多Agent涌现协调六栈：从编排到群体自生实测

graph LR
    subgraph "协调光谱六层"
        L1["L1: 硬编码拓扑<br/>静态角色分配"] --> L2["L2: 模板编排<br/>DAG/流水线预设"]
        L2 --> L3["L3: 动态路由<br/>运行时拓扑切换"]
        L3 --> L4["L4: 引导自组织<br/>GNN/扩散生成拓扑"]
        L4 --> L5["L5: 随机自组织<br/>Agent自主构建拓扑"]
        L5 --> L6["L6: 涌现社会规范<br/>自发形成协调规则"]
    end

    style L1 fill:#ff6b6b,color:#fff
    style L2 fill:#ffa502,color:#fff
    style L3 fill:#ffd93d,color:#333
    style L4 fill:#6bcb77,color:#fff
    style L5 fill:#4d96ff,color:#fff
    style L6 fill:#9b59b6,color:#fff

graph TD
    subgraph "G-Designer 架构"
        A[任务描述 T] --> B[Agent能力矩阵<br/>C₁, C₂, ..., Cₙ]
        B --> C[VGNN编码器<br/>Graph Neural Network]
        C --> D[拓扑采样器<br/>Gumbel-Softmax]
        D --> E[稀疏通信图 G*]
        E --> F[多Agent执行]
        F --> G[任务奖励 R]
        G -->|REINFORCE梯度| C
    end

    style C fill:#4d96ff,color:#fff
    style D fill:#6bcb77,color:#fff
    style E fill:#ffd93d,color:#333

# G-Designer拓扑生成核心伪代码
import torch
import torch_geometric

class GDesignerTopologyGenerator(nn.Module):
    def __init__(self, num_agents, agent_dim, hidden_dim=128):
        super().__init__()
        # 变分GNN编码器
        self.gnn_encoder = VGNN(agent_dim, hidden_dim)
        # 拓扑采样头（Gumbel-Softmax实现可微采样）
        self.topology_head = nn.Linear(hidden_dim * 2, 1)
        self.temperature = nn.Parameter(torch.tensor(1.0))

    def forward(self, agent_capabilities, task_embedding):
        # 1. 编码Agent能力
        node_features = self.gnn_encoder(agent_capabilities, task_embedding)

        # 2. 构建候选边特征（全连接候选）
        num_agents = node_features.size(0)
        edge_pairs = torch.combinations(
            torch.arange(num_agents), r=2
        )
        edge_features = torch.cat([
            node_features[edge_pairs[:, 0]],
            node_features[edge_pairs[:, 1]]
        ], dim=-1)

        # 3. Gumbel-Softmax采样稀疏拓扑
        edge_logits = self.topology_head(edge_features).squeeze(-1)
        topology = gumbel_softmax(edge_logits, tau=self.temperature, hard=True)

        return topology, edge_pairs  # 返回稀疏邻接矩阵

graph TD
    subgraph "AgentNet 去中心化协调"
        A1[Agent α] -->|检索历史交互| RAG[(共享RAG存储)]
        A2[Agent β] -->|检索历史交互| RAG
        A3[Agent γ] -->|检索历史交互| RAG
        RAG -->|返回伙伴推荐| A1
        RAG -->|返回伙伴推荐| A2
        RAG -->|返回伙伴推荐| A3

        A1 -->|评估任务适配度| E1[进化选择压力]
        A2 -->|评估任务适配度| E1
        A3 -->|评估任务适配度| E1
        E1 -->|优胜劣汰| A1
        E1 -->|优胜劣汰| A2
        E1 -->|优胜劣汰| A3
    end

    style RAG fill:#ff6b6b,color:#fff
    style E1 fill:#9b59b6,color:#fff

# AgentNet核心协调循环（简化）
class AgentNetCoordinator:
    def __init__(self, agents, rag_store):
        self.agents = agents
        self.rag = rag_store  # 共享RAG存储
        self.fitness_scores = {a.id: 0.0 for a in agents}

    def run_task(self, task):
        # 1. 每个Agent独立评估任务
        proposals = {}
        for agent in self.agents:
            # 通过RAG检索历史交互，找到最合适的伙伴
            partner_hints = self.rag.query(
                f"task:{task.type} capability:{agent.capability}",
                top_k=3
            )
            proposal = agent.propose(task, partner_hints)
            proposals[agent.id] = proposal

        # 2. 去中心化投票（无中心编排器）
        votes = self._decentralized_vote(proposals)

        # 3. 执行获胜方案
        winner = max(votes, key=votes.get)
        result = self._execute(proposals[winner])

        # 4. 进化更新：根据结果调整适应度
        self._evolve(winner, result)

        # 5. 将交互记录写入RAG供未来检索
        self.rag.store({
            "task": task,
            "winner": winner,
            "result_quality": result.quality,
            "topology": self._current_topology()
        })

        return result

    def _evolve(self, winner_id, result):
        # 进化选择：成功策略的适应度增加
        self.fitness_scores[winner_id] += result.quality
        # 低适应度Agent被替换或重新初始化
        for aid, score in self.fitness_scores.items():
            if score < self._threshold():
                self._reset_agent(aid)

graph TD
    subgraph "SelfOrg 自组织过程"
        T1["t=0: 全连接随机图<br/>每个Agent随机选目标"] --> T2["t=1: 执行任务<br/>收集响应质量Q"]
        T2 --> T3{"Q > 阈值?"}
        T3 -->|是| T4["保留当前边<br/>增强连接权重"]
        T3 -->|否| T5["随机扰动<br/>以概率p重连"]
        T4 --> T6["DAG演化<br/>收敛到最优拓扑"]
        T5 --> T2
        T6 --> T7["涌现的专化结构<br/>无需人工设计"]
    end

    style T7 fill:#9b59b6,color:#fff
    style T6 fill:#4d96ff,color:#fff

# SelfOrg框架核心（简化）
import random

class SelfOrgFramework:
    def __init__(self, agents, perturbation_rate=0.15):
        self.agents = {a.id: a for a in agents}
        self.topology = self._init_random_dag(agents)
        self.perturbation_rate = perturbation_rate

    def _init_random_dag(self, agents):
        """初始化随机有向无环图"""
        ids = [a.id for a in agents]
        dag = {i: random.sample(
            [j for j in ids if j != i], 
            k=random.randint(1, 3)
        ) for i in ids}
        return dag

    def run_round(self, task):
        results = {}
        for agent_id, targets in self.topology.items():
            agent = self.agents[agent_id]
            # 1. 从上游Agent接收消息
            incoming = self._collect_incoming(agent_id)
            # 2. 处理任务并生成输出
            output = agent.process(task, context=incoming)
            # 3. 发送给下游Agent
            for target_id in targets:
                self._send(agent_id, target_id, output)
            results[agent_id] = output.quality

        # 4. 随机拓扑扰动
        self._perturb_topology(results)

        return self._aggregate(results)

    def _perturb_topology(self, quality_scores):
        """基于响应质量的随机拓扑扰动"""
        for agent_id in self.topology:
            if random.random() < self.perturbation_rate:
                if quality_scores[agent_id] < self._avg_quality():
                    # 低质量：以更高概率重连
                    self.topology[agent_id] = self._random_rewire(agent_id)
                # 高质量：保持不变或微调
        self._ensure_dag_property()

# GTD扩散生成拓扑核心伪代码
class GuidedTopologyDiffusion:
    def __init__(self, num_agents, num_diffusion_steps=50):
        self.num_steps = num_diffusion_steps
        self.denoiser = GraphDenoiser(num_agents)
        self.reward_model = TopologyRewardModel()

    def generate(self, task_embedding, guidance_scale=3.0):
        # 1. 初始化：全连接噪声图
        num_edges = self.num_agents * (self.num_agents - 1) // 2
        noisy_adj = torch.bernoulli(torch.ones(num_edges) * 0.5)

        # 2. 迭代去噪
        for t in reversed(range(self.num_steps)):
            # 预测去噪后的拓扑
            adj_pred = self.denoiser(noisy_adj, t, task_embedding)

            # 用奖励模型引导去噪方向
            reward_grad = self.reward_model.gradient(adj_pred)
            adj_pred = adj_pred + guidance_scale * reward_grad

            # 离散化：概率采样保持可微性
            noisy_adj = gumbel_softmax(adj_pred, hard=True)

        return noisy_adj  # 最终稀疏拓扑

graph TD
    GOAL[研究目标] --> SUP[Supervisor Agent]
    SUP --> GEN[Generation Agents<br/>生成假设]
    GEN --> REF[Reflection Agents<br/>批判审视]
    REF --> RANK[Elo Ranking Tournament<br/>假设竞赛]
    RANK --> EVO[Evolution Agents<br/>假设进化]
    EVO --> PROX[Proximity Agents<br/>新颖性检查]
    PROX --> META[Meta-review Agent<br/>综合输出]
    META --> FINAL[最终研究提案]

    RANK -.->|Elo分数反馈| GEN
    RANK -.->|Elo分数反馈| EVO

    style RANK fill:#ff6b6b,color:#fff
    style EVO fill:#9b59b6,color:#fff
    style META fill:#4d96ff,color:#fff

sequenceDiagram
    participant A as Agent A
    participant B as Agent B
    participant C as Agent C
    participant D as Agent D

    Note over A,B: 第1-10轮：混乱期
    A->>B: 我选"blue"
    B->>C: 我选"azur"
    C->>D: 我选"cyan"
    D->>A: 我选"teal"

    Note over A,B: 第11-50轮：区域收敛
    A->>B: 我选"blue"
    B->>C: 我选"blue"
    C->>D: 我选"cyan"
    D->>A: 我选"teal"

    Note over A,B: 第51-100轮：全局涌现
    A->>B: 我选"blue"
    B->>C: 我选"blue"
    C->>D: 我选"blue"
    D->>A: 我选"blue"

    Note over A,B: 社会规范自发形成！

# 数字信息素协调伪代码
class DigitalPheromoneStore:
    def __init__(self):
        self.pheromones = {}  # {(任务类型, 方法): 强度}
        self.decay_rate = 0.95

    def deposit(self, task_type, method, quality):
        """Agent完成任务后留下信息素"""
        key = (task_type, method)
        self.pheromones[key] = self.pheromones.get(key, 0) + quality

    def evaporate(self):
        """信息素自然挥发"""
        for key in list(self.pheromones.keys()):
            self.pheromones[key] *= self.decay_rate
            if self.pheromones[key] < 0.01:
                del self.pheromones[key]

    def sense(self, task_type, top_k=3):
        """Agent根据信息素强度选择方法"""
        candidates = {k: v for k, v in self.pheromones.items() 
                      if k[0] == task_type}
        return sorted(candidates.items(), 
                      key=lambda x: -x[1])[:top_k]


class StigmergicAgent:
    def __init__(self, pheromone_store):
        self.pheromone_store = pheromone_store

    def choose_method(self, task):
        # 感知信息素，概率化选择
        trails = self.pheromone_store.sense(task.type)
        if trails:
            # 轮盘赌选择（信息素越强被选中概率越高）
            methods = [t[0][1] for t in trails]
            weights = [t[1] for t in trails]
            chosen = random.choices(methods, weights=weights, k=1)[0]
        else:
            chosen = self._random_method()  # 探索

        return chosen

    def execute(self, task):
        method = self.choose_method(task)
        result = self._run(method, task)
        # 留下信息素（结果越好，信息素越强）
        self.pheromone_store.deposit(task.type, method, result.quality)
        return result

graph LR
    subgraph "任务准确率 vs 通信轮数"
        direction TB
        A["G-Designer<br/>准确率: 85% | 轮数: 3.2"]
        B["GTD<br/>准确率: 83% | 轮数: 2.8"]
        C["AgentNet<br/>准确率: 87% | 轮数: 4.5"]
        D["SelfOrg<br/>准确率: 82% | 轮数: 6.1"]
        E["Co-Scientist<br/>准确率: 91% | 轮数: 8.3"]
        F["信息素<br/>准确率: 79% | 轮数: 5.4"]
    end