggest reliable ordering.

    An ambiguous figure with a figure number adjacent to an already-matched
    figure number is promoted to ``matched_figures`` with
    ``strict_status="sequence_match"``.  This is a strict-layer operation;
    the reader layer projects the resulting status.

    Promotion preconditions (spec):
    1. Contiguous figure numbers (no gaps in numbered sequence)
    2. Compatible cluster/media count per figure
    3. Monotonic page/order alignment
    4. No contradiction with existing exact matches

    For this minimal implementation, the simple heuristic is:

      If this ambiguous figure has figure number N and we already have a
      matched figure at N-1 or N+1, it is part of a sequence and is promoted.
    """
    matched = figure_inventory.get("matched_figures", [])
    matched_fig_nums: set[int] = set()
    for mf in matched:
        fn = mf.get("figure_number")
        if fn is not None:
            matched_fig_nums.add(fn)

    ambiguous = figure_inventory.get("ambiguous_figures", [])
    promoted: list[dict] = []

    # Find ambiguous figures that are adjacent to a matched figure number
    remaining_ambiguous: list[dict] = []
    for af in ambiguous:
        fn = af.get("figure_number")
        if fn is None:
            remaining_ambiguous.append(af)
            continue
        if (fn - 1 in matched_fig_nums) or (fn + 1 in matched_fig_nums):
            asset_block_ids = af.get("asset_block_ids", [])
            if not asset_block_ids:
                af["sequence_skip_empty_assets"] = True
                af["hold_reason"] = "assetless_sequence_shell"
                remaining_ambiguous.append(af)
                continue
            matched_assets = list(af.get("matched_assets", []))
            asset_pages = list(af.get("asset_pages", []))
            legend_page = af.get("legend_page", af.get("page"))
            page = af.get("page")
            if not matched_assets or not asset_pages or legend_page is None or page is None:
                af["sequence_skip_incomplete_contract"] = True
                remaining_ambiguous.append(af)
                continue
            ns_promoted = _extract_figure_namespace(af.get("text", ""))
            promoted_entry = {
                "figure_id": _format_figure_id(ns_promoted, fn),
                "figure_namespace": ns_promoted,
                "legend_block_id": af.get("legend_block_id", ""),
                "page": page,
                "legend_page": legend_page,
                "asset_pages": asset_pages,
                "text": af.get("text", ""),
                "figure_number": fn,
                "matched_assets": matched_assets,
                "asset_block_ids": asset_block_ids,
                "group_type": "",
                "group_evidence": [],
                "confidence": 0.0,
                "match_score": {
                    "score": 0.0,
                    "decision": "sequence_match",
                    "evidence": ["sequence_promotion"],
                },
                "flags": ["sequence_match"],
                "caption_score": af.get("caption_score", {}),
                "strict_status": "sequence_match",
                "settlement_type": "sequence_match",
                "zone": af.get("zone"),
                "style_family": af.get("style_family"),
                "marker_signature": af.get("marker_signature") or {},
            }
            promoted.append(promoted_entry)
        else:
            remaining_ambiguous.append(af)

    if promoted:
        figure_inventory["matched_figures"].extend(promoted)
        figure_inventory["ambiguous_figures"] = remaining_ambiguous
        figure_inventory["official_figure_count"] = len(figure_inventory["matched_figures"])

    return figure_inventory


def _collect_figure_owned_asset_ids(figure_inventory: dict) -> set[tuple[int, str]]:
    owned: set[tuple[int, str]] = set()
    for figure in figure_inventory.get("matched_figures", []):
        asset_page = int(figure.get("page", figure.get("legend_page", 0)) or 0)
        for asset in figure.get("matched_assets", []):
            asset_id = _asset_page_id(asset_page, asset.get("block_id", ""))
            if asset_id is not None:
                owned.add(asset_id)
    return owned


def _collect_table_owned_asset_ids(table_inventory: dict) -> set[tuple[int, str]]:
    owned: set[tuple[int, str]] = set()
    for table in table_inventory.get("tables", []):
        if not table.get("has_asset"):
            continue
        asset_id = _asset_page_id(table.get("page", 0), table.get("asset_block_id", ""))
        if asset_id is not None:
            owned.add(asset_id)
    return owned


def _build_ownership_conflicts(figure_inventory: dict, table_inventory: dict) -> list[dict]:
    figure_owned = _collect_figure_owned_asset_ids(figure_inventory)
    table_owned = _collect_table_owned_asset_ids(table_inventory)
    conflicts: list[dict] = []
    for asset_page_id in sorted(figure_owned & table_owned):
        page, block_id = asset_page_id
        conflicts.append(
            {
                "asset_page_id": asset_page_id,
                "page": page,
                "block_id": block_id,
                "conflict_type": "ownership_conflict",
                "owners": ["figure", "table"],
            }
        )
    return conflicts


def attach_ownership_conflicts(figure_inventory: dict, table_inventory: dict) -> None:
    figure_inventory["ownership_conflicts"] = _build_ownership_conflicts(figure_inventory, table_inventory)


def write_back_figure_roles(inventory: dict, structured_blocks: list[dict]) -> None:
    """Update structured block roles for matched figures from media_asset to figure_asset."""
    for figure in inventory.get("matched_figures", []):
        for asset in figure.get("matched_assets", []):
            asset_bid = asset.get("block_id")
            if not asset_bid:
                continue
            for block in structured_blocks:
                if block.get("block_id") == asset_bid and block.get("page") == figure.get("page"):
                    if block.get("role") in {"media_asset", "figure_asset"}:
                        block["role"] = "figure_asset"
                    break


_LEAK_ROLES = {
    "body_paragraph", "section_heading", "subsection_heading",
    "sub_subsection_heading", "backmatter_heading", "backmatter_body",
    "tail_candidate_body", "footnote", "structured_insert",
    "non_body_insert", "frontmatter_noise",
}


def _cluster_bboxes_by_proximity(
    bboxes: list[list[float]],
    margin: float = 40,
) -> list[list[float]]:
    if not bboxes:
        return []
    if len(bboxes) == 1:
        return [list(bboxes[0])]
    n = len(bboxes)
    parent = list(range(n))

    def _find(x: int) -> int:
        while parent[x] != x:
            parent[x] = parent[parent[x]]
            x = parent[x]
        return x

    def _union(x: int, y: int) -> None:
        px, py = _find(x), _find(y)
        if px != py:
            parent[py] = px

    for i in range(n):
        ax1, ay1, ax2, ay2 = bboxes[i]
        for j in range(i + 1, n):
            bx1, by1, bx2, by2 = bboxes[j]
            if (max(ax1, bx1) - margin < min(ax2, bx2) + margin
                    and max(ay1, by1) - margin < min(ay2, by2) + margin):
                _union(i, j)

    clusters: dict[int, list[list[float]]] = {}
    for i in range(n):
        clusters.setdefault(_find(i), []).append(bboxes[i])
    return [
        [min(b[0] for b in g), min(b[1] for b in g),
         max(b[2] for b in g), max(b[3] for b in g)]
        for g in clusters.values()
    ]


def _is_contained(block_bbox: list[float], region_bbox: list[float]) -> bool:
    bx1, by1, bx2, by2 = block_bbox
    rx1, ry1, rx2, ry2 = region_bbox
    block_w = bx2 - bx1
    region_w = rx2 - rx1
    if block_w > region_w * 0.95:
        return False
    cx = (bx1 + bx2) / 2
    cy = (by1 + by2) / 2
    if not (rx1 <= cx <= rx2 and ry1 <= cy <= ry2):
        return False
    ix1 = max(bx1, rx1)
    iy1 = max(by1, ry1)
    ix2 = min(bx2, rx2)
    iy2 = min(by2, ry2)
    overlap = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
    block_area = max(1.0, block_w * (by2 - by1))
    return overlap / block_area >= 0.85


def _highly_overlaps_any_matched_region(
    fallback_bbox: list[float],
    figure_regions: list[tuple[str, list[float]]],
) -> bool:
    fx1, fy1, fx2, fy2 = fallback_bbox
    fallback_area = max(1.0, (fx2 - fx1) * (fy2 - fy1))
    for tag, region_bbox in figure_regions:
        if tag != "matched":
            continue
        rx1, ry1, rx2, ry2 = region_bbox
        ix1 = max(fx1, rx1)
        iy1 = max(fy1, ry1)
        ix2 = min(fx2, rx2)
        iy2 = min(fy2, ry2)
        overlap = max(0.0, ix2 - ix1) * max(0.0, iy2 - iy1)
        if overlap / fallback_area > 0.5:
            return True
    return False


def _figure_region_bbox(mf: dict) -> list[float] | None:
    cluster = mf.get("cluster_bbox")
    if cluster and len(cluster) >= 4:
        return cluster
    asset = mf.get("asset_bbox")
    if asset and len(asset) >= 4:
        return asset
    matched = mf.get("matched_assets", [])
    bboxes = [a["bbox"] for a in matched if len(a.get("bbox") or []) >= 4]
    if bboxes:
        return _cluster_bbox(bboxes)
    return None


def _matched_asset_keys(mf: dict) -> set[tuple[int, str]]:
    keys: set[tuple[int, str]] = set()
    page = int(mf.get("page", 0) or 0)
    for asset in mf.get("matched_assets", []):
        bid = asset.get("block_id")
        if bid is not None:
            keys.add((page, str(bid)))
    for bid in mf.get("asset_block_ids", []):
        if bid is not None:
            keys.add((page, str(bid)))
    return keys


def tag_figure_contained_text(
    blocks: list[dict],
    matched_figures: list[dict],
) -> None:
    pages = {b.get("page") for b in blocks if b.get("page") is not None}
    matched_by_page: dict[int, list[dict]] = {}
    for mf in matched_figures:
        p = mf.get("page")
        if p is not None:
            matched_by_page.setdefault(p, []).append(mf)
    for page in sorted(pages):
        page_blocks = [b for b in blocks if b.get("page") == page]
        figure_regions: list[tuple[str, list[float]]] = []
        covered_asset_keys: set[tuple[int, str]] = set()
        for mf in matched_by_page.get(page, []):
            region = _figure_region_bbox(mf)
            if region:
                figure_regions.append(("matched", region))
                covered_asset_keys |= _matched_asset_keys(mf)
        fallback_assets = [
            b for b in page_blocks
            if (int(b.get("page", 0) or 0), str(b.get("block_id", ""))) not in covered_asset_keys
            and b.get("role") in {"figure_asset", "media_asset"}
            and b.get("role") not in {"table_html", "table_asset"}
            and (
                b.get("asset_family_hint") == "figure_like"
                or str(b.get("raw_label", "") or "") in {"image", "chart", "figure_title", "figure"}
            )
        ]
        if fallback_assets:
            fallback_bboxes = [b["bbox"] for b in fallback_assets if len(b.get("bbox") or []) >= 4]
            for fr in _cluster_bboxes_by_proximity(fallback_bboxes, margin=40):
                if not _highly_overlaps_any_matched_region(fr, figure_regions):
                    figure_regions.append(("fallback", fr))
        if not figure_regions:
            continue
        for block in page_blocks:
            role = str(block.get("role") or "")
            if role in {"figure_asset", "media_asset", "noise",
                        "figure_caption", "figure_caption_candidate",
                        "table_html", "table_asset",
                        "figure_inner_text"}:
                continue
            bbox = block.get("bbox")
            if not bbox or len(bbox) < 4:
                continue
            for _, fr in figure_regions:
                if _is_contained(bbox, fr):
                    block["_figure_contained"] = True
                    if role in _LEAK_ROLES:
                        block["role"] = "figure_inner_text"
                    break


def write_figure_inventory(dst: Path, inventory: dict[str, Any]) -> None:
    write_json(dst, inventory)