#!/usr/bin/env python3
"""Build the context-free SDIL project introduction deck.

All quantitative claims come from tracked project result files. The deck uses
only completed full-method experiments: the controlled traffic panel, the
standard ResNet-20/32/56 panel, and the calibrated synthetic BCI confirmation.
"""

from pathlib import Path
from typing import Iterable, Sequence

from PIL import Image
from pptx import Presentation
from pptx.dml.color import RGBColor
from pptx.enum.shapes import MSO_CONNECTOR, MSO_SHAPE
from pptx.enum.text import MSO_ANCHOR, PP_ALIGN
from pptx.util import Inches, Pt


ROOT = Path(__file__).resolve().parents[1]
OUT_DIR = ROOT / "slides"
ASSET_DIR = OUT_DIR / "assets"
OUT_PATH = OUT_DIR / "SDIL_project_intro.pptx"

FIG3 = ROOT / "results/figs/figure3_innovation.png"
FIG5 = ROOT / "results/figs/figure5_bci_v2.png"

SLIDE_W = 13.333
SLIDE_H = 7.5

FONT = "Noto Sans CJK SC"
FONT_LATIN = "DejaVu Sans"

BG = "F7F9FB"
PAPER = "FFFFFF"
INK = "17212B"
MUTED = "5D6975"
LIGHT = "DDE4EA"
GRID = "D9E0E6"
BLUE = "0072B2"
BLUE_LIGHT = "DCEFF9"
GREEN = "009E73"
GREEN_LIGHT = "DDF3EC"
ORANGE = "E69F00"
ORANGE_LIGHT = "FBEBC9"
RED = "D55E00"
GRAY = "777777"
DARK_GRAY = "3F4850"


def rgb(hex_color: str) -> RGBColor:
    return RGBColor.from_string(hex_color)


def add_text(
    slide,
    text: str,
    x: float,
    y: float,
    w: float,
    h: float,
    *,
    size: float = 18,
    color: str = INK,
    bold: bool = False,
    align=PP_ALIGN.LEFT,
    valign=MSO_ANCHOR.TOP,
    margin: float = 0.03,
    font: str = FONT,
    line_spacing: float = 1.0,
):
    box = slide.shapes.add_textbox(Inches(x), Inches(y), Inches(w), Inches(h))
    tf = box.text_frame
    tf.clear()
    tf.word_wrap = True
    tf.margin_left = Inches(margin)
    tf.margin_right = Inches(margin)
    tf.margin_top = Inches(margin)
    tf.margin_bottom = Inches(margin)
    tf.vertical_anchor = valign
    p = tf.paragraphs[0]
    p.alignment = align
    p.line_spacing = line_spacing
    run = p.add_run()
    run.text = text
    run.font.name = font
    run.font.size = Pt(size)
    run.font.bold = bold
    run.font.color.rgb = rgb(color)
    return box


def add_rich_text(
    slide,
    runs: Sequence[tuple[str, float, str, bool]],
    x: float,
    y: float,
    w: float,
    h: float,
    *,
    align=PP_ALIGN.LEFT,
    valign=MSO_ANCHOR.TOP,
    margin: float = 0.03,
):
    box = slide.shapes.add_textbox(Inches(x), Inches(y), Inches(w), Inches(h))
    tf = box.text_frame
    tf.clear()
    tf.word_wrap = True
    tf.margin_left = Inches(margin)
    tf.margin_right = Inches(margin)
    tf.margin_top = Inches(margin)
    tf.margin_bottom = Inches(margin)
    tf.vertical_anchor = valign
    p = tf.paragraphs[0]
    p.alignment = align
    for text, size, color, bold in runs:
        run = p.add_run()
        run.text = text
        run.font.name = FONT
        run.font.size = Pt(size)
        run.font.bold = bold
        run.font.color.rgb = rgb(color)
    return box


def add_bullets(
    slide,
    items: Iterable[str],
    x: float,
    y: float,
    w: float,
    h: float,
    *,
    size: float = 17,
    color: str = INK,
    bullet_color: str = BLUE,
    gap: float = 7,
):
    box = slide.shapes.add_textbox(Inches(x), Inches(y), Inches(w), Inches(h))
    tf = box.text_frame
    tf.clear()
    tf.word_wrap = True
    tf.margin_left = Inches(0.03)
    tf.margin_right = Inches(0.03)
    tf.margin_top = Inches(0.02)
    tf.margin_bottom = Inches(0.02)
    for idx, item in enumerate(items):
        p = tf.paragraphs[0] if idx == 0 else tf.add_paragraph()
        p.level = 0
        p.space_after = Pt(gap)
        p.line_spacing = 1.05
        p.text = ""
        marker = p.add_run()
        marker.text = "●  "
        marker.font.name = FONT
        marker.font.size = Pt(max(size - 4, 10))
        marker.font.color.rgb = rgb(bullet_color)
        body = p.add_run()
        body.text = item
        body.font.name = FONT
        body.font.size = Pt(size)
        body.font.color.rgb = rgb(color)
    return box


def add_rect(
    slide,
    x: float,
    y: float,
    w: float,
    h: float,
    *,
    fill: str = PAPER,
    line: str = LIGHT,
    radius: bool = True,
    line_width: float = 1.0,
):
    kind = MSO_SHAPE.ROUNDED_RECTANGLE if radius else MSO_SHAPE.RECTANGLE
    shape = slide.shapes.add_shape(kind, Inches(x), Inches(y), Inches(w), Inches(h))
    shape.fill.solid()
    shape.fill.fore_color.rgb = rgb(fill)
    shape.line.color.rgb = rgb(line)
    shape.line.width = Pt(line_width)
    return shape


def add_line(
    slide,
    x1: float,
    y1: float,
    x2: float,
    y2: float,
    *,
    color: str = MUTED,
    width: float = 1.5,
    dash=None,
):
    line = slide.shapes.add_connector(
        MSO_CONNECTOR.STRAIGHT,
        Inches(x1),
        Inches(y1),
        Inches(x2),
        Inches(y2),
    )
    line.line.color.rgb = rgb(color)
    line.line.width = Pt(width)
    if dash is not None:
        line.line.dash_style = dash
    return line


def add_arrow(slide, x1, y1, x2, y2, *, color=MUTED, width=2.0):
    line = add_line(slide, x1, y1, x2, y2, color=color, width=width)
    line.line.end_arrowhead = True
    return line


def add_circle(slide, cx, cy, r, *, fill=BLUE, line=PAPER, line_width=1.0):
    shape = slide.shapes.add_shape(
        MSO_SHAPE.OVAL,
        Inches(cx - r),
        Inches(cy - r),
        Inches(2 * r),
        Inches(2 * r),
    )
    shape.fill.solid()
    shape.fill.fore_color.rgb = rgb(fill)
    shape.line.color.rgb = rgb(line)
    shape.line.width = Pt(line_width)
    return shape


def add_title(slide, title: str, number: int, subtitle: str | None = None):
    add_text(slide, title, 0.55, 0.28, 11.9, 0.54, size=26, bold=True)
    add_text(slide, f"{number:02d}", 12.35, 0.34, 0.45, 0.3, size=11, color=MUTED, align=PP_ALIGN.RIGHT)
    add_line(slide, 0.55, 0.91, 12.78, 0.91, color=LIGHT, width=1.0)
    if subtitle:
        add_text(slide, subtitle, 0.58, 1.00, 12.0, 0.35, size=13.5, color=MUTED)


def add_footer(slide, text: str):
    add_text(slide, text, 0.58, 7.18, 12.15, 0.2, size=8.5, color=MUTED, valign=MSO_ANCHOR.MIDDLE)


def add_badge(slide, text, x, y, w, *, fill=BLUE_LIGHT, color=BLUE, size=13):
    add_rect(slide, x, y, w, 0.43, fill=fill, line=fill, radius=True)
    add_text(slide, text, x + 0.05, y + 0.02, w - 0.1, 0.35, size=size, color=color, bold=True, align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)


def prepare_crops():
    ASSET_DIR.mkdir(parents=True, exist_ok=True)
    img = Image.open(FIG5)
    w, h = img.size
    crops = {
        "figure5a_actor_critic.png": (0, 0, w // 2, h // 2),
        "figure5d_outcome.png": (w // 2, h // 2, w, h),
    }
    for name, box in crops.items():
        img.crop(box).save(ASSET_DIR / name)


def add_custom_line_chart(
    slide,
    x: float,
    y: float,
    w: float,
    h: float,
    categories: Sequence[str],
    series: Sequence[tuple[str, Sequence[float], str]],
    *,
    ymin: float,
    ymax: float,
    yticks: Sequence[float],
    title: str,
    show_legend: bool = True,
):
    add_rect(slide, x, y, w, h, fill=PAPER, line=LIGHT, radius=True)
    add_text(slide, title, x + 0.25, y + 0.12, w - 0.5, 0.35, size=15, bold=True)
    left = x + 0.60
    right = x + w - 0.25
    top = y + 0.72
    bottom = y + h - 0.65

    def px(i):
        if len(categories) == 1:
            return (left + right) / 2
        return left + i * (right - left) / (len(categories) - 1)

    def py(v):
        return bottom - (v - ymin) * (bottom - top) / (ymax - ymin)

    for tick in yticks:
        yy = py(tick)
        add_line(slide, left, yy, right, yy, color=GRID, width=0.8)
        add_text(slide, f"{tick:g}", x + 0.08, yy - 0.12, 0.42, 0.24, size=9.5, color=MUTED, align=PP_ALIGN.RIGHT, valign=MSO_ANCHOR.MIDDLE)

    add_line(slide, left, top, left, bottom, color=DARK_GRAY, width=1.1)
    add_line(slide, left, bottom, right, bottom, color=DARK_GRAY, width=1.1)
    for i, cat in enumerate(categories):
        xx = px(i)
        add_line(slide, xx, bottom, xx, bottom + 0.07, color=DARK_GRAY, width=1.0)
        add_text(slide, cat, xx - 0.38, bottom + 0.10, 0.76, 0.28, size=9.5, align=PP_ALIGN.CENTER)

    for name, values, color in series:
        points = [(px(i), py(v)) for i, v in enumerate(values)]
        for (x1, y1), (x2, y2) in zip(points, points[1:]):
            add_line(slide, x1, y1, x2, y2, color=color, width=2.3)
        for xx, yy in points:
            add_circle(slide, xx, yy, 0.065, fill=color, line=PAPER, line_width=0.8)

    if show_legend:
        legend_y = y + h - 0.27
        total = len(series)
        slot = (w - 0.55) / total
        for i, (name, _, color) in enumerate(series):
            lx = x + 0.30 + i * slot
            add_line(slide, lx, legend_y + 0.08, lx + 0.26, legend_y + 0.08, color=color, width=2.3)
            add_circle(slide, lx + 0.13, legend_y + 0.08, 0.04, fill=color, line=color)
            add_text(slide, name, lx + 0.32, legend_y - 0.04, slot - 0.34, 0.25, size=9.2, color=INK, valign=MSO_ANCHOR.MIDDLE)


def build_deck():
    prepare_crops()
    prs = Presentation()
    prs.slide_width = Inches(SLIDE_W)
    prs.slide_height = Inches(SLIDE_H)
    blank = prs.slide_layouts[6]

    def new_slide():
        slide = prs.slides.add_slide(blank)
        background = slide.background
        background.fill.solid()
        background.fill.fore_color.rgb = rgb(BG)
        return slide

    # Slide 1: title and one-minute summary.
    slide = new_slide()
    add_text(slide, "SDIL", 0.62, 0.60, 3.0, 0.72, size=38, color=BLUE, bold=True)
    add_text(slide, "让局部学习使用意外的反馈", 0.62, 1.28, 7.1, 0.70, size=29, bold=True)
    add_text(
        slide,
        "Somato-Dendritic Innovation Learning\n受 Francioni et al.（Harnett lab）Nature 2026 启发",
        0.65,
        2.10,
        6.5,
        0.88,
        size=16,
        color=MUTED,
        line_spacing=1.08,
    )

    add_rect(slide, 7.55, 0.72, 5.05, 2.42, fill=PAPER, line=LIGHT, radius=True, line_width=1.2)
    add_text(slide, "核心操作", 7.90, 1.02, 1.4, 0.35, size=14, color=MUTED, bold=True)
    add_text(slide, "teaching signal", 7.92, 1.48, 1.75, 0.34, size=16, color=DARK_GRAY, align=PP_ALIGN.RIGHT)
    add_text(slide, "=", 9.76, 1.45, 0.35, 0.40, size=22, color=MUTED, bold=True, align=PP_ALIGN.CENTER)
    add_text(slide, "apical feedback", 10.12, 1.31, 2.05, 0.33, size=15.5, color=INK, bold=True, align=PP_ALIGN.CENTER)
    add_text(slide, "− expected from soma", 10.12, 1.75, 2.05, 0.33, size=15.5, color=BLUE, bold=True, align=PP_ALIGN.CENTER)
    add_text(slide, "每个神经元各自减去正常 soma–dendrite 耦合", 7.90, 2.43, 4.25, 0.38, size=13.5, color=MUTED, align=PP_ALIGN.CENTER)

    cards = [
        ("BP-free", "学习器内无反向计算图", BLUE_LIGHT, BLUE),
        ("92.76%", "ResNet-56 + 4× predictable traffic", GREEN_LIGHT, GREEN),
        ("1.33×", "BP MAC estimate", ORANGE_LIGHT, RED),
    ]
    for i, (big, small, fill, color) in enumerate(cards):
        x = 0.65 + i * 4.12
        add_rect(slide, x, 4.17, 3.75, 1.47, fill=fill, line=fill, radius=True)
        add_text(slide, big, x + 0.18, 4.36, 3.39, 0.48, size=25, color=color, bold=True, align=PP_ALIGN.CENTER)
        add_text(slide, small, x + 0.18, 4.91, 3.39, 0.38, size=12.3, color=INK, align=PP_ALIGN.CENTER)
    add_text(
        slide,
        "一句话：可扩展的局部信用路径负责把方向送到各层；SDIL 负责从混合反馈中提取可用于学习的部分。",
        0.82,
        6.30,
        11.7,
        0.50,
        size=18,
        color=INK,
        bold=True,
        align=PP_ALIGN.CENTER,
    )
    add_footer(slide, "Project introduction · ICLR 2027 work in progress · headline values use completed full experiments")

    # Slide 2: scientific origin and problem.
    slide = new_slide()
    add_title(slide, "问题：反馈通道不只传教学信号", 2, "把全部 apical activity 当作 error，会把正常状态与上下文一起写入权重。")

    add_rect(slide, 0.62, 1.52, 5.70, 4.88, fill=PAPER, line=LIGHT)
    add_text(slide, "局部学习常见假设", 0.95, 1.78, 2.6, 0.38, size=17, bold=True)
    add_rect(slide, 0.98, 2.42, 1.55, 0.76, fill=BLUE_LIGHT, line=BLUE)
    add_text(slide, "teaching\nsignal", 1.10, 2.52, 1.31, 0.50, size=14, color=BLUE, bold=True, align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)
    add_rect(slide, 0.98, 3.62, 1.55, 0.76, fill="EFF1F3", line=GRAY)
    add_text(slide, "ordinary\ntraffic", 1.10, 3.72, 1.31, 0.50, size=14, color=DARK_GRAY, bold=True, align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)
    add_arrow(slide, 2.55, 2.80, 3.55, 3.25, color=BLUE, width=2.1)
    add_arrow(slide, 2.55, 4.00, 3.55, 3.55, color=GRAY, width=2.1)
    add_rect(slide, 3.58, 2.72, 2.10, 1.36, fill="F3F5F6", line=DARK_GRAY)
    add_text(slide, "raw apical\nfeedback", 3.76, 2.93, 1.74, 0.72, size=18, bold=True, align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)
    add_arrow(slide, 4.63, 4.10, 4.63, 4.85, color=RED, width=2.4)
    add_rect(slide, 3.48, 4.90, 2.30, 0.84, fill="F8E6DE", line=RED)
    add_text(slide, "update direction\nmay rotate", 3.65, 5.04, 1.96, 0.52, size=14.2, color=RED, bold=True, align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)

    add_rect(slide, 6.58, 1.52, 6.12, 4.88, fill=PAPER, line=LIGHT)
    add_text(slide, "Nature 2026 提供了更具体的对象", 6.92, 1.78, 4.8, 0.38, size=17, bold=True)
    # Simple soma-dendrite regression sketch.
    plot_x, plot_y, plot_w, plot_h = 7.08, 2.60, 2.35, 2.30
    add_line(slide, plot_x, plot_y + plot_h, plot_x + plot_w, plot_y + plot_h, color=DARK_GRAY, width=1.2)
    add_line(slide, plot_x, plot_y + plot_h, plot_x, plot_y, color=DARK_GRAY, width=1.2)
    add_line(slide, plot_x + 0.20, plot_y + 1.97, plot_x + 2.12, plot_y + 0.35, color=GRAY, width=2.0)
    points = [(0.35, 1.74), (0.72, 1.41), (1.03, 1.29), (1.42, 0.88), (1.82, 0.61)]
    for dx, dy in points:
        add_circle(slide, plot_x + dx, plot_y + dy, 0.055, fill=GRAY, line=PAPER)
    expected_x = plot_x + 1.45
    expected_y = plot_y + 0.90
    observed_y = plot_y + 0.35
    add_circle(slide, expected_x, observed_y, 0.075, fill=BLUE, line=PAPER)
    add_line(slide, expected_x, expected_y, expected_x, observed_y, color=BLUE, width=2.8)
    add_text(slide, "residual", expected_x + 0.12, observed_y + 0.10, 0.80, 0.25, size=11.5, color=BLUE, bold=True)
    add_text(slide, "soma", plot_x + 0.88, plot_y + plot_h + 0.18, 0.80, 0.25, size=10.5, color=MUTED, align=PP_ALIGN.CENTER)
    add_text(slide, "dendrite", plot_x - 0.68, plot_y + 0.93, 0.70, 0.25, size=10.5, color=MUTED, align=PP_ALIGN.CENTER)

    add_bullets(
        slide,
        [
            "先拟合每个神经元正常的 soma–dendrite 关系",
            "残差与 soma 明显去相关",
            "残差携带 outcome 与神经元特异的有符号任务信息",
        ],
        9.75,
        2.56,
        2.62,
        2.60,
        size=14.3,
        gap=8,
    )
    add_rect(slide, 6.93, 5.34, 5.42, 0.68, fill=BLUE_LIGHT, line=BLUE_LIGHT)
    add_text(slide, "算法问题：学习是否也应使用 residual，而不是 raw activity？", 7.10, 5.50, 5.08, 0.33, size=15.2, color=BLUE, bold=True, align=PP_ALIGN.CENTER)
    add_footer(slide, "Source: Francioni et al., “Vectorized instructive signals in cortical dendrites,” Nature (2026).")

    # Slide 3: method.
    slide = new_slide()
    add_title(slide, "方法：预测正常耦合，再用残差完成局部更新", 3, "SDIL 改变教学变量；它不要求重新发明整条反馈传播路径。")

    steps = [
        ("1", "Neutral observation", "âₗ = Pₗ(hₗ)", "估计同一神经元的正常耦合", "EFF1F3", DARK_GRAY),
        ("2", "Innovation", "rₗ = aₗ − âₗ", "只保留 soma 无法预测的部分", BLUE_LIGHT, BLUE),
        ("3", "Local plasticity", "ΔWₗ = η(rₗ ⊙ φ′(uₗ))hₗ₋₁ᵀ", "pre × post gain × dendritic residual", GREEN_LIGHT, GREEN),
    ]
    for i, (num, label, eq, desc, fill, color) in enumerate(steps):
        x = 0.67 + i * 4.18
        add_rect(slide, x, 1.70, 3.76, 2.38, fill=fill, line=color, radius=True, line_width=1.4)
        add_circle(slide, x + 0.35, 2.04, 0.20, fill=color, line=color)
        add_text(slide, num, x + 0.23, 1.90, 0.24, 0.28, size=13, color=PAPER, bold=True, align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE)
        add_text(slide, label, x + 0.68, 1.83, 2.72, 0.36, size=16, color=color, bold=True)
        add_text(slide, eq, x + 0.22, 2.48, 3.32, 0.53, size=19, color=INK, bold=True, align=PP_ALIGN.CENTER, valign=MSO_ANCHOR.MIDDLE, font=FONT_LATIN)
        add_text(slide, desc, x + 0.30, 3.34, 3.16, 0.43, size=13.5, color=MUTED, align=PP_ALIGN.CENTER)
        if i < 2:
            add_arrow(slide, x + 3.82, 2.86, x + 4.10, 2.86, color=MUTED, width=2.0)

    add_rect(slide, 0.67, 4.42, 5.96, 2.02, fill=PAPER, line=LIGHT)
    add_text(slide, "学习器内部为什么是 BP-free", 0.98, 4.68, 3.2, 0.35, size=17, bold=True)
    add_bullets(
        slide,
        [
            "更新只读取 pre-synaptic activity、local gain 和本细胞 residual",
            "无 reverse-mode autograd；不复制 forward-weight transpose",
        ],
        0.98,
        5.12,
        5.25,
        1.12,
        size=13.5,
        gap=5,
    )

    add_rect(slide, 6.88, 4.42, 5.78, 2.02, fill=PAPER, line=LIGHT)
    add_text(slide, "信用路径是可替换的已有组件", 7.19, 4.68, 3.8, 0.35, size=17, bold=True)
    add_bullets(
        slide,
        [
            "Small nets: node-perturbation feedback vectorizer",
            "ResNet: reciprocal KP plasticity",
            "两者均为已有组件；SDIL 的贡献是 residualization",
        ],
        7.19,
        5.10,
        5.06,
        1.18,
        size=13.2,
        bullet_color=GREEN,
        gap=3,
    )
    add_footer(slide, "Credit substrates: Lansdell et al. (ICLR 2020); reciprocal plasticity: Akrout et al. (NeurIPS 2019).")

    # Slide 4: controlled causal evidence.
    slide = new_slide()
    add_title(slide, "关键消融：减法改变了方向，不只是信号大小", 4, "强 soma-predictable traffic 下，raw 与 norm-matched raw 都失效；innovation 保留学习方向。")
    slide.shapes.add_picture(str(FIG3), Inches(0.40), Inches(1.38), width=Inches(12.52), height=Inches(4.89))
    add_rect(slide, 1.02, 6.36, 11.25, 0.57, fill=BLUE_LIGHT, line=BLUE_LIGHT)
    add_rich_text(
        slide,
        [
            ("ρ = 0.5：", 15, INK, True),
            ("raw 10.38%", 15, DARK_GRAY, True),
            ("  ·  norm-matched raw 10.31%", 15, ORANGE, True),
            ("  ·  innovation 97.35%", 15, BLUE, True),
            ("  （5 seeds）", 13, MUTED, False),
        ],
        1.18,
        6.47,
        10.92,
        0.33,
        align=PP_ALIGN.CENTER,
        valign=MSO_ANCHOR.MIDDLE,
    )
    add_footer(slide, "Controlled MNIST traffic intervention. Norm matching uses the same per-example update magnitude as innovation.")

    # Slide 5: standard-scale evidence.
    slide = new_slide()
    add_title(slide, "标准 ResNet：扩展性来自 KP，抗混合流量来自 SDIL", 5, "60 个 CIFAR-10 endpoints；每个深度 5 seeds，200 epochs，无 depth-specific tuning。")

    categories = ["R20", "R32", "R56"]
    bp = [91.624, 92.302, 92.632]
    dfa = [31.878, 32.684, 30.850]
    kp = [91.388, 92.332, 92.670]
    sdil = [91.584, 92.254, 92.760]
    add_custom_line_chart(
        slide,
        0.62,
        1.48,
        5.92,
        4.82,
        categories,
        [("BP", bp, DARK_GRAY), ("DFA", dfa, ORANGE), ("clean KP", kp, GREEN), ("SDIL + traffic", sdil, BLUE)],
        ymin=25,
        ymax=95,
        yticks=[30, 50, 70, 90],
        title="完整尺度：固定 DFA 随深度仍处于约 31%",
    )
    add_custom_line_chart(
        slide,
        6.80,
        1.48,
        5.92,
        4.82,
        categories,
        [("BP", bp, DARK_GRAY), ("clean KP", kp, GREEN), ("SDIL + 4× traffic", sdil, BLUE)],
        ymin=91.0,
        ymax=93.0,
        yticks=[91.0, 91.5, 92.0, 92.5, 93.0],
        title="近 BP 放大：SDIL 在 4× traffic 下跟随 clean KP",
    )
    add_badge(slide, "SDIL: 91.58 → 92.76%", 0.88, 6.45, 3.42, fill=BLUE_LIGHT, color=BLUE)
    add_badge(slide, "1.31–1.33× BP MAC", 4.48, 6.45, 3.42, fill=ORANGE_LIGHT, color=RED)
    add_badge(slide, "0 loss queries · 1 neutral obs/example", 8.08, 6.45, 4.25, fill=GREEN_LIGHT, color=GREEN, size=12.2)
    add_footer(slide, "Source: complete 60-record ResNet panel. SDIL-specific claim is robustness under traffic, not clean superiority over KP.")

    # Slide 6: dynamical task evidence.
    slide = new_slide()
    add_title(slide, "动态任务：residual 携带 outcome surprise，而不只是分类误差", 6, "独立的 synthetic BCI actor–critic：6 task clusters × 5 models，所有学习更新均为手写局部规则。")
    p_a = ASSET_DIR / "figure5a_actor_critic.png"
    p_d = ASSET_DIR / "figure5d_outcome.png"
    slide.shapes.add_picture(str(p_a), Inches(0.57), Inches(1.46), width=Inches(5.95), height=Inches(4.42))
    slide.shapes.add_picture(str(p_d), Inches(6.80), Inches(1.46), width=Inches(5.95), height=Inches(4.42))
    add_badge(slide, "100% final task success", 0.93, 6.07, 3.44, fill=BLUE_LIGHT, color=BLUE)
    add_badge(slide, "99.83% terminal outcome decoding", 4.73, 6.07, 3.82, fill=GREEN_LIGHT, color=GREEN, size=12.4)
    add_badge(slide, "outcome lesion: −0.400 separation", 8.92, 6.07, 3.50, fill=ORANGE_LIGHT, color=RED, size=12.0)
    add_text(slide, "范围：这是机制验证用的合成任务；terminal reward 被直接提供，不是皮层数据。", 0.82, 6.66, 11.70, 0.32, size=13.5, color=MUTED, align=PP_ALIGN.CENTER)
    add_footer(slide, "Source: complete untouched calibrated BCI confirmation; outcome labels and exact roles are diagnostic-only.")

    # Slide 7: positioning and next decisive evidence.
    slide = new_slide()
    add_title(slide, "当前最准确的定位：可扩展 local credit 的混合信号分离模块", 7)

    # Pipeline strip.
    pipeline = [
        ("existing credit path", "KP / learned feedback", "EFF1F3", DARK_GRAY, 0.66, 2.28),
        ("mixed apical channel", "instruction + ordinary traffic", ORANGE_LIGHT, RED, 3.33, 2.76),
        ("SDIL", "subtract soma-predictable part", BLUE_LIGHT, BLUE, 6.52, 2.52),
        ("local update", "eligibility × innovation", GREEN_LIGHT, GREEN, 9.48, 2.80),
    ]
    for i, (head, sub, fill, color, x, w) in enumerate(pipeline):
        add_rect(slide, x, 1.35, w, 0.98, fill=fill, line=color, radius=True, line_width=1.2)
        add_text(slide, head, x + 0.12, 1.51, w - 0.24, 0.28, size=14.2, color=color, bold=True, align=PP_ALIGN.CENTER)
        add_text(slide, sub, x + 0.12, 1.87, w - 0.24, 0.24, size=10.8, color=INK, align=PP_ALIGN.CENTER)
        if i < len(pipeline) - 1:
            next_x = pipeline[i + 1][4]
            add_arrow(slide, x + w + 0.06, 1.84, next_x - 0.07, 1.84, color=MUTED, width=1.8)

    columns = [
        (0.66, 3.05, 3.80, "已经建立", BLUE, BLUE_LIGHT, [
            "可预测 traffic 会旋转 raw local update",
            "per-neuron residualization 在受控干扰下是 load-bearing 的",
            "组合方法在 ResNet-20/32/56 上保持近 BP accuracy",
        ]),
        (4.76, 3.05, 3.80, "真正的新东西", GREEN, GREEN_LIGHT, [
            "把 Harnett residual 直接定义成 teaching variable",
            "neutral-period predictor + dynamic local projection",
            "机制、方向、成本和动态 outcome 的联合证据",
        ]),
        (8.86, 3.05, 3.80, "仍然缺少", RED, ORANGE_LIGHT, [
            "自然任务中不可预先测量的 mixed feedback",
            "真实硬件或真实神经数据上的必要性",
            "证明 SDIL 的价值不能由 clean KP 单独解释",
        ]),
    ]
    for x, y, w, head, color, fill, items in columns:
        add_rect(slide, x, y, w, 2.68, fill=PAPER, line=LIGHT)
        add_rect(slide, x, y, w, 0.56, fill=fill, line=fill)
        add_text(slide, head, x + 0.18, y + 0.10, w - 0.36, 0.32, size=16, color=color, bold=True, align=PP_ALIGN.CENTER)
        add_bullets(slide, items, x + 0.25, y + 0.78, w - 0.50, 1.70, size=13.2, bullet_color=color, gap=7)

    add_rect(slide, 1.06, 6.10, 11.20, 0.67, fill=BLUE_LIGHT, line=BLUE_LIGHT)
    add_text(
        slide,
        "下一项决定性证据：在自然或硬件产生的 state-dependent mixed traffic 中，raw credit 失败，而 SDIL 无需 oracle calibration 即可恢复。",
        1.27,
        6.25,
        10.78,
        0.34,
        size=14.6,
        color=BLUE,
        bold=True,
        align=PP_ALIGN.CENTER,
    )
    add_footer(slide, "Bounded conclusion: strong controlled mechanism + standard-scale compatibility; natural mixed-traffic necessity remains open.")

    OUT_DIR.mkdir(parents=True, exist_ok=True)
    prs.save(OUT_PATH)
    print(OUT_PATH)


if __name__ == "__main__":
    build_deck()