Recursion 🌌 Decentralized anonymous self-sim system with Anatta & Tao philosophy. AI self-observes, physics rendered at 30–60 FPS. Built with C++/CUDA, PyTorch, Rust. MIT License. 这是一个极其宏大的系统，无法在一则回答中完整实现全部百万行代码。但我可以为你提供各核心模块的关键代码骨架与集成思路，让你能够以此为基础填充完整实现。以下分模块给出示例代码与说明。
- 物理引擎 (C++ + CUDA)
1.1 宏观流体求解器（简化版，基于FLIP）
// fluid_solver.hpp
#include <vector>
#include <cuda_runtime.h>
struct Particle {
float3 pos, vel;
float density;
};
class FluidSolver {
public:
FluidSolver(int numParticles);
~FluidSolver();
void step(float dt, float3* externalForces); // 外部力（如引力）
void getParticles(float3* pos, float3* vel, int n); // 拷贝到CPU
private:
int numParticles;
Particle* d_particles; // GPU端粒子数据
void updateDensities(); // CUDA核函数
void updateForces(float dt);
void integrate(float dt);
};
// fluid_solver.cu
__global__ void computeDensityKernel(Particle* particles, int n) {
// 简化：用SPH或MPS计算密度
}
__global__ void computeForcesKernel(Particle* particles, int n, float dt, float3* external) {
// 计算压力、粘性、外力等
}
__global__ void integrateKernel(Particle* particles, int n, float dt) {
// 欧拉积分
}
void FluidSolver::step(float dt, float3* externalForces) {
updateDensities();
updateForces(dt);
integrate(dt);
}1.2 粒子碰撞检测（空间哈希）
// spatial_hash.cu
struct CellKey { int x, y, z; };
__device__ CellKey getCellKey(float3 pos, float cellSize) {
return {int(pos.x / cellSize), int(pos.y / cellSize), int(pos.z / cellSize)};
}
__global__ void buildHashTable(Particle* particles, int* hashKeys, int* indices, int n, float cellSize) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= n) return;
CellKey key = getCellKey(particles[i].pos, cellSize);
// 将key映射为唯一整数
hashKeys[i] = (key.x * 73856093) ^ (key.y * 19349663) ^ (key.z * 83492791);
indices[i] = i;
}
// 使用Thrust排序和邻域查询1.3 事件驱动LOD（变步长积分器）
// lod_integrator.cpp
class LODIntegrator {
public:
double step(double currentTime, double targetTime, const std::vector<Region>& focusRegions) {
double dt = maxStep;
for (auto& region : focusRegions) {
if (region.attention > 0.8) dt = min(dt, fineStep);
else dt = max(dt, coarseStep);
}
// 实际积分
return dt;
}
};- 自指AI模型 (Python + PyTorch)
2.1 自指注意力 Transformer
import torch
import torch.nn as nn
import torch.nn.functional as F
class SelfAttentionWithWeights(nn.Module):
def __init__(self, d_model, nhead):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead)
self.weight_encoder = nn.Linear(d_model, d_model) # 将权重映射到token空间
def forward(self, x, weights_flat):
# x: (seq_len, batch, d_model)
# weights_flat: (batch, num_weights) 模型自身权重的一维展开
w_emb = self.weight_encoder(weights_flat.unsqueeze(0).repeat(x.size(0), 1, 1))
# 将权重嵌入作为额外的token或与x拼接
combined = torch.cat([x, w_emb], dim=0) # (seq_len+1, batch, d_model)
attn_out, _ = self.self_attn(combined, combined, combined)
return attn_out[:x.size(0)] # 返回原始长度的输出2.2 具身交互接口（传感器/运动指令）
import pyarrow as pa
import pyarrow.plasma as plasma
import numpy as np
class EmbodiedInterface:
def __init__(self, plasma_socket="/tmp/plasma"):
self.client = plasma.connect(plasma_socket)
self.object_id_sensor = plasma.ObjectID(b"sensor_data")
self.object_id_motor = plasma.ObjectID(b"motor_cmd")
def read_sensors(self):
buf = self.client.get(self.object_id_sensor)
return np.frombuffer(buf, dtype=np.float32).reshape(3, 256, 256) # 假设3通道图像
def send_motor(self, cmd):
buf = pa.py_buffer(cmd.tobytes())
self.client.put(buf, self.object_id_motor)2.3 动态权重修改（通过Rust管理的共享内存）
此处用Python调用Rust的FFI（如ctypes或maturin）实现。
- 系统控制与沙箱 (Rust)
3.1 安全内存管理器（动态链接）
// memory_manager.rs
use libc::{c_void, mmap, munmap, PROT_READ, PROT_WRITE, MAP_ANONYMOUS, MAP_PRIVATE};
use std::ptr::null_mut;
pub struct DynamicMemory {
ptr: *mut c_void,
size: usize,
}
impl DynamicMemory {
pub fn new(size: usize) -> Option<Self> {
let ptr = unsafe {
mmap(null_mut(), size, PROT_READ | PROT_WRITE, MAP_ANONYMOUS | MAP_PRIVATE, -1, 0)
};
if ptr == libc::MAP_FAILED { None } else { Some(DynamicMemory { ptr, size }) }
}
}
impl Drop for DynamicMemory {
fn drop(&mut self) { unsafe { munmap(self.ptr, self.size); } }
}3.2 微虚拟机监控器（基于wasmtime）
use wasmtime::*;
pub struct SubSimulation {
engine: Engine,
store: Store<()>,
instance: Instance,
}
impl SubSimulation {
pub fn new(wasm_bytes: &[u8], resource_quota: u64) -> Result<Self, anyhow::Error> {
let engine = Engine::default();
let module = Module::new(&engine, wasm_bytes)?;
let mut store = Store::new(&engine, ());
// 限制内存
let linker = Linker::new(&engine);
let instance = linker.instantiate(&mut store, &module)?;
Ok(SubSimulation { engine, store, instance })
}
pub fn step(&mut self, dt: f32) -> Result<(), anyhow::Error> {
let step = self.instance.get_typed_func::<f32, ()>(&mut self.store, "step")?;
step.call(&mut self.store, dt)?;
Ok(())
}
}3.3 信息熵防火墙（C++层）
// entropy_monitor.h
#include <cmath>
#include <vector>
class EntropyMonitor {
public:
void update(const std::vector<float>& densities) {
double entropy = 0.0;
for (float d : densities) entropy -= d * log(d + 1e-12);
if (entropy > threshold) {
// 触发降维打击：简化子模拟物理规则
simplify_physics();
}
}
private:
double threshold = 10.0;
void simplify_physics() { /* 通知物理引擎降低精度 */ }
};- 部署与运维
4.1 Kubernetes部署 (物理节点与AI节点)
# physics-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: physics-engine
spec:
replicas: 2
template:
spec:
containers:
- name: cuda-solver
image: myrepo/physics-solver:latest
resources:
limits:
nvidia.com/gpu: 1
env:
- name: SHARED_MEMORY_KEY
value: "0x1234"
---
# ai-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: ai-self
spec:
replicas: 1
template:
spec:
containers:
- name: pytorch-model
image: myrepo/ai-self:latest
resources:
limits:
nvidia.com/gpu: 14.2 时序数据库（键值对快照）
# time_travel_db.py
import redis
import pickle
class TimeTravelDB:
def __init__(self):
self.r = redis.Redis()
def save_snapshot(self, time_step, state):
key = f"snapshot:{time_step}"
self.r.set(key, pickle.dumps(state))
def load_snapshot(self, time_step):
data = self.r.get(f"snapshot:{time_step}")
return pickle.loads(data) if data else None4.3 可观测性（Prometheus metrics）
from prometheus_client import Gauge, start_http_server
gpu_mem = Gauge('gpu_memory_used', 'GPU memory used in MB')
self_depth = Gauge('ai_self_depth', 'Current recursion depth')
phys_step = Gauge('physics_step_size', 'Current step size')
def update_metrics():
gpu_mem.set(get_gpu_memory())
self_depth.set(ai.recursion_depth)
phys_step.set(engine.step_size)- 多尺度耦合：共享内存示例 (C++ ↔ Python)
5.1 C++端（boost::interprocess）
#include <boost/interprocess/shared_memory_object.hpp>
#include <boost/interprocess/mapped_region.hpp>
struct PhysicsState {
float3 pos[1000000];
float3 vel[1000000];
};
void write_physics_state() {
using namespace boost::interprocess;
shared_memory_object shm(open_or_create, "physics_state", read_write);
shm.truncate(sizeof(PhysicsState));
mapped_region region(shm, read_write);
PhysicsState* state = static_cast<PhysicsState*>(region.get_address());
// 填充 state->pos, state->vel
}5.2 Python端（使用mmap）
import mmap
import numpy as np
class SharedMemoryReader:
def __init__(self, name="physics_state", size=1024*1024*1024):
self.fd = os.open("/dev/shm/" + name, os.O_RDWR | os.O_CREAT)
os.ftruncate(self.fd, size)
self.mmap = mmap.mmap(self.fd, size, mmap.MAP_SHARED, mmap.PROT_READ | mmap.PROT_WRITE)
def read_state(self):
data = np.frombuffer(self.mmap, dtype=np.float32)
pos = data[:3*1000000].reshape(1000000, 3)
vel = data[3*1000000:6*1000000].reshape(1000000, 3)
return pos, vel总结
以上代码展示了如何从物理引擎（CUDA并行）、自指AI（PyTorch带自注意）、系统控制与沙箱（Rust内存管理+wasmtime）以及部署运维四个层面构建核心模块。实际开发中，你需要：
· 在C++侧实现完整的FLIP/MPS流体求解器，并导出CUDA核函数。 · 在Python侧完善Transformer变体，训练自指损失函数（预测自身权重变化）。 · 在Rust侧实现动态内存分配器，并与wasmtime结合运行子模拟。 · 通过共享内存或gRPC实现高频数据交换，注意线程安全和锁策略。 · 使用Kubernetes编排GPU资源，并通过Prometheus监控自指深度等关键指标。
这是一个极其庞大的工程，建议按照自底向上的方式迭代：先实现稳定60FPS的10^7粒子物理引擎，再逐步加入AI感知与自指能力，最后开放递归与沙箱。祝你开发顺利！