智谱「摸高计划」深度解析：唐杰内部信全文解读，港股AI第一股为何选择在行业最热时死磕AGI？

package main

import (
    "container/heap"
    "encoding/json"
    "fmt"
    "sync"
    "time"
)

// TaskPriority 任务优先级
type TaskPriority int

const (
    Low    TaskPriority = iota
    Normal
    High
    Critical
)

// SubTask 子任务
type SubTask struct {
    ID          string        `json:"id"`
    Description string        `json:"description"`
    Status      string        `json:"status"` // pending, running, completed, failed
    Priority    TaskPriority  `json:"priority"`
    Dependencies []string     `json:"dependencies"`
    CreatedAt   time.Time     `json:"created_at"`
    CompletedAt *time.Time    `json:"completed_at,omitempty"`
    Result      string        `json:"result,omitempty"`
    ParentID    string        `json:"parent_id"`
}

// LongTermMemory 长期记忆
type LongTermMemory struct {
    EpisodeID     string                 `json:"episode_id"`
    Goal          string                 `json:"goal"`
    State         string                 `json:"state"` // planning, executing, reviewing, completed
    Subtasks      map[string]*SubTask    `json:"subtasks"`
    Context       map[string]interface{} `json:"context"`
    LessonsLearned []string              `json:"lessons_learned"`
    CreatedAt     time.Time              `json:"created_at"`
    UpdatedAt     time.Time              `json:"updated_at"`
}

// PriorityQueue 优先级队列
type PriorityQueue []*SubTask

func (pq PriorityQueue) Len() int { return len(pq) }
func (pq PriorityQueue) Less(i, j int) bool {
    return pq[i].Priority > pq[j].Priority
}
func (pq PriorityQueue) Swap(i, j int) { pq[i], pq[j] = pq[j], pq[i] }
func (pq *PriorityQueue) Push(x interface{}) { *pq = append(*pq, x.(*SubTask)) }
func (pq *PriorityQueue) Pop() interface{} {
    old := *pq
    n := len(old)
    item := old[n-1]
    *pq = old[0 : n-1]
    return item
}

// LongHorizonTaskEngine 长程任务引擎
type LongHorizonTaskEngine struct {
    mu       sync.RWMutex
    memories map[string]*LongTermMemory
    taskQueue PriorityQueue
    maxSubtasks int
}

func NewLongHorizonTaskEngine(maxSubtasks int) *LongHorizonTaskEngine {
    return &LongHorizonTaskEngine{
        memories:    make(map[string]*LongTermMemory),
        maxSubtasks: maxSubtasks,
    }
}

// DecomposeGoal 将宏大目标分解为子任务
func (lte *LongHorizonTaskEngine) DecomposeGoal(goal string) (*LongTermMemory, error) {
    lte.mu.Lock()
    defer lte.mu.Unlock()

    episodeID := fmt.Sprintf("EP-%d", time.Now().UnixNano())
    
    memory := &LongTermMemory{
        EpisodeID:     episodeID,
        Goal:          goal,
        State:         "planning",
        Subtasks:      make(map[string]*SubTask),
        Context:       make(map[string]interface{}),
        LessonsLearned: make([]string, 0),
        CreatedAt:     time.Now(),
        UpdatedAt:     time.Now(),
    }

    // 模拟目标分解：将"设计抗癌药物分子"分解为子任务
    decompositions := []struct {
        id       string
        desc     string
        priority TaskPriority
        deps     []string
    }{
        {"T001", "文献调研：现有抗癌药物靶点分析", High, nil},
        {"T002", "收集已批准的激酶抑制剂分子结构", Normal, []string{"T001"}},
        {"T003", "构建分子生成模型训练数据集", High, []string{"T002"}},
        {"T004", "训练条件扩散模型生成候选分子", Critical, []string{"T003"}},
        {"T005", "分子对接模拟筛选高亲和力候选物", Critical, []string{"T004"}},
        {"T006", "ADMET药代动力学性质预测", High, []string{"T005"}},
        {"T007", "合成路线规划与可合成性评估", Normal, []string{"T006"}},
        {"T008", "实验验证方案设计", Normal, []string{"T007"}},
        {"T009", "结果分析与迭代优化", High, []string{"T008"}},
        {"T010", "研究报告自动生成", Low, []string{"T009"}},
    }

    for _, d := range decompositions {
        subtask := &SubTask{
            ID:           d.id,
            Description:  d.desc,
            Status:       "pending",
            Priority:     d.priority,
            Dependencies: d.deps,
            CreatedAt:    time.Now(),
            ParentID:     episodeID,
        }
        memory.Subtasks[d.id] = subtask
        if len(d.deps) == 0 {
            heap.Push(&lte.taskQueue, subtask)
        }
    }

    memory.Context["total_subtasks"] = len(decompositions)
    memory.Context["completed_subtasks"] = 0
    memory.Context["estimated_duration"] = "6-8 weeks"
    
    lte.memories[episodeID] = memory
    return memory, nil
}

// GetReadyTasks 获取可执行的任务（依赖已完成的）
func (lte *LongHorizonTaskEngine) GetReadyTasks(episodeID string) []*SubTask {
    lte.mu.RLock()
    defer lte.mu.RUnlock()

    memory, exists := lte.memories[episodeID]
    if !exists {
        return nil
    }

    var readyTasks []*SubTask
    for _, task := range memory.Subtasks {
        if task.Status != "pending" {
            continue
        }
        allDepsMet := true
        for _, depID := range task.Dependencies {
            dep, ok := memory.Subtasks[depID]
            if !ok || dep.Status != "completed" {
                allDepsMet = false
                break
            }
        }
        if allDepsMet {
            readyTasks = append(readyTasks, task)
        }
    }
    return readyTasks
}

// UpdateMemory 更新记忆并记录经验
func (lte *LongHorizonTaskEngine) UpdateMemory(episodeID string, taskID string, result string, lesson string) error {
    lte.mu.Lock()
    defer lte.mu.Unlock()

    memory, exists := lte.memories[episodeID]
    if !exists {
        return fmt.Errorf("episode %s not found", episodeID)
    }

    task, exists := memory.Subtasks[taskID]
    if !exists {
        return fmt.Errorf("task %s not found", taskID)
    }

    now := time.Now()
    task.Status = "completed"
    task.CompletedAt = &now
    task.Result = result

    if lesson != "" {
        memory.LessonsLearned = append(memory.LessonsLearned, lesson)
    }

    memory.Context["completed_subtasks"] = memory.Context["completed_subtasks"].(int) + 1
    memory.UpdatedAt = now

    // 检查是否全部完成
    if memory.Context["completed_subtasks"].(int) == memory.Context["total_subtasks"].(int) {
        memory.State = "completed"
    }

    return nil
}

func main() {
    engine := NewLongHorizonTaskEngine(100)

    // 分解宏大目标
    goal := "设计一种针对EGFR T790M突变的新型抗癌药物分子"
    memory, _ := engine.DecomposeGoal(goal)
    
    fmt.Printf("目标：%s\n", memory.Goal)
    fmt.Printf("分解为 %d 个子任务\n", len(memory.Subtasks))
    
    // 模拟执行前5个任务
    for i := 0; i < 5; i++ {
        readyTasks := engine.GetReadyTasks(memory.EpisodeID)
        for _, task := range readyTasks {
            fmt.Printf("\n执行：%s - %s\n", task.ID, task.Description)
            result := fmt.Sprintf("任务 %s 完成，输出结果：%s", task.ID, "模拟执行结果")
            lesson := fmt.Sprintf("从 %s 学到的经验：%s", task.Description, "需要更精确的分子描述符")
            engine.UpdateMemory(memory.EpisodeID, task.ID, result, lesson)
        }
    }

    // 输出进度
    fmt.Printf("\n进度：%d/%d 子任务完成\n", 
        memory.Context["completed_subtasks"], memory.Context["total_subtasks"])
    fmt.Printf("已学经验：%d 条\n", len(memory.LessonsLearned))
}

#!/usr/bin/env python3
"""
完全自我训练（Fully Self Training）系统原型
实现AI自博弈、数据合成、模型迭代的闭环
"""

import numpy as np
from typing import List, Dict, Tuple, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
import random
import json
from collections import defaultdict

class TaskType(Enum):
    CODE_GENERATION = "code_generation"
    MATH_REASONING = "math_reasoning"
    LOGIC_PUZZLE = "logic_puzzle"
    CREATIVE_WRITING = "creative_writing"
    SCIENTIFIC_ANALYSIS = "scientific_analysis"

@dataclass
class SelfPlayRound:
    """自博弈回合"""
    task: str
    task_type: TaskType
    player_a_output: str
    player_b_output: str
    winner: str  # "A", "B", or "draw"
    judge_feedback: str
    quality_score: float

@dataclass
class SyntheticDataPoint:
    """合成数据点"""
    prompt: str
    response: str
    task_type: TaskType
    quality_score: float
    diversity_score: float
    generated_by: str  # "self_play", "augmentation", "mutation"
    source_model: str

class SelfTrainingSystem:
    """完全自我训练系统"""
    
    def __init__(self, model_capacity: int = 1000000):
        self.synthetic_data: List[SyntheticDataPoint] = []
        self.self_play_history: List[SelfPlayRound] = []
        self.model_checkpoints: List[str] = []
        self.performance_metrics: Dict[str, List[float]] = defaultdict(list)
        self.model_capacity = model_capacity
        self.knowledge_graph: Dict[str, Set[str]] = defaultdict(set)
        
    def generate_self_play_task(self, difficulty: float = 0.5) -> Tuple[str, TaskType]:
        """
        生成自博弈任务：模型自己出题自己答
        """
        task_types = list(TaskType)
        task_type = random.choice(task_types)
        
        # 基于知识图谱生成渐进式任务
        topics = list(self.knowledge_graph.keys()) if self.knowledge_graph else ["基础运算", "逻辑推理", "代码生成"]
        
        templates = {
            TaskType.CODE_GENERATION: [
                f"实现一个{difficulty:.1f}难度的高性能{random.choice(topics)}算法",
                f"编写一个支持{random.choice(['并发', '异步', '流式'])}处理的{random.choice(topics)}函数",
                f"优化以下代码使其在{random.choice(['内存', '时间', 'IO'])}维度提升{int(difficulty * 50)}%",
            ],
            TaskType.MATH_REASONING: [
                f"证明：对于任意{random.choice(['素数', '自然数', '矩阵'])}，{random.choice(['费马小定理', '欧拉定理', '柯西不等式'])}成立",
                f"求解{random.choice(['微分方程', '积分方程', '优化问题'])}：{int(difficulty * 100)}个变量",
            ],
            TaskType.LOGIC_PUZZLE: [
                f"设计一个包含{int(difficulty * 10)}个约束条件的逻辑推理问题",
                f"构造一个{random.choice(['图论', '博弈论', '决策论'])}问题并给出完整求解过程",
            ],
        }
        
        task = random.choice(templates.get(task_type, templates[TaskType.CODE_GENERATION]))
        return task, task_type
    
    def adversarial_judge(self, output_a: str, output_b: str, task: str) -> Tuple[str, str, float]:
        """
        对抗性评判：模型自己评判自己的输出
        """
        # 模拟评判逻辑
        criteria = {
            "correctness": random.uniform(0.6, 1.0),
            "completeness": random.uniform(0.5, 1.0),
            "efficiency": random.uniform(0.4, 1.0),
            "readability": random.uniform(0.5, 1.0),
        }
        
        score_a = sum(criteria.values()) / len(criteria)
        score_b = score_a * random.uniform(0.85, 1.15)
        
        if score_a > score_b + 0.05:
            winner = "A"
            feedback = f"模型A胜出，综合评分{score_a:.3f} vs {score_b:.3f}"
        elif score_b > score_a + 0.05:
            winner = "B"
            feedback = f"模型B胜出，综合评分{score_b:.3f} vs {score_a:.3f}"
        else:
            winner = "draw"
            feedback = f"平局，评分{score_a:.3f} vs {score_b:.3f}"
        
        return winner, feedback, max(score_a, score_b)
    
    def run_self_play_round(self, model_a: Callable, model_b: Callable, 
                           difficulty: float) -> SelfPlayRound:
        """
        执行一轮自博弈
        """
        task, task_type = self.generate_self_play_task(difficulty)
        
        # 两个模型分别回答
        output_a = model_a(task)
        output_b = model_b(task)
        
        # 对抗性评判
        winner, feedback, quality = self.adversarial_judge(output_a, output_b, task)
        
        round_result = SelfPlayRound(
            task=task,
            task_type=task_type,
            player_a_output=output_a,
            player_b_output=output_b,
            winner=winner,
            judge_feedback=feedback,
            quality_score=quality,
        )
        
        self.self_play_history.append(round_result)
        
        # 高质量数据加入合成数据池
        if quality > 0.8:
            for output, source in [(output_a, "model_a"), (output_b, "model_b")]:
                data_point = SyntheticDataPoint(
                    prompt=task,
                    response=output,
                    task_type=task_type,
                    quality_score=quality,
                    diversity_score=random.uniform(0.3, 0.9),
                    generated_by="self_play",
                    source_model=source,
                )
                self.synthetic_data.append(data_point)
        
        return round_result
    
    def augment_data_via_mutation(self, base_data: List[SyntheticDataPoint], 
                                  mutation_rate: float = 0.3) -> List[SyntheticDataPoint]:
        """
        通过变异增强数据多样性
        """
        augmented = []
        for data in base_data:
            if random.random() < mutation_rate:
                # 添加噪声
                mutated_prompt = data.prompt + f" [变异: {random.choice(['重构', '泛化', '特化'])}]"
                mutated_response = data.response + f"\n# 变异版本_{random.randint(1, 100)}"
                
                augmented.append(SyntheticDataPoint(
                    prompt=mutated_prompt,
                    response=mutated_response,
                    task_type=data.task_type,
                    quality_score=data.quality_score * random.uniform(0.9, 1.0),
                    diversity_score=min(1.0, data.diversity_score + 0.2),
                    generated_by="mutation",
                    source_model=data.source_model,
                ))
        return augmented
    
    def curriculum_learning_schedule(self, round_number: int) -> float:
        """
        课程学习调度：随着训练进行，难度逐渐提升
        """
        # 从简单到困难的渐进式难度曲线
        base_difficulty = 0.3
        growth_rate = 0.02
        max_difficulty = 0.95
        
        difficulty = min(max_difficulty, base_difficulty + growth_rate * round_number)
        return difficulty
    
    def knowledge_distillation(self, teacher_outputs: List[str], 
                              student_capacity: int) -> List[str]:
        """
        知识蒸馏：将大模型知识压缩到小模型
        """
        # 模拟知识蒸馏过程
        distilled = []
        for output in teacher_outputs[:student_capacity]:
            # 压缩率
            compression_ratio = len(output) // 2
            distilled.append(output[:compression_ratio])
        return distilled
    
    def generate_training_report(self) -> Dict:
        """生成训练报告"""
        total_rounds = len(self.self_play_history)
        if total_rounds == 0:
            return {"status": "no_training_data"}
        
        wins = sum(1 for r in self.self_play_history if r.winner in ("A", "B"))
        draws = sum(1 for r in self.self_play_history if r.winner == "draw")
        avg_quality = np.mean([r.quality_score for r in self.self_play_history])
        
        return {
            "total_self_play_rounds": total_rounds,
            "total_synthetic_data": len(self.synthetic_data),
            "win_rate": wins / total_rounds if total_rounds > 0 else 0,
            "draw_rate": draws / total_rounds if total_rounds > 0 else 0,
            "avg_quality_score": avg_quality,
            "data_diversity": np.mean([d.diversity_score for d in self.synthetic_data]) if self.synthetic_data else 0,
            "knowledge_graph_nodes": len(self.knowledge_graph),
            "model_checkpoints": len(self.model_checkpoints),
        }

# 运行模拟
if __name__ == "__main__":
    system = SelfTrainingSystem()
    
    # 模拟两个模型
    def model_a(task):
        return f"模型A对'{task}'的回答：[详细解决方案]...\n代码实现：\n```python\ndef solution():\n    pass\n```"
    
    def model_b(task):
        return f"模型B对'{task}'的回答：[优化方案]...\n代码实现：\n```python\ndef optimized():\n    return None\n```"
    
    # 执行100轮自博弈
    print("开始完全自我训练模拟...")
    for round_num in range(100):
        difficulty = system.curriculum_learning_schedule(round_num)
        result = system.run_self_play_round(model_a, model_b, difficulty)
        
        if (round_num + 1) % 20 == 0:
            print(f"第{round_num+1}轮：难度{difficulty:.2f}，质量{result.quality_score:.3f}，胜者{result.winner}")
    
    # 数据增强
    augmented = system.augment_data_via_mutation(system.synthetic_data)
    system.synthetic_data.extend(augmented)
    
    # 生成报告
    report = system.generate_training_report()
    print("\n训练报告：")
    for key, value in report.items():
        print(f"  {key}: {value}")
    print(f"\n总数据量（含增强）：{len(system.synthetic_data)}")