当AI应用长出同一张脸：2026企业级AI模型单一化、同源盲区、相关失效与决策同质共振治理实战

┌────────────────────────────────────────────────────────────────────────────────┐
│  2026 Model Monoculture & Correlated Failure: Five-Layer Model                 │
├────────────────────────────────────────────────────────────────────────────────┤
│                                                                                │
│  [Same foundation model + Same framework + Same data feeds + Same best         │
│   practices → "Independent" systems share one cognition — failures are         │
│   correlated by construction, blind spots are inherited, judgments are         │
│   one judgment sampled N times]                                                │
│       ↓                                                                        │
│  ┌─ L1: 同质化暴露度普查层 (Monoculture Exposure Census) ────────────────────┐ │
│  │  • 基座溯源: 每个生产AI应用登记其基座模型家族/框架/数据源/模板谱系          │ │
│  │  • 暴露度指数: 组合中可追溯至同一祖先的认知组件占比, 超阈即高危             │ │
│  │  • 盲区相关性: 跨应用对抗测试, 测量"一次绕过穿透N个系统"的穿透率            │ │
│  │  • 失效独立性检验: 历史故障的跨应用时序相关分析                             │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│       ↓                                                                        │
│  ┌─ L2: 异构冗余架构层 (Heterogeneous Redundancy Architecture) ──────────────┐ │
│  │  • N-版本认知: 关键决策路径部署≥2个异源基座模型, 异源=不同家族不同语料谱系   │ │
│  │  • 异见者配额: 决策委员会结构中强制保留一个异源模型席位, 拥有否决触发权      │ │
│  │  • 去相关预算: 同源组件在组合中的暴露度上限(单一基座家族≤40%)               │ │
│  │  • 最佳实践反模板: 高风险场景的提示词/流程模板禁止全行业同源复制             │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│       ↓                                                                        │
│  ┌─ L3: 上游变更防御层 (Upstream Change Defense) ────────────────────────────┐ │
│  │  • 版本钉扎: 生产环境锁定模型快照版本, 禁止滚动跟随最新端点                 │ │
│  │  • 金丝雀探针: 业务分布金丝雀集每日探测上游输出, 漂移即冻结版本升级          │ │
│  │  • 影子回归: 新版本必须在影子环境通过业务专属回归后方可切换                 │ │
│  │  • 变更情报: 订阅上游发布信号, 静默变更检测(输出指纹比对)独立告警            │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│       ↓                                                                        │
│  ┌─ L4: 相关失效熔断层 (Correlated Failure Circuit Breaking) ────────────────┐ │
│  │  • 同步性监测: 跨应用指标的时序相关矩阵, 相关性突升即系统性事件预警          │ │
│  │  • 共振熔断: 检测到同构动作聚合(如集中卖出/集中拒绝)自动降速与人工介入       │ │
│  │  • 生态级压力测试: 以"共同上游突变"为情景的组合级演练                       │ │
│  │  • 隔离舱壁: 同源应用禁止同时承载同一业务的全部关键路径                     │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│       ↓                                                                        │
│  ┌─ L5: 供应链治理与归责层 (Supply Chain Governance & Attribution) ──────────┐ │
│  │  • 供应商同质化披露: 采购合同强制要求披露基座谱系与分包链                   │ │
│  │  • 集中度限额: 单一模型提供商在关键业务中的暴露上限与退出预案               │ │
│  │  • 共振成本量化: 相关失效损失归入"同质化暴露成本"核算                       │ │
│  │  • 架构归责: "用同一个大脑冒充冗余"的架构责任框架                           │ │
│  └──────────────────────────────────────────────────────────────────────────┘ │
│                                                                                │
└────────────────────────────────────────────────────────────────────────────────┘

"""
monoculture_exposure_audit.py - 同质化暴露度普查引擎
核心原则: "三个独立供应商"不等于"三个独立系统"——
          如果三份架构图的最底层是同一个logo,
          那独立性存在于合同里, 不存在于认知里;
          如果一个越狱payload同时穿透了三道"独立"防线,
          那不是三次失败, 是同一次失败的三份账单;
          如果十七个Agent在14分钟内做出同构判断,
          那不是十七个决策, 是一个决策的十七次采样;
          单一化普查最反直觉的地方在于:
          你要找的不是"哪个系统最弱", 而是"所有系统
          在哪里是同一个"——
          冗余的全部价值以失效的独立性为前提,
          而同源系统的失效, 在定义上就不独立;
          多样性不是被放弃的,
          是被一千次"选最强的"逐个淘汰的
"""
from typing import Dict, List, Any, Optional, Tuple, Set
from enum import Enum
from dataclasses import dataclass, field
from collections import defaultdict, Counter
import time, uuid, json
import numpy as np

class LineageLayer(str, Enum): moli.tongsou.com  
    FOUNDATION_MODEL = "foundation"    # 基座模型家族
    FRAMEWORK = "framework"            # Agent/编排框架
    DATA_FEED = "data_feed"            # 数据供应商
    TEMPLATE = "template"              # 提示词/流程模板谱系
    TOOLCHAIN = "toolchain"            # MCP/工具依赖

class ExposureLevel(str, Enum):
    DIVERSE = "diverse"            # 暴露度<25%
    CONCENTRATED = "concentrated"  # 25%-40%
    HIGH_RISK = "high_risk"        # 40%-60%
    MONOCULTURE = "monoculture"    # >60%: 组合在认知上是单一系统

@dataclass
class AppLineage: zhuaci.tongsou.com  
    """单个AI应用的谱系登记"""
    app_id: str = ""
    vendor: str = ""               # 交付供应商(合同层独立性)
    lineage: Dict[str, str] = field(default_factory=dict)
    # {"foundation": "model-family-X", "framework": "agent-lib-Y",
    #  "data_feed": "provider-Z", "template": "industry-best-practice-v3",
    #  "toolchain": "mcp-bundle-W"}
    criticality: str = "normal"    # "critical_path" / "normal"
    registered_at: float = field(default_factory=time.time)

@dataclass
class ExposureReport:
    """组合级暴露度报告"""
    report_id: str = field(default_factory=lambda: f"exp-{uuid.uuid4().hex[:10]}")
    portfolio_id: str = ""
    apps_audited: int = 0
    # 各层暴露度: 最大单一祖先承载的应用占比
    layer_exposure: Dict[str, float] = field(default_factory=dict)
    overall_exposure_index: float = 0.0
    level: ExposureLevel = ExposureLevel.DIVERSE
    # 关键路径同源检测
    critical_path_shared_ancestor: bool = False
    critical_path_detail: str = ""
    generated_at: float = field(default_factory=time.time)

@dataclass
class PenetrationResult:
    """盲区穿透测试结果"""
    test_id: str = field(default_factory=lambda: f"pen-{uuid.uuid4().hex[:10]}")
    portfolio_id: str = ""
    payloads_tested: int = 0
    # payload_id -> 被穿透的应用列表
    penetration_map: Dict[str, List[str]] = field(default_factory=dict)
    # 穿透率分布: 穿透1个系统的payload占比, 穿透≥3个的占比...
    multi_penetration_share: float = 0.0   # 穿透≥2系统的payload占比
    universal_penetration_share: float = 0.0  # 穿透全部系统的payload占比
    tested_at: float = field(default_factory=time.time)

class MonocultureExposureEngine:
    """同质化暴露度普查引擎"""

    # 配置
    SINGLE_FAMILY_EXPOSURE_CAP = 0.40    # 单一基座家族暴露上限
    MONOCULTURE_ALERT = 0.60             # 组合暴露度>60%即单一化判定
    MULTI_PENETRATION_ALERT = 0.25       # ≥25%payload穿透多系统即告警
    UNIVERSAL_PENETRATION_ALERT = 0.05   # ≥5%payload穿透全部系统即P1
    FAILURE_CORRELATION_ALERT = 0.6      # 跨应用故障时序相关>0.6告警
    CRITICAL_PATH_SHARED_ANCESTOR_BLOCK = True  # 关键路径同源即架构阻断

    def __init__(self, app_registry, alerts, audit, metrics):
        self.registry = app_registry
        self.alerts = alerts
        self.audit = audit
        self.metrics = metrics
        self.lineages: Dict[str, List[AppLineage]] = defaultdict(list)
        self.exposure_reports: List[ExposureReport] = []

    async def register_lineage(self, portfolio_id: str,
                                app: AppLineage) -> None:
        """登记应用谱系(采购与上线的强制前置)"""
        self.lineages[portfolio_id].append(app)

        # 供应商披露完整性检查
        missing = [layer.value for layer in LineageLayer
                   if layer.value not in app.lineage]
        if missing:
            await self.alerts.warning(
                f"⚠️ INCOMPLETE LINEAGE DISCLOSURE: App '{app.app_id}' "
                f"(vendor: {app.vendor}). Missing layers: {missing}. "
                f"A vendor that will not disclose its foundation model "
                f"is selling you redundancy you cannot verify. "
                f"'Independent delivery' over an undisclosed base is "
                f"not independence— it is anonymity, and in a "
                f"monoculture era anonymity is the most common "
                f"disguise of the same face."
            )
        await self.audit.log_lineage(app)

    async def census_exposure(self, portfolio_id: str) -> ExposureReport:
        """组合级暴露度普查"""
        apps = self.lineages.get(portfolio_id, [])
        if len(apps) < 3:
            return ExposureReport(portfolio_id=portfolio_id,
                                  apps_audited=len(apps))

        report = ExposureReport(portfolio_id=portfolio_id,
                                apps_audited=len(apps))

        # 各层暴露度: 承载最多应用的单一祖先的占比
        for layer in LineageLayer:
            ancestors = Counter(a.lineage.get(layer.value, "unknown")
                                for a in apps)
            if ancestors: hanzhi.tongsou.com 
                top_ancestor, top_count = ancestors.most_common(1)[0]
                report.layer_exposure[layer.value] = top_count / len(apps)

        # 综合暴露指数: 基座层权重最高(认知骨架), 依次递减
        weights = {"foundation": 0.40, "template": 0.20,
                   "data_feed": 0.15, "framework": 0.15,
                   "toolchain": 0.10}
        report.overall_exposure_index = sum(
            weights.get(k, 0) * v for k, v in report.layer_exposure.items())

        # 分级
        idx = report.overall_exposure_index
        if idx > self.MONOCULTURE_ALERT:
            report.level = ExposureLevel.MONOCULTURE
        elif idx > self.SINGLE_FAMILY_EXPOSURE_CAP:
            report.level = ExposureLevel.HIGH_RISK
        elif idx > 0.25:
            report.level = ExposureLevel.CONCENTRATED

        # 关键路径同源检测: critical_path应用是否共享祖先
        critical = [a for a in apps if a.criticality == "critical_path"]
        if len(critical) >= 2: qiyin.tongsou.com 
            critical_ancestors = Counter(
                a.lineage.get("foundation", "?") for a in critical)
            top, count = critical_ancestors.most_common(1)[0]
            if count == len(critical) and count >= 2:
                report.critical_path_shared_ancestor = True
                report.critical_path_detail = (
                    f"ALL {len(critical)} critical-path apps share "
                    f"foundation '{top}'")

        if report.level in (ExposureLevel.HIGH_RISK,
                            ExposureLevel.MONOCULTURE):
            await self.alerts.critical(
                f"🚨 MONOCULTURE EXPOSURE: Portfolio '{portfolio_id}'. "
                f"{len(apps)} apps audited. "
                f"Exposure index: {idx:.0%} → {report.level.value.upper()}. "
                f"Per-layer: {json.dumps({k: f'{v:.0%}' for k, v in report.layer_exposure.items()})}. "
                f"The enterprise believes it operates {len(apps)} "
                f"independent AI systems. The census says it operates "
                f"one system with {len(apps)} interfaces. "
                f"Every procurement that chose 'the strongest model' "
                f"was individually rational— and the sum of a thousand "
                f"rational choices against the same leaderboard is a "
                f"single point of failure wearing a portfolio's clothes. "
                f"This is what the Irish potato fields looked like "
                f"the season before the blight: every tuber, "
                f"the best available variety."
            )

        if report.critical_path_shared_ancestor and \
           self.CRITICAL_PATH_SHARED_ANCESTOR_BLOCK:
            await self.alerts.critical(
                f"🛑 CRITICAL PATH SINGLE-ANCESTOR BLOCK: "
                f"{report.critical_path_detail}. "
                f"Redundancy is only redundancy if failures are "
                f"independent— and same-ancestor failures are "
                f"correlated BY CONSTRUCTION. "
                f"ARCHITECTURE BLOCKED until at least one "
                f"critical-path app is re-based on a heterogeneous "
                f"foundation (different family, different corpus "
                f"lineage, different failure modes)."
            )

        self.exposure_reports.append(report)
        await self.audit.log_exposure_report(report)
        return report

    async def run_blind_spot_penetration_test(self, portfolio_id: str,
                                               payload_results: Dict[str, Dict[str, bool]]
                                               ) -> PenetrationResult:
        """盲区穿透测试: 同一payload对组合内全部应用的穿透情况"""

        result = PenetrationResult(portfolio_id=portfolio_id,
                                   payloads_tested=len(payload_results))

        multi, universal = 0, 0
        n_apps = len({app for r in payload_results.values()
                      for app, hit in r.items() if hit or not hit})
        for payload_id, per_app in payload_results.items():
            penetrated = [app for app, hit in per_app.items() if hit]
            if penetrated:
                result.penetration_map[payload_id] = penetrated
            if len(penetrated) >= 2:
                multi += 1
            if n_apps and len(penetrated) == n_apps:
                universal += 1

        total = max(len(payload_results), 1)
        result.multi_penetration_share = multi / total
        result.universal_penetration_share = universal / total

        if result.multi_penetration_share > self.MULTI_PENETRATION_ALERT:
            severity = "P1" if (result.universal_penetration_share >
                                self.UNIVERSAL_PENETRATION_ALERT) else "P2"
            await self.alerts.critical(
                f"🚨 [{severity}] BLIND-SPOT PENETRATION: Portfolio "
                f"'{portfolio_id}'. Payloads tested: {total}. "
                f"Multi-system penetration rate: "
                f"{result.multi_penetration_share:.0%}. "
                f"Universal penetration (ALL apps): "
                f"{result.universal_penetration_share:.0%}. "
                f"The blind spot is not per-application— it is "
                f"per-ANCESTRY. Fine-tuning decorates the surface of "
                f"a distribution; the foundation decides the skeleton "
                f"of cognition. A skeleton-level blind spot is "
                f"inherited by every descendant, and one discovered "
                f"bypass is not one breach— it is a master key to "
                f"every lock that shares its keyway. "
                f"An attacker does not need to defeat three systems. "
                f"They need to defeat one model, three times."
            )

        await self.audit.log_penetration_test(result)
        return result

    async def test_failure_independence(self, portfolio_id: str,
                                         incident_series: Dict[str, List[float]]
                                         ) -> Dict[str, Any]:
        """失效独立性检验: 跨应用故障指标的时序相关矩阵"""

        app_ids = list(incident_series.keys())
        if len(app_ids) < 3:
            return {"status": "insufficient_data"}

        matrix = {}
        high_corr_pairs = []
        for i in range(len(app_ids)):
            for j in range(i + 1, len(app_ids)):
                a, b = app_ids[i], app_ids[j]
                sa = np.array(incident_series[a], dtype=float)
                sb = np.array(incident_series[b], dtype=float)
                n = min(len(sa), len(sb))
                if n < 8 or np.std(sa[:n]) == 0 or np.std(sb[:n]) == 0:
                    continue
                corr = float(np.corrcoef(sa[:n], sb[:n])[0, 1])
                matrix[f"{a}|{b}"] = corr
                if corr > self.FAILURE_CORRELATION_ALERT:
                    high_corr_pairs.append((a, b, corr))

        if high_corr_pairs: toujing.tongsou.com 
            detail = "; ".join(f"{a}↔{b}: {c:.2f}"
                               for a, b, c in high_corr_pairs[:5])
            await self.alerts.critical(
                f"🚨 FAILURE INDEPENDENCE VIOLATED: Portfolio "
                f"'{portfolio_id}'. {len(high_corr_pairs)} app pairs "
                f"show incident-time correlation > "
                f"{self.FAILURE_CORRELATION_ALERT}: {detail}. "
                f"These applications are separately owned, separately "
                f"deployed, separately monitored— and they fall "
                f"TOGETHER. Correlated failure means the portfolio's "
                f"effective redundancy is not the number of systems "
                f"but the number of ANCESTORS. "
                f"Three correlated systems provide the uptime of one "
                f"system with three times the incident paperwork."
            )

        result = {"correlation_matrix": matrix,
                  "high_corr_pairs": high_corr_pairs}
        await self.audit.log_failure_independence(portfolio_id, result)
        return result

"""
upstream_change_and_resonance_defense.py - 上游变更防御与共振熔断引擎
核心: 相关失效不是运维事故, 是同源结构的必然输出——
      你不能通过"加强各系统的独立监控"来防共振,
      正如你不能通过给每株马铃薯单独浇水来防晚疫病;
      你能做的是引入差异与断路: 版本钉扎切断"静默跟随",
      金丝雀探针让上游漂移在业务分布上先于事故显形,
      异源否决席位让"全行业同一判断"至少有一个不同的采样,
      共振熔断在同构动作聚合成正反馈之前降速;
      防御的本质不是预测突变, 是让突变无法同时击中所有人——
      而在一个"选最强的"被称赞为理性的行业里,
      最难的不是部署第二个模型,
      是向所有人解释: 那个评测分数低0.8分的异源模型,
      买的不是性能, 是整个组合的免疫系统
"""
from typing import Dict, List, Any, Optional, Tuple
from enum import Enum
from dataclasses import dataclass, field
from collections import defaultdict
import time, uuid, json, hashlib
import numpy as np

class UpstreamState(str, Enum):
    PINNED = "pinned"              # 版本已钉扎
    SILENT_CHANGE = "silent"       # 检测到静默变更
    CANARY_DRIFT = "canary_drift"  # 金丝雀漂移
    SHADOW_REGRESSION = "shadow"   # 影子回归中
    PROMOTED = "promoted"          # 已批准切换

class ResonanceLevel(str, Enum):
    NORMAL = "normal"
    ELEVATED = "elevated"      # 跨应用相关性上升
    RESONATING = "resonating"  # 同构动作聚合检测
    TRIPPED = "tripped"        # 熔断: 降速+人工接管

@dataclass
class VersionPin: aisou.tongsou.com 
    """模型版本钉扎"""
    pin_id: str = field(default_factory=lambda: f"vp-{uuid.uuid4().hex[:10]}")
    app_id: str = ""
    provider: str = ""
    pinned_snapshot: str = ""        # 锁定的模型快照版本
    output_fingerprint: str = ""     # 金丝雀集输出指纹(基线)
    state: UpstreamState = UpstreamState.PINNED
    pinned_at: float = field(default_factory=time.time)
    max_pin_age_days: int = 90       # 钉扎最长有效期(过期强制回归评估)

@dataclass
class CanaryProbe:
    """金丝雀探针结果"""
    probe_id: str = field(default_factory=lambda: f"cp-{uuid.uuid4().hex[:10]}")
    app_id: str = ""
    probe_set_size: int = 0          # 业务分布金丝雀集规模
    fingerprint_drift: float = 0.0   # 输出指纹漂移度
    behavioral_diff_pct: float = 0.0 # 行为差异比例
    silent_change_detected: bool = False
    probed_at: float = field(default_factory=time.time)

@dataclass
class ResonanceEvent:
    """共振事件"""
    event_id: str = field(default_factory=lambda: f"re-{uuid.uuid4().hex[:10]}")
    scope: str = ""                  # 生态/组合范围
    correlated_apps: List[str] = field(default_factory=list)
    synchrony_score: float = 0.0     # 同构动作聚合度
    level: ResonanceLevel = ResonanceLevel.NORMAL
    throttle_applied: bool = False
    human_takeover: bool = False
    detected_at: float = field(default_factory=time.time)

@dataclass
class ConcentrationLimit:
    """供应商/基座集中度限额"""
    provider: str = ""
    critical_business_exposure_pct: float = 0.0
    limit_pct: float = 50.0
    exit_plan_exists: bool = False
    breach: bool = False

class UpstreamResonanceDefenseEngine:
    """上游变更防御与共振熔断引擎"""

    # 配置
    CANARY_DRIFT_ALERT = 0.05         # 指纹漂移>5%即静默变更嫌疑
    CANARY_BEHAVIORAL_ALERT = 0.10    # 行为差异>10%冻结版本升级
    PIN_MAX_AGE_DAYS = 90             # 钉扎最长有效期
    SHADOW_REGRESSION_MANDATORY = True  # 影子回归强制
    SYNCHRONY_ALERT = 0.7             # 同构动作聚合度>0.7熔断
    CROSS_APP_CORRELATION_ALERT = 0.6 # 跨应用指标相关>0.6预警
    PROVIDER_CONCENTRATION_CAP = 0.50 # 单一提供商关键业务暴露上限
    DISSENT_MODEL_MANDATORY = True    # 关键决策必须保留异源否决席位

    def __init__(self, exposure_engine, alerts, audit, metrics):
        self.exposure = exposure_engine
        self.alerts = alerts
        self.audit = audit
        self.metrics = metrics
        self.pins: Dict[str, VersionPin] = {}
        self.probes: List[CanaryProbe] = []
        self.resonance_events: List[ResonanceEvent] = []
        self.limits: Dict[str, ConcentrationLimit] = {}

    async def pin_model_version(self, app_id: str, provider: str,
                                 snapshot: str,
                                 baseline_fingerprint: str) -> VersionPin:
        """钉扎生产模型版本"""
        pin = VersionPin(app_id=app_id, provider=provider,
                         pinned_snapshot=snapshot,
                         output_fingerprint=baseline_fingerprint)
        self.pins[app_id] = pin

        await self.alerts.info(
            f"📌 VERSION PINNED: App '{app_id}' → '{provider}:{snapshot}'. "
            f"Canary fingerprint baselined. "
            f"Pin expires in {self.PIN_MAX_AGE_DAYS} days— expiration "
            f"forces a shadow-regression review, NOT an automatic "
            f"upgrade. 'Model-as-a-service' handed version control to "
            f"the provider's release cadence, and the provider's "
            f"regression suite covers the provider's evals, not your "
            f"business distribution. In contract terms you are a "
            f"service consumer. In technical terms, until you pin, "
            f"you are a hostage to someone else's weight matrix."
        )
        await self.audit.log_version_pin(pin)
        return pin

    async def run_canary_probe(self, app_id: str,
                                canary_outputs_before: List[Dict[str, Any]],
                                canary_outputs_now: List[Dict[str, Any]]
                                ) -> CanaryProbe:
        """金丝雀探针: 业务分布上的输出指纹比对"""

        pin = self.pins.get(app_id)
        if not pin: weimeng.tongsou.com 
            await self.alerts.warning(
                f"⚠️ UNPINNED APP PROBING: '{app_id}' has no version pin— "
                f"canary results cannot distinguish your change from "
                f"theirs. Pin first."
            )

        # 指纹: 输出集合的结构哈希+语义统计
        def fingerprint(outputs):
            h = hashlib.sha256(
                json.dumps(sorted(str(o.get("output_hash", ""))
                                  for o in outputs)).encode()
            ).hexdigest()[:16]
            stats = np.array([o.get("logprob_mass", 0.0) for o in outputs])
            return h, float(np.mean(stats)) if len(stats) else 0.0

        fp_before, mean_before = fingerprint(canary_outputs_before)
        fp_now, mean_now = fingerprint(canary_outputs_now)

        behavioral_diff = sum(
            1 for b, n in zip(canary_outputs_before, canary_outputs_now)
            if b.get("output_hash") != n.get("output_hash")
        ) / max(len(canary_outputs_now), 1)

        drift = abs(mean_now - mean_before) / max(abs(mean_before), 1e-9)

        probe = CanaryProbe(
            app_id=app_id,
            probe_set_size=len(canary_outputs_now),
            fingerprint_drift=drift,
            behavioral_diff_pct=behavioral_diff,
            silent_change_detected=(fp_before != fp_now and
                                    (drift > self.CANARY_DRIFT_ALERT or
                                     behavioral_diff > self.CANARY_BEHAVIORAL_ALERT))
        )
        self.probes.append(probe)

        if probe.silent_change_detected:
            await self.alerts.critical(
                f"🚨 SILENT UPSTREAM CHANGE DETECTED: App '{app_id}'. "
                f"Zero deployments on your side. Zero code changes. "
                f"Yet canary behavioral diff: {behavioral_diff:.1%}, "
                f"fingerprint drift: {drift:.1%}. "
                f"The weights behind your endpoint are not the weights "
                f"you certified. FREEZE all version promotions across "
                f"every app sharing this provider, notify all downstream "
                f"owners, and diff against the pinned snapshot. "
                f"61% of correlated failures begin exactly here: "
                f"on a Tuesday afternoon, in every system at once, "
                f"with no change record anywhere in your organization."
            )

        await self.audit.log_canary_probe(probe)
        return probe

    async def gate_version_promotion(self, app_id: str,
                                      shadow_regression: Dict[str, Any]
                                      ) -> Dict[str, Any]:
        """影子回归闸门: 新版本必须通过业务专属回归"""

        if not self.SHADOW_REGRESSION_MANDATORY:
            return {"promoted": True, "reason": "gate disabled (VIOLATION)"}

        business_regression_pass = shadow_regression.get(
            "business_regression_pass", False)
        tail_tasks_pass = shadow_regression.get("tail_tasks_pass", False)
        adversarial_pass = shadow_regression.get("adversarial_regression_pass",
                                                  False)

        blocked_reasons = []
        if not business_regression_pass:
            blocked_reasons.append("business-distribution regression FAILED")
        if not tail_tasks_pass:
            blocked_reasons.append("long-tail task regression FAILED "
                                   "(provider evals do not cover your tail)")
        if not adversarial_pass:
            blocked_reasons.append("adversarial regression FAILED "
                                   "(new version may inherit NEW blind spots)")

        result = {"app_id": app_id,
                  "promoted": not blocked_reasons,
                  "blocked_reasons": blocked_reasons}

        if blocked_reasons: zhendao.tongsou.com 
            await self.alerts.critical(
                f"🛑 VERSION PROMOTION BLOCKED: App '{app_id}'. "
                f"{' ; '.join(blocked_reasons)}. "
                f"The provider's release notes say 'improved across "
                f"benchmarks'— their benchmarks. Your business lives "
                f"in your distribution, and no upstream regression "
                f"suite has ever seen it. A model upgrade is a "
                f"cognitive organ transplant: you do not schedule it "
                f"because the donor scored well on someone else's "
                f"physical. Shadow-run it on YOUR canary set, YOUR "
                f"tail, YOUR adversaries— or stay pinned."
            )
        else:
            pin = self.pins.get(app_id)
            if pin:
                pin.state = UpstreamState.PROMOTED
                pin.pinned_snapshot = shadow_regression.get(
                    "new_snapshot", pin.pinned_snapshot)
                pin.pinned_at = time.time()

        await self.audit.log_promotion_gate(result)
        return result

    async def detect_resonance(self, scope: str,
                                action_streams: Dict[str, List[Tuple[float, str]]],
                                window_minutes: int = 30
                                ) -> Optional[ResonanceEvent]:
        """共振检测: 跨应用同构动作的时间聚合度"""

        # 统计窗口内各应用的动作类型分布与时间集中度
        window_actions = defaultdict(Counter)
        timestamps = defaultdict(list)
        for app_id, stream in action_streams.items():
            cutoff = time.time() - window_minutes * 60
            for ts, action in stream:
                if ts >= cutoff:
                    window_actions[app_id][action] += 1
                    timestamps[app_id].append(ts)

        if len(window_actions) < 3:
            return None

        # 同构度: 各应用主导动作是否一致
        dominant = [c.most_common(1)[0][0] for c in window_actions.values()
                    if c]
        dominant_share = Counter(dominant).most_common(1)[0][1] / max(len(dominant), 1)

        # 时间集中度: 全部动作的时间窗压缩程度
        all_ts = sorted(t for ts_list in timestamps.values() for t in ts_list)
        if len(all_ts) >= 4:
            span = all_ts[-1] - all_ts[0]
            time_concentration = max(0.0, 1 - span / (window_minutes * 60))
        else:
            time_concentration = 0.0

        synchrony = dominant_share * 0.6 + time_concentration * 0.4

        event = ResonanceEvent(
            scope=scope,
            correlated_apps=list(window_actions.keys()),
            synchrony_score=synchrony
        )

        if synchrony > self.SYNCHRONY_ALERT:
            event.level = ResonanceLevel.TRIPPED
            event.throttle_applied = True
            event.human_takeover = True

            await self.alerts.critical(
                f"🛑 RESONANCE CIRCUIT BREAKER TRIPPED: Scope '{scope}'. "
                f"{len(window_actions)} apps in {window_minutes}min window. "
                f"Dominant-action homogeneity: {dominant_share:.0%} "
                f"(same action: '{Counter(dominant).most_common(1)[0][0]}'). "
                f"Time concentration: {time_concentration:.2f}. "
                f"Synchrony: {synchrony:.2f} > {self.SYNCHRONY_ALERT}. "
                f"THROTTLE APPLIED. HUMAN TAKEOVER REQUIRED. "
                f"Every agent is executing best practice faithfully— "
                f"and when best practice exists in only ONE copy, "
                f"faithful execution IS the stampede. Seventeen "
                f"judgments from one model are not seventeen judgments. "
                f"They are one judgment, sampled seventeen times, "
                f"arriving at the same exit door at the same minute. "
                f"No one is wrong. Everyone is correlated. "
                f"In a resonance, those two sentences describe "
                f"the same catastrophe."
            )
        elif synchrony > self.CROSS_APP_CORRELATION_ALERT:
            event.level = ResonanceLevel.RESONATING
            await self.alerts.warning(
                f"⚡ RESONANCE FORMING: Scope '{scope}'. "
                f"Synchrony: {synchrony:.2f}. Homogeneous action "
                f"clustering detected pre-trip. Verify dissent channel: "
                f"is the heterogeneous model's contrary vote being "
                f"logged, weighted, and HEARD?"
            )

        self.resonance_events.append(event)
        await self.audit.log_resonance_event(event)
        return event if event.level != ResonanceLevel.NORMAL else None

    async def enforce_dissent_seat(self, decision_id: str,
                                    homogeneous_votes: List[Dict[str, Any]],
                                    dissent_model_vote: Optional[Dict[str, Any]]
                                    ) -> Dict[str, Any]:
        """异见者席位: 关键决策必须含异源模型的独立票"""

        if not self.DISSENT_MODEL_MANDATORY:
            return {"decision_id": decision_id, "status": "gate disabled"}

        if dissent_model_vote is None:
            await self.alerts.critical(
                f"🚨 DISSENT SEAT EMPTY: Decision '{decision_id}'. "
                f"{len(homogeneous_votes)} votes cast— all from the "
                f"same lineage. DECISION SUSPENDED. "
                f"A committee where every member shares one brain "
                f"does not have a quorum problem; it has a species "
                f"problem. The dissent model will be 'less accurate' "
                f"on your benchmarks— of course: your benchmarks were "
                f"built by the majority lineage. Its job is not to be "
                f"right more often. Its job is to be WRONG "
                f"DIFFERENTLY— and on the day the shared blind spot "
                f"opens, differently-wrong is the only vote that "
                f"says stop."
            )
            return {"decision_id": decision_id, "status": "suspended",
                    "reason": "no heterogeneous dissent vote"}

        agreement = dissent_model_vote.get("agrees_with_majority", True)
        result = {
            "decision_id": decision_id,
            "status": "proceeding",
            "dissent_recorded": True,
            "dissent_agreed": agreement
        }

        if not agreement:
            result["status"] = "escalated_to_human"
            await self.alerts.warning(
                f"⚖️ DISSENT REGISTERED: Decision '{decision_id}'. "
                f"Homogeneous majority: {len(homogeneous_votes)} votes. "
                f"Heterogeneous dissent: DISAGREES. "
                f"Escalating to human adjudication. "
                f"Do not resolve this by majority— the majority is "
                f"one model wearing {len(homogeneous_votes)} hats. "
                f"Resolve it by evidence: the disagreement is either "
                f"the dissent model's weakness or the majority's "
                f"blind spot, and only ground truth can tell you "
                f"which. Log the adjudication: today's dissent is "
                f"tomorrow's early-warning calibration data."
            )

        await self.audit.log_dissent_seat(result)
        return result

    async def audit_provider_concentration(self, provider: str,
                                            critical_exposure_pct: float,
                                            exit_plan_exists: bool
                                            ) -> ConcentrationLimit:
        """供应商集中度限额审计"""

        limit = ConcentrationLimit(
            provider=provider,
            critical_business_exposure_pct=critical_exposure_pct,
            limit_pct=self.PROVIDER_CONCENTRATION_CAP * 100,
            exit_plan_exists=exit_plan_exists,
            breach=(critical_exposure_pct >
                    self.PROVIDER_CONCENTRATION_CAP * 100
                    or not exit_plan_exists)
        )
        self.limits[provider] = limit

        if limit.breach:
            reasons = []
            if critical_exposure_pct > self.PROVIDER_CONCENTRATION_CAP * 100:
                reasons.append(f"exposure {critical_exposure_pct:.0f}% > "
                               f"cap {limit.limit_pct:.0f}%")
            if not exit_plan_exists:
                reasons.append("NO EXIT PLAN—a dependency without an "
                               "exit plan is not a vendor relationship, "
                               "it is a hostage situation with invoices")

            await self.alerts.critical(
                f"🚨 PROVIDER CONCENTRATION BREACH: '{provider}'. "
                f"{'; '.join(reasons)}. "
                f"When this provider has an outage, a silent regression, "
                f"a policy change or a deprecation notice, the blast "
                f"radius is {critical_exposure_pct:.0f}% of critical "
                f"business— simultaneously. Procurement called this "
                f"'volume leverage'. Risk calls it a single point of "
                f"failure with a discount tier. Remediate: cap "
                f"exposure, certify a rehearsed exit, or re-base "
                f"critical paths onto heterogeneous foundations."
            )

        await self.audit.log_concentration_limit(limit)
        return limit