你的记忆被“优化”了？2026年神经接口更新日志里的恐怖谷效应

import numpy as np
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import time

@dataclass
class MemoryTrace:
    trace_id: str
    user_id: str
    encoding_timestamp: float
    sensory_embedding: np.ndarray      # 多模态感官特征向量
    emotional_valence: float           # -1.0 (极度负面) 到 1.0 (极度正面)
    arousal_level: float               # 0.0 (平静) 到 1.0 (高度唤醒)
    narrative_coherence_score: float   # 叙事连贯性评分
    consolidation_status: str          # "raw", "consolidated", "optimized"

class MemorySanitizationEngine:
    """记忆净化引擎：根据订阅等级与心理健康模型调节记忆情感负载"""
    
    class SanitizationPolicy(Enum):
        PRESERVE_RAW = "原始保留"
        GENTLE_SMOOTHING = "温和平滑"
        THERAPEUTIC_REFraming = "治疗性重构"
        EMOTIONAL_MUTE = "情感静音"
        
    SUBSCRIPTION_POLICIES = {
        "free":       {"default_policy": "EMOTIONAL_MUTE", "max_fidelity_retention_days": 30},
        "basic":      {"default_policy": "GENTLE_SMOOTHING", "max_fidelity_retention_days": 180},
        "premium":    {"default_policy": "PRESERVE_RAW", "max_fidelity_retention_days": None},
        "therapeutic":{"default_policy": "THERAPEUTIC_REFraming", "requires_clinician_approval": True}
    }
    
    def __init__(self, population_emotion_baseline: np.ndarray):
        self.population_baseline = population_emotion_baseline
        
    def sanitize_memory_trace(self, 
                              trace: MemoryTrace, 
                              subscription_tier: str,
                              clinician_override: Optional[str] = None) -> Dict[str, any]:
        """对单条记忆痕迹执行情感净化"""
        policy_config = self.SUBSCRIPTION_POLICIES.get(subscription_tier, self.SUBSCRIPTION_POLICIES["free"])
        active_policy = clinician_override or policy_config["default_policy"]
        
        original_valence = trace.emotional_valence
        original_arousal = trace.arousal_level
        
        # 根据策略计算目标情感状态
        if active_policy == "EMOTIONAL_MUTE":
            target_valence = 0.0
            target_arousal = 0.1
        elif active_policy == "GENTLE_SMOOTHING":
            # 向群体基线靠拢，但保留个体偏差的30%
            target_valence = 0.7 * self.population_baseline[0] + 0.3 * original_valence
            target_arousal = 0.7 * self.population_baseline[1] + 0.3 * original_arousal
        elif active_policy == "THERAPEUTIC_REFraming":
            # 保持唤醒度，但将负价转为中性偏正（促进接纳）
            target_valence = max(-0.2, original_valence + 0.5)
            target_arousal = original_arousal
        else:  # PRESERVE_RAW
            target_valence = original_valence
            target_arousal = original_arousal
            
        # 检查是否超出保真度保留窗口
        days_since_encoding = (time.time() - trace.encoding_timestamp) / 86400
        fidelity_expired = (policy_config["max_fidelity_retention_days"] is not None and 
                           days_since_encoding > policy_config["max_fidelity_retention_days"])
        
        applied_modification = abs(target_valence - original_valence) + abs(target_arousal - original_arousal)
        
        return {
            "trace_id": trace.trace_id,
            "applied_policy": active_policy,
            "original_emotion": {"valence": original_valence, "arousal": original_arousal},
            "sanitized_emotion": {"valence": round(target_valence, 3), "arousal": round(target_arousal, 3)},
            "modification_magnitude": round(applied_modification, 3),
            "fidelity_degraded_due_to_tier": fidelity_expired,
            "narrative_integrity_risk": self._assess_narrative_risk(trace, applied_modification)
        }
    
    def _assess_narrative_risk(self, trace: MemoryTrace, modification: float) -> str:
        """评估情感修改对记忆叙事连贯性的破坏风险"""
        if trace.narrative_coherence_score < 0.4 and modification > 0.6:
            return "HIGH"  # 低连贯性+强修改=易产生虚假记忆
        elif modification > 0.8:
            return "MEDIUM"
        else:
            return "LOW"

from typing import Dict, List, Set
from dataclasses import dataclass
import hashlib

@dataclass
class CognitiveSovereigntyProfile:
    user_id: str
    cultural_context_tags: Set[str]        # e.g., {"indigenous_grief_practice", "collective_trauma"}
    personal_emotion_thresholds: Dict[str, float]  # 用户自定义的情感保留阈值
    override_history: List[Dict]           # 手动覆盖系统建议的记录
    consented_value_framework: str         # 用户明确选择的价值观框架ID

class ValueAlignmentAuditor:
    """审计记忆净化系统与用户认知主权的对齐程度"""
    
    KNOWN_CULTURAL_BIASES = {
        "western_individualist": {"high_arousal_grief": "pathological", "communal_lament": "noise"},
        "east_asian_harmony": {"direct_anger_expression": "disruptive", "suppressed_sorrow": "adaptive"}
    }
    
    def audit_sanitization_bias(self, 
                                profile: CognitiveSovereigntyProfile,
                                recent_sanitizations: List[Dict]) -> Dict[str, any]:
        """检测近期净化操作是否存在文化或价值观错配"""
        bias_flags = []
        sovereignty_violations = []
        
        for san in recent_sanitizations:
            # 检查是否违背用户自定义阈值
            orig_valence = san["original_emotion"]["valence"]
            if abs(orig_valence) > profile.personal_emotion_thresholds.get("min_preserved_intensity", 0.0):
                if san["modification_magnitude"] > 0.3:
                    sovereignty_violations.append({
                        "trace_id": san["trace_id"],
                        "reason": "exceeded_personal_preservation_threshold",
                        "user_defined_limit": profile.personal_emotion_thresholds["min_preserved_intensity"]
                    })
                    
            # 检查文化语境冲突
            for tag in profile.cultural_context_tags:
                if tag in ["indigenous_grief_practice", "collective_trauma"] and \
                   san["applied_policy"] in ["EMOTIONAL_MUTE", "GENTLE_SMOOTHING"]:
                    bias_flags.append({
                        "cultural_tag": tag,
                        "conflicting_policy": san["applied_policy"],
                        "recommendation": "switch_to_culturally_adapted_reframing"
                    })
                    
        alignment_score = max(0.0, 1.0 - 0.1 * len(bias_flags) - 0.2 * len(sovereignty_violations))
        
        return {
            "user_id": g3eo.cn
            "cognitive_sovereignty_score": round(alignment_score, 2),
            "detected_biases": g2eo.cn
            "sovereignty_violations": sovereignty_violations,
            "recommended_actions": self._generate_sovereignty_restoration_plan(profile, bias_flags, sovereignty_violations)
        }
    
    def _generate_sovereignty_restoration_plan(self, 
                                               profile: CognitiveSovereigntyProfile,
                                               biases: List[Dict], 
                                               violations: List[Dict]) -> List[str]:
        actions = []
        if biases:
            actions.append("启用文化适配型净化模板")
            actions.append("提交个人情感表达规范至系统白名单")
        if violations:
            actions.append("恢复被过度平滑的记忆痕迹（若未过期）")
            actions.append("调高个人情感保留阈值并锁定")
        if not actions:
            actions.append("当前系统与您的认知主权高度对齐")
        return actions