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_language.py
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main_language.py
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#!/usr/bin/env python3
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"""
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JspaceAI 语言版 —— 自主进化实验
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模型在 Shakespeare 文本上持续学习，边推理边进化。
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观察：
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1. loss 持续下降（在学习）
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2. 生成文本从乱码逐渐变成类 Shakespeare 风格
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3. 专家分工涌现（不同专家处理不同字符模式）
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4. EWC + 经验回放防止灾难性遗忘
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运行：
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python main_language.py
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python main_language.py --steps 300 --device mps
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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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LanguageConfig, JSpaceLanguageModel, EvolutionTrainer,
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CharTokenizer, load_shakespeare,
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)
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def run_language_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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# 1. 数据
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text = load_shakespeare()
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tokenizer = CharTokenizer.from_text(text)
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print(f"文本长度: {len(text)} 字符")
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print(f"词汇表大小: {tokenizer.vocab_size}")
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print(f"词汇表: {''.join(tokenizer.chars[:50])}...")
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# 切成多段，模拟持续到来的文本流
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chunk_size = 200
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text_stream = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
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print(f"文本流: {len(text_stream)} 段, 每段 {chunk_size} 字符")
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# 2. 模型
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config = LanguageConfig(
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vocab_size=tokenizer.vocab_size,
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embed_dim=16,
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input_dim=8, # J-space 输入维度
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workspace_dim=32, # 工作空间维度
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expert_dim=16, # 每个专家内部状态
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num_experts=5, # 5 个并行专家
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num_wells=4, # 每个专家 4 个吸引子
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ode_steps=3, # ODE 积分子步
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dt=0.1,
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tau_w=0.3,
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jacobian_sparsity=8,
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noise_std=0.005,
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)
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model = JSpaceLanguageModel(config)
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n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
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print(f"\n模型参数量: {n_params:,}")
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# 3. 自主进化训练器
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trainer = EvolutionTrainer(
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model, config,
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lr=5e-3,
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ewc_lambda=0.05, # 较小的 EWC 权重，让模型能学新东西
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device=device,
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)
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# 4. 进化
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print("\n" + "=" * 70)
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print("自主进化开始")
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print("=" * 70)
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# 限制步数
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history = trainer.evolve(
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text_stream,
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tokenizer,
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seq_len=48,
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batch_size=4,
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consolidate_every=30,
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generate_every=30,
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max_steps=n_steps,
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prompt_text="To be",
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)
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# 5. 总结
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summary = trainer.get_evolution_summary()
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print("\n" + "=" * 70)
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print("进化总结")
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print("=" * 70)
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print(f"总步数: {summary['steps']}")
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print(f"初始 loss: {summary['initial_loss']:.4f}")
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print(f"最终 loss: {summary['final_loss']:.4f}")
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print(f"最低 loss: {summary['min_loss']:.4f}")
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print(f"loss 下降: {(1 - summary['final_loss']/summary['initial_loss'])*100:.1f}%")
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print(f"\n专家最终使用率: {[f'{u:.3f}' for u in summary['expert_usage']]}")
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# 专家专业化（每个专家最常处理的 top-5 字符）
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print("\n专家专业化（top-5 字符）:")
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for i, spec in enumerate(summary['expert_specialization']):
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chars = tokenizer.decode([s[0] for s in spec])
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weights = [f"{s[1]:.1f}" for s in spec]
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display = ' '.join(f"{repr(c)}({w})" for c, w in zip(chars, weights))
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print(f" 专家 {i}: {display}")
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# 6. 最终生成对比
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print("\n" + "=" * 70)
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print("最终生成对比")
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print("=" * 70)
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for prompt in ["To be", "Romeo", "The "]:
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prompt_ids = tokenizer.encode(prompt)
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generated = model.generate(prompt_ids, n_new=100, temperature=0.7, top_k=5)
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sample = prompt + tokenizer.decode(generated)
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print(f"\n提示 '{prompt}':")
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print(f" {sample}")
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# 7. 可视化
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print("\n生成可视化...")
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fig, axes = plt.subplots(2, 2, figsize=(14, 10))
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# Loss 曲线
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ax = axes[0, 0]
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losses = [h['loss'] for h in history]
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ax.plot(losses, alpha=0.3, linewidth=0.5, color='blue', label='raw')
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if len(losses) > 10:
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smoothed = np.convolve(losses, np.ones(10)/10, mode='valid')
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ax.plot(smoothed, linewidth=2, color='blue', label='smoothed')
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ax.set_xlabel('Evolution step')
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ax.set_ylabel('Cross-entropy loss')
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ax.set_title('Loss during self-evolution')
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ax.legend()
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ax.grid(True, alpha=0.3)
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# 专家使用率演化
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ax = axes[0, 1]
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alpha_history = np.array([h['alpha_mean'] for h in history])
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for i in range(config.num_experts):
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ax.plot(alpha_history[:, i], label=f'Expert {i}', alpha=0.8)
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ax.set_xlabel('Evolution step')
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ax.set_ylabel('Attention weight (mean)')
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ax.set_title('Expert usage during evolution')
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ax.legend()
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ax.grid(True, alpha=0.3)
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# ||w|| 演化
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ax = axes[1, 0]
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w_norms = [h['w_norm_mean'] for h in history]
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ax.plot(w_norms, linewidth=1.5, color='green')
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ax.set_xlabel('Evolution step')
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ax.set_ylabel('||w|| (mean)')
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ax.set_title('Workspace norm during evolution')
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ax.grid(True, alpha=0.3)
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# 生成样本随时间演化
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ax = axes[1, 1]
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ax.axis('off')
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samples = [(h['step'], h.get('sample', '')) for h in history if 'sample' in h]
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text_lines = []
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for step, sample in samples[:8]:
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s = sample[:60].replace('\n', ' ')
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text_lines.append(f"step {step:3d}: {s}")
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ax.text(0.05, 0.95, '\n'.join(text_lines),
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transform=ax.transAxes, fontsize=9, verticalalignment='top',
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fontfamily='monospace',
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bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))
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ax.set_title('Generated samples during evolution')
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plt.tight_layout()
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fig_path = outdir / 'language_evolution.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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'model': model.state_dict(),
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'config': config,
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'tokenizer_chars': tokenizer.chars,
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'history': history,
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'summary': summary,
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}, outdir / 'language_model.pt')
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print(f"模型已保存: {outdir / 'language_model.pt'}")
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print("\n" + "=" * 70)
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print("实验完成")
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print("=" * 70)
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def main():
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parser = argparse.ArgumentParser(description='JspaceAI 语言版自主进化实验')
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parser.add_argument('--steps', type=int, default=100,
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help='文本流段数 (default: 100)')
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parser.add_argument('--device', type=str, default='cpu',
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help='设备 (cpu / cuda / mps / auto)')
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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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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_language_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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