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_multimodal.py
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main_multimodal.py
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#!/usr/bin/env python3
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"""
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JspaceAI 多模态实时交互 demo
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接入摄像头 + 麦克风 + 扬声器，原生支持图像/音频/文本。
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模式：
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1. --mode train: 在合成多模态数据上训练（无需摄像头权限）
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2. --mode live: 实时感知-行动循环（需要摄像头/麦克风权限）
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3. --mode eval: 离线评估多模态对齐能力
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运行：
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python main_multimodal.py --mode train
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python main_multimodal.py --mode live --steps 100
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python main_multimodal.py --mode eval
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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 torch.nn.functional as F
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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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import time
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from jspaceai import (
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MultimodalConfig, MultimodalJSpaceModel,
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MultimodalStream, SensoryMotorLoop,
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)
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def get_config(vocab_size: int = 50) -> MultimodalConfig:
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return MultimodalConfig(
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vocab_size=vocab_size,
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embed_dim=16,
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input_dim=8,
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workspace_dim=64,
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expert_dim=24,
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num_experts=8,
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num_wells=4,
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ode_steps=3,
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dt=0.1,
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tau_w=0.3,
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jacobian_sparsity=16,
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noise_std=0.01,
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img_size=32,
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audio_frame_size=1024,
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)
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def generate_synthetic_multimodal(batch_size: int = 4, device: str = 'cpu'):
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"""生成合成多模态数据：3 个概念对应 3 种模态特征"""
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import cv2
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concepts = ['A', 'B', 'C']
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colors = [(1, 0, 0), (0, 0, 1), (0, 1, 0)]
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freqs = [200, 800, 400]
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images, audios, tokens = [], [], []
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for _ in range(batch_size):
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idx = np.random.randint(3)
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img = np.zeros((32, 32, 3), dtype=np.float32)
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color = colors[idx]
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if idx == 0:
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cv2.circle(img, (16, 16), 8, color, -1)
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elif idx == 1:
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img[8:24, 8:24] = color
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else:
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for y in range(32):
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w = min(y, 31 - y)
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if 8 < y < 24:
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img[31-y, 16-w:16+w] = color
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images.append(img.transpose(2, 0, 1))
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t = np.linspace(0, 1024/16000, 1024)
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audio = np.sin(2 * np.pi * freqs[idx] * t).astype(np.float32) * 0.5
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audios.append(audio)
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tokens.append(idx)
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images = torch.tensor(np.stack(images)).to(device) / 255.0 * 2 - 1
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audios = torch.tensor(np.stack(audios)).to(device)
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tokens = torch.tensor(tokens, dtype=torch.long).to(device)
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return images, audios, tokens
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def train_multimodal(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); np.random.seed(42)
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config = get_config()
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model = MultimodalJSpaceModel(config).to(device)
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
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print(f"模型参数: {sum(p.numel() for p in model.parameters()):,}")
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print(f"专家模态: {model.expert_modality}")
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print(f"\n训练 {n_steps} 步...")
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history = []
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for step in range(n_steps):
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images, audios, tokens = generate_synthetic_multimodal(8, device)
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modality_choice = np.random.randint(3)
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total_loss = 0
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if modality_choice == 0:
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outputs, _ = model.forward_multimodal('image', images)
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text_loss = F.cross_entropy(outputs['text_logits'], tokens)
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audio_loss = F.mse_loss(outputs['audio'], audios)
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total_loss = text_loss + 0.1 * audio_loss
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elif modality_choice == 1:
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outputs, _ = model.forward_multimodal('audio', audios)
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text_loss = F.cross_entropy(outputs['text_logits'], tokens)
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img_loss = F.mse_loss(outputs['image'], images)
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total_loss = text_loss + 0.1 * img_loss
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else:
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outputs, _ = model.forward_multimodal('text', tokens)
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img_loss = F.mse_loss(outputs['image'], images)
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audio_loss = F.mse_loss(outputs['audio'], audios)
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total_loss = 0.1 * img_loss + 0.1 * audio_loss
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optimizer.zero_grad()
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total_loss.backward()
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torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
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optimizer.step()
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history.append(total_loss.item())
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if (step + 1) % 20 == 0:
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print(f" step {step+1:4d} | loss {total_loss.item():.4f} | "
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f"modality {['img','aud','txt'][modality_choice]}")
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print("\n" + "=" * 60)
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print("跨模态对齐评估")
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print("=" * 60)
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model.eval()
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with torch.no_grad():
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for mod_name, mod_data in [('image', images), ('audio', audios), ('text', tokens)]:
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outputs, _ = model.forward_multimodal(mod_name, mod_data[:3])
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pred_tokens = outputs['text_logits'].argmax(dim=-1)
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print(f" 输入 {mod_name:6s} → 预测 token: {pred_tokens.cpu().tolist()} "
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f"(真实: {tokens[:3].cpu().tolist()})")
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fig, axes = plt.subplots(2, 3, figsize=(12, 8))
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ax = axes[0, 0]
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ax.plot(history, alpha=0.5, linewidth=0.5)
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if len(history) > 10:
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smoothed = np.convolve(history, np.ones(10)/10, mode='valid')
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ax.plot(smoothed, linewidth=2)
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ax.set_title('Training Loss')
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ax.set_xlabel('Step')
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ax.grid(True, alpha=0.3)
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with torch.no_grad():
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test_img = images[:1]
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outputs, _ = model.forward_multimodal('image', test_img)
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ax = axes[0, 1]
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ax.imshow(((test_img[0].cpu().permute(1,2,0) + 1) / 2).numpy())
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ax.set_title('Input Image')
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ax.axis('off')
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ax = axes[0, 2]
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ax.imshow(((outputs['image'][0].cpu().permute(1,2,0) + 1) / 2).clamp(0,1).numpy())
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ax.set_title('Reconstructed Image')
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ax.axis('off')
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ax = axes[1, 0]
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input_audio = audios[0].cpu().numpy()
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ax.plot(input_audio[:200], alpha=0.7, label='input')
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out_audio = outputs['audio'][0].cpu().numpy()
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ax.plot(out_audio[:200], alpha=0.7, label='reconstructed')
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ax.set_title('Audio Waveform')
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ax.legend()
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ax.grid(True, alpha=0.3)
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ax = axes[1, 1]
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logits = outputs['text_logits'][0].cpu().numpy()
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probs = np.exp(logits) / np.exp(logits).sum()
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ax.bar(['A', 'B', 'C'], probs[:3])
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ax.set_title('Text Logits (softmax)')
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ax = axes[1, 2]
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w = outputs['w'][0].cpu().numpy()
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ax.bar(range(len(w)), w)
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ax.set_title('Workspace w (64-dim)')
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plt.tight_layout()
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fig_path = outdir / 'multimodal_train.png'
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plt.savefig(fig_path, dpi=150, bbox_inches='tight')
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print(f"\n可视化: {fig_path}")
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torch.save({'model': model.state_dict(), 'config': config},
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outdir / 'multimodal_model.pt')
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print(f"模型: {outdir / 'multimodal_model.pt'}")
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def live_demo(n_steps: int, device: str, outdir: Path):
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outdir.mkdir(parents=True, exist_ok=True)
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config = get_config()
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model = MultimodalJSpaceModel(config).to(device)
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model_path = outdir / 'multimodal_model.pt'
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if model_path.exists():
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ckpt = torch.load(model_path, map_location=device)
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model.load_state_dict(ckpt['model'])
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print(f"已加载模型: {model_path}")
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else:
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print("未找到训练好的模型，用随机初始化运行")
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model.eval()
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print("\n启动实时多模态流...")
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print("（macOS 会请求摄像头和麦克风权限，请允许）")
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stream = MultimodalStream(
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use_camera=True, use_mic=True,
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img_size=(32, 32), sample_rate=16000, audio_frame_size=1024,
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)
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loop = SensoryMotorLoop(model, stream, device=device)
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step_log = []
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def on_step(info):
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mod = info.get('modality', '?')
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step = info.get('step', 0)
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w_norm = info.get('w_norm', torch.tensor([0])).mean().item()
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step_log.append({'step': step, 'modality': mod, 'w_norm': w_norm})
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if step % 5 == 0:
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print(f" step {step:3d} | modality {mod:5s} | ||w|| {w_norm:.3f}")
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loop.run(n_steps=n_steps, interval=0.2, on_step=on_step)
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if step_log:
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fig, ax = plt.subplots(1, 1, figsize=(10, 4))
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steps = [s['step'] for s in step_log]
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w_norms = [s['w_norm'] for s in step_log]
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modalities = [s['modality'] for s in step_log]
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colors = ['blue' if m == 'audio' else 'green' for m in modalities]
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ax.scatter(steps, w_norms, c=colors, alpha=0.6, s=20)
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ax.set_xlabel('Step')
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ax.set_ylabel('||w||')
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ax.set_title('Workspace Norm During Live (blue=audio, green=image)')
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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fig_path = outdir / 'multimodal_live.png'
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plt.savefig(fig_path, dpi=150, bbox_inches='tight')
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print(f"可视化: {fig_path}")
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def eval_multimodal(device: str, outdir: Path):
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model_path = outdir / 'multimodal_model.pt'
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if not model_path.exists():
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print("请先运行 --mode train")
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return
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config = get_config()
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model = MultimodalJSpaceModel(config).to(device)
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ckpt = torch.load(model_path, map_location=device)
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model.load_state_dict(ckpt['model'])
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model.eval()
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print("多模态对齐评估")
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print("=" * 60)
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images, audios, tokens = generate_synthetic_multimodal(6, device)
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with torch.no_grad():
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for mod_name, mod_data in [('image', images), ('audio', audios), ('text', tokens)]:
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outputs, _ = model.forward_multimodal(mod_name, mod_data[:3])
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pred_tokens = outputs['text_logits'].argmax(dim=-1)
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correct = (pred_tokens == tokens[:3]).float().mean().item()
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print(f" 输入 {mod_name:6s} → token 准确率: {correct:.3f}")
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def main():
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parser = argparse.ArgumentParser(description='JspaceAI 多模态')
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parser.add_argument('--mode', type=str, default='train',
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choices=['train', 'live', 'eval'])
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parser.add_argument('--steps', type=int, default=200)
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parser.add_argument('--device', type=str, default='cpu')
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parser.add_argument('--outdir', type=str, default='outputs')
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args = parser.parse_args()
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device = args.device
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if device == 'auto':
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if torch.cuda.is_available(): device = 'cuda'
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elif torch.backends.mps.is_available(): device = 'mps'
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else: device = 'cpu'
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if args.mode == 'train':
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train_multimodal(args.steps, device, Path(args.outdir))
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elif args.mode == 'live':
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live_demo(args.steps, device, Path(args.outdir))
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elif args.mode == 'eval':
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eval_multimodal(device, Path(args.outdir))
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if __name__ == '__main__':
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main()
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