世界模型研究前沿：AI 能理解物理世界吗？

# Sora 架构概览（基于公开信息）
"""
Sora = DiT (Diffusion Transformer) + 视频压缩网络

1. 视频压缩：3D VAE 将视频压缩到时空 latent space
   - 输入：256x256x16 帧 → latent: 32x32x4
   - 压缩比 ~64x

2. DiT 扩散：
   - 在 latent space 做条件扩散
   - 条件：文本 prompt + 参考帧
   - Transformer blocks: 28层, hidden=1024

3. 解码：latent → 视频帧
"""

# Sora 的"物理"可能只是统计相关
# 测试：球从桌上弹起后能否正确预测弹跳轨迹

import matplotlib.pyplot as plt

# 真实物理引擎（精确）
def true_bounce(x0, y0, vx0, vy0, steps=60):
    """使用 ODE 求解器的真实弹跳"""
    g = 9.81
    positions = []
    x, y, vx, vy = x0, y0, vx0, vy0
    for _ in range(steps):
        vy += g * 0.016  # dt=16ms
        x += vx * 0.016
        y += vy * 0.016
        if y > 0 and y + vy * 0.016 > 0.5:  # 碰地
            vy = -vy * 0.7  # 恢复系数
        positions.append((x, y))
    return positions

# Sora 风格的"物理"（统计生成）
# 问题：第一次弹跳大致对，第二次开始偏离，第三次后完全不可预测
# 因为它没有显式的物理状态跟踪，只有帧间的统计关联

# Genie 架构
"""
Genie = VQ-VAE (视觉分词) + Transformer (自回归) + Latent Action Model

关键创新：无监督发现"隐式动作"

1. VQ-VAE：将每帧图像编码为离散 token
   frame_t → tokens_t

2. Latent Action Model：推断帧间的"动作"
   (frame_t, frame_{t+1}) → action_t (离散)
   这个 action 不是人类定义的，是模型自己发现的

3. Dynamics Model：给定历史帧和动作，预测下一帧
   (tokens_{t-k:t}, action_t) → tokens_{t+1}

4. 交互时：用户提供"动作"（如按键），模型生成对应的下一帧
"""

import torch
import torch.nn as nn

class DreamerV3:
    """简化版 DreamerV3 世界模型"""

    class WorldModel(nn.Module):
        def __init__(self, obs_dim, action_dim, latent_dim=512):
            super().__init__()
            # 编码器：观察 → 潜在状态
            self.encoder = nn.GRU(obs_dim + action_dim, latent_dim, batch_first=True)

            # RSSM (Recurrent State-Space Model)
            # 分解为 deterministic 和 stochastic 部分
            self.prior_net = nn.Sequential(
                nn.Linear(latent_dim, 256),
                nn.SiLU(),
                nn.Linear(256, latent_dim * 2)  # mean, std
            )
            self.posterior_net = nn.Sequential(
                nn.Linear(latent_dim + obs_dim, 256),
                nn.SiLU(),
                nn.Linear(256, latent_dim * 2)
            )

            # 解码器：潜在状态 → 观察重构
            self.decoder = nn.Sequential(
                nn.Linear(latent_dim, 256),
                nn.SiLU(),
                nn.Linear(256, obs_dim)
            )

            # 奖励预测器
            self.reward_head = nn.Sequential(
                nn.Linear(latent_dim, 256),
                nn.SiLU(),
                nn.Linear(256, 1)
            )

        def forward(self, obs_seq, action_seq):
            """在潜在空间中想象"""
            latent_states = []
            h = torch.zeros(1, self.encoder.hidden_size)

            for t in range(obs_seq.size(1)):
                # 先验（预测）
                prior = self.prior_net(h)
                prior_mean, prior_std = prior.chunk(2, -1)

                # 后验（从观察推断）
                posterior_input = torch.cat([h, obs_seq[:, t]], -1)
                posterior = self.posterior_net(posterior_input)
                post_mean, post_std = posterior.chunk(2, -1)

                # 采样
                z = post_mean + post_std * torch.randn_like(post_std)

                # GRU 更新
                _, h = self.encoder(
                    torch.cat([obs_seq[:, t], action_seq[:, t]], -1).unsqueeze(1), h.unsqueeze(0)
                )
                h = h.squeeze(0)
                latent_states.append(z)

            return torch.stack(latent_states, dim=1)

    def imagine_and_learn(self, world_model, policy, horizon=15):
        """在世界模型中想象并训练策略"""
        # 1. 从真实经验中训练世界模型
        # 2. 从当前状态开始想象未来 horizon 步
        # 3. 用想象的轨迹训练 Actor-Critic
        # 关键优势：不需要与环境交互，纯在"脑内"学习
        pass

# JEPA (Joint-Embedding Predictive Architecture) 核心思想
"""
传统生成式：
  x → encoder → s_x
  目标：从 s_x 重构 x（浪费算力在无关细节上）

JEPA：
  x → encoder_x → s_x
  y → encoder_y → s_y
  目标：预测 s_y from (s_x, action)
  不需要重构原始像素！只预测抽象表示。
"""

class JEPA:
    def __init__(self):
        self.context_encoder = VisionTransformer(patch_size=16)
        self.target_encoder = VisionTransformer(patch_size=16)
        self.predictor = TransformerPredictor()

    def forward(self, context_frame, target_frame, action):
        # 编码
        s_context = self.context_encoder(context_frame)
        with torch.no_grad():
            s_target = self.target_encoder(target_frame)

        # 预测目标表示
        s_predicted = self.predictor(s_context, action)

        # 损失：表示空间中的 L2 距离
        loss = F.mse_loss(s_predicted, s_target)

        # EMA 更新 target encoder
        self._update_target_ema()
        return loss

class PhysicsUnderstandingEval:
    """物理理解能力评估框架"""

    def __init__(self, model):
        self.model = model
        self.test_cases = self._load_test_cases()

    def eval_object_permanence(self):
        """物体永久性测试"""
        results = []
        for case in self.test_cases["object_permanence"]:
            # 展示物体被遮挡的过程
            frames = case["video_frames"]  # 球滚到墙后
            question = case["question"]     # "球现在在哪里？"

            response = self.model.answer(video=frames, question=question)
            correct = self._check_answer(response, case["answer"])

            results.append({
                "case_id": case["id"],
                "correct": correct,
                "response": response,
            })

        accuracy = sum(r["correct"] for r in results) / len(results)
        return {"object_permanence_accuracy": accuracy}

    def eval_counterfactual(self):
        """反事实物理推理"""
        results = []
        for case in self.test_cases["counterfactual"]:
            # "如果桌子向左倾斜 10 度，球会往哪边滚？"
            image = case["image"]
            question = case["question"]

            response = self.model.answer(image=image, question=question)

            # 检查是否理解了因果链
            correct = self._check_physics_reasoning(response, case["expected_reasoning"])
            results.append({"correct": correct})

        return {"counterfactual_accuracy": sum(r["correct"] for r in results) / len(results)}