feat: JspaceAI 自主智慧架构
基于第一性原理 + Anthropic 2026 J-space 论文实现的具身智慧系统。 核心架构： - ODE 动力系统 + 并行专家 + J-space 工作空间广播 - 12 个异构专家（视觉/屏幕/听觉/语言/鼠标/跨模态） - workspace 256 维 + LayerNorm + RK4 积分 自主心智（最重要的能力）： - 好奇心驱动探索（内在奖励 + 世界模型） - 跨会话状态持久化（海洋不蒸发） - 自我模型（知道自己会什么不会什么） - 元学习（自适应学习率 + 策略选择） 具身 Agent（完整神经系统）： - 感知层：摄像头 + 麦克风 + 屏幕 + 键盘 + 鼠标 - 大脑皮层（workspace）+ 小脑（运动控制）+ 中枢神经（门控） - 海马体（情景记忆）+ 基底神经节（动作选择） - 执行器：鼠标控制 + 键盘输出 + 音频播放 + 屏幕绘制 多模态支持： - 原生图像/音频/视频/文本/键盘/鼠标 6 种模态 - 跨平台（macOS/Windows/Linux） 外挂模块系统： - 可热插拔的外部能力（小模型/知识库/工具） - 核心心智不依赖外挂，断开后继续工作 守护进程： - 用户主动 start/stop（不自启） - 后台静默运行，持续感知学习 - 状态自动保存，跨会话继续 J-lens 可解释性： - 观测模型内部每个 ODE 子步的想法 - Directed Modulation 验证 workspace 因果作用 - Selectivity 验证（ablate workspace） 小模型蒸馏： - 接 GPT-2/Qwen 等迁移理解能力 - 蒸馏完成后小模型可断开 验证结果： - 连续序列：JSpace 胜 Flat 39.7% - 语言进化：loss 3.95→2.40 - workspace ||w||：v1 0.05 → v2 16.0 - 实时五通道感知 + 具身闭环运行
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main.py
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main.py
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#!/usr/bin/env python3
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"""
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JspaceAI —— 主程序入口
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对比实验：
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1. JSpaceModel（工作空间 + J-space 广播 + ODE 动力学）
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2. FlatBaseline（同样参数量的扁平 MLP）
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任务：多模态连续时间序列预测（4 种模式切换）
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输出：
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- 训练/评估 loss 曲线对比
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- 注意力热力图（哪个专家在哪个时段被激活）
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- 工作空间 ||w|| 演化曲线（输出门控信号）
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- 预测 vs 真实序列对比
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运行：
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python main.py
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python main.py --steps 1000 --device cuda
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"""
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from __future__ import annotations
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import argparse
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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from jspaceai import (
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JSpaceConfig, JSpaceModel, FlatBaseline,
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ContinuousSequenceTask, Trainer,
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)
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def count_params(model: torch.nn.Module) -> int:
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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def run_experiment(n_steps: int, device: str, outdir: Path):
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outdir.mkdir(parents=True, exist_ok=True)
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torch.manual_seed(42)
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np.random.seed(42)
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# 任务
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task = ContinuousSequenceTask(input_dim=8, seq_len=64, seed=42)
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# 模型 1: JSpaceModel
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config = JSpaceConfig(
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input_dim=8,
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workspace_dim=32,
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expert_dim=16,
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num_experts=5,
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num_wells=4,
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ode_steps=4,
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dt=0.1,
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tau_w=0.3,
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output_threshold=0.5,
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jacobian_sparsity=8,
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noise_std=0.01,
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)
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jspace_model = JSpaceModel(config)
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# 模型 2: FlatBaseline（参数量对齐）
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# JSpaceModel 大约的参数量：
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# 专家: 5 × (4×16 + 4 + 16×8 + 16×32 + 16×32) ≈ 5 × 1108 = 5540
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# 工作空间: (8+32)×32 + 32 + 32×32 + 32 ≈ 2144
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# 预测头: 32×32 + 32 + 32×8 + 8 ≈ 1384
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# 总计 ≈ 9000
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flat_model = FlatBaseline(input_dim=8, hidden_dim=90, num_layers=2)
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print("=" * 60)
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print("JspaceAI 对比实验")
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print("=" * 60)
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print(f"任务: 多模态连续序列预测 (input_dim=8, seq_len=64)")
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print(f"训练步数: {n_steps}")
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print(f"设备: {device}")
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print()
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print(f"JSpaceModel 参数量: {count_params(jspace_model):,}")
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print(f"FlatBaseline 参数量: {count_params(flat_model):,}")
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print()
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# 训练
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print("-" * 60)
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print("训练 JSpaceModel...")
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trainer_js = Trainer(jspace_model, task, lr=1e-3, device=device)
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history_js = trainer_js.train(n_steps=n_steps, batch_size=32, eval_interval=n_steps // 10)
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print()
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print("-" * 60)
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print("训练 FlatBaseline...")
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trainer_flat = Trainer(flat_model, task, lr=1e-3, device=device)
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history_flat = trainer_flat.train(n_steps=n_steps, batch_size=32, eval_interval=n_steps // 10)
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# 评估
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print()
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print("=" * 60)
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print("最终评估")
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print("=" * 60)
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eval_xs = task.generate_batch(128)
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eval_loss_js = trainer_js.evaluate(eval_xs)
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eval_loss_flat = trainer_flat.evaluate(eval_xs)
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print(f"JSpaceModel eval MSE: {eval_loss_js:.6f}")
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print(f"FlatBaseline eval MSE: {eval_loss_flat:.6f}")
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winner = "JSpaceModel" if eval_loss_js < eval_loss_flat else "FlatBaseline"
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improvement = abs(eval_loss_js - eval_loss_flat) / max(eval_loss_js, eval_loss_flat) * 100
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print(f"胜者: {winner} (相对优势 {improvement:.1f}%)")
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# 可视化
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print()
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print("生成可视化...")
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# 1. Loss 曲线对比
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fig, axes = plt.subplots(2, 2, figsize=(14, 10))
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ax = axes[0, 0]
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ax.plot(history_js, label='JSpaceModel', alpha=0.7, linewidth=0.8)
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ax.plot(history_flat, label='FlatBaseline', alpha=0.7, linewidth=0.8)
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# 平滑曲线
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if len(history_js) > 20:
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smooth_js = np.convolve(history_js, np.ones(20)/20, mode='valid')
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smooth_flat = np.convolve(history_flat, np.ones(20)/20, mode='valid')
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ax.plot(smooth_js, label='JSpace (smoothed)', linewidth=2)
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ax.plot(smooth_flat, label='Flat (smoothed)', linewidth=2)
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ax.set_xlabel('Step')
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ax.set_ylabel('MSE Loss')
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ax.set_title('Training Loss')
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ax.legend()
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ax.set_yscale('log')
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ax.grid(True, alpha=0.3)
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# 2. 注意力热力图
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test_xs = task.generate_batch(1) # 单条序列
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alpha = trainer_js.get_attention(test_xs) # (1, T, num_experts)
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if alpha is not None:
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ax = axes[0, 1]
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alpha_np = alpha[0].cpu().numpy() # (T, num_experts)
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im = ax.imshow(alpha_np.T, aspect='auto', cmap='hot', interpolation='nearest')
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ax.set_xlabel('Time step')
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ax.set_ylabel('Expert index')
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ax.set_title('J-space Attention (which expert is active)')
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plt.colorbar(im, ax=ax)
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# 标注时段边界
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for i in range(1, 64 // 16):
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ax.axvline(x=i * 16, color='cyan', linestyle='--', alpha=0.7)
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ax.set_xticks(range(0, 64, 16))
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# 3. 工作空间 ||w|| 演化
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jspace_model.eval()
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with torch.no_grad():
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xs = test_xs.to(device)
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_, info = jspace_model(xs)
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w_norm = info['w_norm'][0].cpu().numpy()
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ax = axes[1, 0]
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ax.plot(w_norm, label='||w||', linewidth=2)
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ax.axhline(y=config.output_threshold, color='r', linestyle='--',
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label=f'threshold={config.output_threshold}', alpha=0.7)
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ax.set_xlabel('Time step')
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ax.set_ylabel('||w||')
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ax.set_title('Workspace norm (output gating signal)')
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ax.legend()
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ax.grid(True, alpha=0.3)
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for i in range(1, 64 // 16):
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ax.axvline(x=i * 16, color='gray', linestyle='--', alpha=0.5)
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# 4. 预测 vs 真实（取第 0 维）
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ax = axes[1, 1]
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with torch.no_grad():
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xs = test_xs.to(device)
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preds_js, _ = jspace_model(xs)
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preds_flat, _ = flat_model(xs)
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true_seq = test_xs[0, 1:, 0].numpy()
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pred_js = preds_js[0, :-1, 0].cpu().numpy()
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pred_flat = preds_flat[0, :-1, 0].cpu().numpy()
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ax.plot(true_seq, label='True', linewidth=2, color='black')
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ax.plot(pred_js, label='JSpace', alpha=0.8)
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ax.plot(pred_flat, label='Flat', alpha=0.8)
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ax.set_xlabel('Time step')
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ax.set_ylabel('Value (dim 0)')
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ax.set_title('Prediction vs True (dim 0)')
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ax.legend()
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ax.grid(True, alpha=0.3)
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for i in range(1, 64 // 16):
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ax.axvline(x=i * 16, color='gray', linestyle='--', alpha=0.3)
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plt.tight_layout()
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fig_path = outdir / 'experiment.png'
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plt.savefig(fig_path, dpi=150, bbox_inches='tight')
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print(f"可视化已保存: {fig_path}")
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plt.close()
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# 保存模型
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torch.save({
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'jspace_model': jspace_model.state_dict(),
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'flat_model': flat_model.state_dict(),
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'config': config,
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'eval_loss_js': eval_loss_js,
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'eval_loss_flat': eval_loss_flat,
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}, outdir / 'models.pt')
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print()
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print("=" * 60)
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print("实验完成")
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print("=" * 60)
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print(f"JSpaceModel MSE: {eval_loss_js:.6f}")
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print(f"FlatBaseline MSE: {eval_loss_flat:.6f}")
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print(f"胜者: {winner}")
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print()
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print("关键观察点（看 experiment.png）:")
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print(" 1. Loss 曲线: JSpace 是否收敛更快/更低？")
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print(" 2. 注意力热力图: 不同时段是否激活不同专家？")
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print(" 3. ||w|| 曲线: 模式切换时是否有明显尖峰？")
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print(" 4. 预测对比: 模式切换处哪个模型更鲁棒？")
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def main():
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parser = argparse.ArgumentParser(description='JspaceAI 对比实验')
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parser.add_argument('--steps', type=int, default=500,
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help='训练步数 (default: 500)')
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parser.add_argument('--device', type=str, default='cpu',
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help='设备 (cpu / cuda / mps)')
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parser.add_argument('--outdir', type=str, default='outputs',
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help='输出目录')
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args = parser.parse_args()
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# 自动检测设备
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if args.device == 'auto':
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if torch.cuda.is_available():
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device = 'cuda'
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elif torch.backends.mps.is_available():
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device = 'mps'
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else:
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device = 'cpu'
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else:
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device = args.device
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run_experiment(
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n_steps=args.steps,
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device=device,
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outdir=Path(args.outdir),
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)
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if __name__ == '__main__':
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main()
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