GPT-5.6 Sol全球解禁：Ultra多智能体架构、150万Token上下文与Agent-of-Agents范式商用化深度解析

// Go语言实现：Ultra模式的任务分解与子智能体调度

package ultra

import (
	"context"
	"fmt"
	"sync"
	"time"
)

// Task 表示一个可分解的任务
type Task struct {
	ID          string
	Description string
	SubTasks    []SubTask
	Result      string
	Status      TaskStatus
}

// SubTask 表示一个子智能体需要完成的子任务
type SubTask struct {
	ID       string
	ParentID string
	Prompt   string
	Context  []string
	Result   string
	Status   SubTaskStatus
}

type TaskStatus int
type SubTaskStatus int

const (
	TaskPending TaskStatus = iota
	TaskDecomposing
	TaskExecuting
	TaskCompleted
	TaskFailed

	SubTaskPending SubTaskStatus = iota
	SubTaskRunning
	SubTaskCompleted
	SubTaskFailed
)

// UltraEngine 是Ultra模式的核心调度引擎
type UltraEngine struct {
	maxSubAgents   int
	taskQueue      chan *Task
	resultChannels map[string]chan string
	mu             sync.RWMutex
}

// NewUltraEngine 创建Ultra调度引擎
func NewUltraEngine(maxSubAgents int) *UltraEngine {
	return &UltraEngine{
		maxSubAgents:   maxSubAgents,
		taskQueue:      make(chan *Task, 100),
		resultChannels: make(map[string]chan string),
	}
}

// DecomposeTask 将任务分解为子任务
// 这是Ultra模式的核心：主模型分析任务结构，自动生成子任务分解方案
func (e *UltraEngine) DecomposeTask(ctx context.Context, task *Task) error {
	task.Status = TaskDecomposing

	// 模拟主模型的任务分解逻辑
	// 在实际系统中，这是由Sol的推理引擎完成的
	subTasks := []SubTask{
		{
			ID:       fmt.Sprintf("%s-sub-1", task.ID),
			ParentID: task.ID,
			Prompt:   "分析代码库的依赖关系",
			Status:   SubTaskPending,
		},
		{
			ID:       fmt.Sprintf("%s-sub-2", task.ID),
			ParentID: task.ID,
			Prompt:   "检查安全漏洞",
			Status:   SubTaskPending,
		},
		{
			ID:       fmt.Sprintf("%s-sub-3", task.ID),
			ParentID: task.ID,
			Prompt:   "生成测试用例",
			Status:   SubTaskPending,
		},
	}

	task.SubTasks = subTasks
	task.Status = TaskExecuting
	return nil
}

// Execute 并行执行所有子任务
func (e *UltraEngine) Execute(ctx context.Context, task *Task) error {
	var wg sync.WaitGroup
	errChan := make(chan error, len(task.SubTasks))

	for i := range task.SubTasks {
		wg.Add(1)
		go func(st *SubTask) {
			defer wg.Done()
			st.Status = SubTaskRunning

			// 每个子智能体在隔离的上下文中执行
			// 这模拟了Sol中每个子Agent拥有独立上下文窗口
			result, err := e.runSubAgent(ctx, st)
			if err != nil {
				errChan <- fmt.Errorf("subtask %s failed: %w", st.ID, err)
				st.Status = SubTaskFailed
				return
			}
			st.Result = result
			st.Status = SubTaskCompleted
		}(&task.SubTasks[i])
	}

	wg.Wait()
	close(errChan)

	// 检查是否有错误
	for err := range errChan {
		if err != nil {
			return err
		}
	}

	task.Status = TaskCompleted
	return nil
}

// runSubAgent 在隔离上下文中运行子智能体
// 每个子Agent拥有独立的内存空间和推理链
func (e *UltraEngine) runSubAgent(ctx context.Context, st *SubTask) (string, error) {
	// 模拟子智能体的推理过程
	// 实际实现中，每个子Agent调用独立的推理实例
	time.Sleep(100 * time.Millisecond) // 模拟推理延迟

	switch st.Prompt {
	case "分析代码库的依赖关系":
		return `{
  "dependencies": ["module-a", "module-b", "module-c"],
  "circular_deps": ["module-b -> module-c -> module-b"],
  "vulnerable_packages": ["lodash@4.17.20"]
}`, nil
	case "检查安全漏洞":
		return `{
  "critical": 1,
  "high": 3,
  "medium": 5,
  "details": ["CVE-2026-1234: SQL injection in auth module"]
}`, nil
	case "生成测试用例":
		return `{
  "unit_tests": 15,
  "integration_tests": 5,
  "coverage": "87.3%"
}`, nil
	}

	return "", nil
}

// SynthesizeResults 合成所有子任务的结果
// 主模型收集所有子Agent的输出，生成最终回答
func (e *UltraEngine) SynthesizeResults(task *Task) string {
	e.mu.RLock()
	defer e.mu.RUnlock()

	// 主模型的结果合成逻辑
	summary := fmt.Sprintf("任务 %s 完成，共 %d 个子任务:\n", task.ID, len(task.SubTasks))
	for _, st := range task.SubTasks {
		summary += fmt.Sprintf("  - %s: %s (状态: %v)\n", st.Prompt, st.Result[:min(len(st.Result), 50)], st.Status)
	}

	return summary
}

func min(a, b int) int {
	if a < b {
		return a
	}
	return b
}

// UltraPipeline 展示完整的Ultra模式执行流程
func UltraPipeline(taskDescription string) {
	ctx := context.Background()
	engine := NewUltraEngine(5)

	task := &Task{
		ID:          "task-001",
		Description: taskDescription,
		Status:      TaskPending,
	}

	// 1. 任务分解
	engine.DecomposeTask(ctx, task)

	// 2. 并行执行子任务
	engine.Execute(ctx, task)

	// 3. 结果合成
	result := engine.SynthesizeResults(task)
	fmt.Println(result)
}

func main() {
	UltraPipeline("审计代码库安全性并生成测试报告")
}

# Python实现：简化的Sol长上下文注意力机制

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class SolLongContextAttention(nn.Module):
    """
    模拟GPT-5.6 Sol的长上下文注意力机制
    结合滑动窗口注意力 + 全局token注意力 + 压缩记忆
    """
    def __init__(self, d_model=8192, n_heads=64, window_size=32768, 
                 global_tokens=1024, mem_compress_ratio=8):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.window_size = window_size
        self.global_tokens = global_tokens
        self.mem_compress_ratio = mem_compress_ratio
        
        # 标准QKV投影
        self.wq = nn.Linear(d_model, d_model, bias=False)
        self.wk = nn.Linear(d_model, d_model, bias=False)
        self.wv = nn.Linear(d_model, d_model, bias=False)
        self.wo = nn.Linear(d_model, d_model, bias=False)
        
        # 全局token选择器
        self.global_selector = nn.Linear(d_model, 1)
        
        # 记忆压缩器
        self.mem_compressor = nn.Conv1d(
            d_model, d_model, kernel_size=mem_compress_ratio, 
            stride=mem_compress_ratio
        )
        self.mem_decompressor = nn.ConvTranspose1d(
            d_model, d_model, kernel_size=mem_compress_ratio, 
            stride=mem_compress_ratio
        )
        
    def forward(self, x, past_memory=None):
        batch_size, seq_len, _ = x.shape
        
        # QKV投影
        q = self.wq(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        k = self.wk(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        v = self.wv(x).view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        
        # 1. 滑动窗口注意力（局部精确建模）
        window_mask = self._create_window_mask(seq_len, self.window_size)
        window_attn = self._compute_attention(q, k, v, window_mask)
        
        # 2. 全局token注意力（长距离保留）
        global_scores = self.global_selector(x).squeeze(-1)  # [batch, seq]
        _, global_indices = torch.topk(global_scores, 
                                       min(self.global_tokens, seq_len), 
                                       dim=-1)
        
        # 收集全局token的KV
        global_k = self._gather_tokens(k, global_indices)
        global_v = self._gather_tokens(v, global_indices)
        global_attn = self._compute_attention(q, global_k, global_v)
        
        # 3. 压缩记忆机制（历史信息压缩）
        if past_memory is not None:
            # 压缩历史记忆
            mem_compressed = self.mem_compressor(
                past_memory.transpose(1, 2)
            ).transpose(1, 2)
            mem_k = self.wk(mem_compressed).view(
                batch_size, -1, self.n_heads, self.head_dim
            ).transpose(1, 2)
            mem_v = self.wv(mem_compressed).view(
                batch_size, -1, self.n_heads, self.head_dim
            ).transpose(1, 2)
            mem_attn = self._compute_attention(q, mem_k, mem_v)
        else:
            mem_attn = 0
        
        # 4. 融合三种注意力
        # 权重分配：窗口注意力0.5 + 全局注意力0.3 + 记忆注意力0.2
        combined = 0.5 * window_attn + 0.3 * global_attn + 0.2 * mem_attn
        
        # 输出投影
        output = combined.transpose(1, 2).contiguous().view(
            batch_size, seq_len, self.d_model
        )
        output = self.wo(output)
        
        # 更新记忆（压缩当前序列）
        current_memory = x.detach()
        
        return output, current_memory
    
    def _create_window_mask(self, seq_len, window_size):
        """创建滑动窗口注意力掩码"""
        mask = torch.full((seq_len, seq_len), float('-inf'))
        for i in range(seq_len):
            start = max(0, i - window_size)
            mask[i, start:i+1] = 0
        return mask.to(self.wq.weight.device)
    
    def _compute_attention(self, q, k, v, mask=None):
        """计算缩放点积注意力"""
        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        if mask is not None:
            scores = scores + mask.unsqueeze(0).unsqueeze(0)
        attn = F.softmax(scores, dim=-1)
        return torch.matmul(attn, v)
    
    def _gather_tokens(self, x, indices):
        """按索引收集token"""
        batch_size, n_heads, seq_len, head_dim = x.shape
        # 注意：简化实现，实际需要处理batch维度
        expanded_indices = indices.unsqueeze(1).unsqueeze(-1).expand(
            -1, n_heads, -1, head_dim
        )
        return torch.gather(x, 2, expanded_indices)

# 模拟1.5M token上下文的处理
def simulate_long_context():
    model = SolLongContextAttention()
    memory = None
    
    # 模拟分块处理超长上下文
    chunk_size = 131072  # 128K per chunk
    total_chunks = 12    # 共1.5M tokens
    
    for i in range(total_chunks):
        # 模拟输入chunk
        x = torch.randn(1, chunk_size, 8192)
        output, memory = model(x, memory)
        
        if i % 3 == 0:
            print(f"Chunk {i+1}/{total_chunks} processed, "
                  f"memory shape: {memory.shape}")
    
    print("1.5M token context processed successfully")
    print(f"Final memory compressed to {memory.shape[1]} tokens "
          f"(compression ratio: {chunk_size * total_chunks // memory.shape[1]}:1)")

# 显式缓存断点（Cache Breakpoint）
# OpenAI引入了显式缓存断点和30分钟最低缓存存留时间
class CacheBreakpoint:
    """
    显式缓存断点机制
    缓存写入：未缓存输入价格的1.25倍
    缓存读取：享受90%折扣（仅支付原价的10%）
    """
    def __init__(self, cache_duration_minutes=30):
        self.cache_duration = cache_duration_minutes * 60
        self.cache_store = {}
        self.timestamps = {}
    
    def set_breakpoint(self, prefix: str, content: str):
        """在指定位置设置缓存断点"""
        import time
        cache_key = hash(prefix + content[:100])
        self.cache_store[cache_key] = content
        self.timestamps[cache_key] = time.time()
        return cache_key
    
    def read_cache(self, cache_key: int) -> str:
        """读取缓存内容（适用90%折扣）"""
        import time
        if cache_key not in self.cache_store:
            return None
        
        elapsed = time.time() - self.timestamps[cache_key]
        if elapsed > self.cache_duration:
            del self.cache_store[cache_key]
            del self.timestamps[cache_key]
            return None
        
        return self.cache_store[cache_key]

# 推理延迟对比：标准GPU vs Cerebras WSE-3

def calculate_agent_latency(total_tokens, tokens_per_second, tool_calls=0):
    """
    计算Agent循环的总延迟
    """
    generation_time = total_tokens / tokens_per_second
    tool_overhead = tool_calls * 2.0  # 每次工具调用约2秒
    return generation_time + tool_overhead

# 标准GPU（H100）：~50 tokens/s
standard_gpu = calculate_agent_latency(100000, 50, 50)
print(f"标准GPU (H100) 总延迟: {standard_gpu:.1f} 秒 = {standard_gpu/60:.1f} 分钟")

# Cerebras WSE-3：750 tokens/s
cerebras = calculate_agent_latency(100000, 750, 50)
print(f"Cerebras WSE-3 总延迟: {cerebras:.1f} 秒 = {cerebras/60:.1f} 分钟")

print(f"加速比: {standard_gpu/cerebras:.1f}x")