2026数字遗产“灵魂篡改”调查：人格模型漂移与记忆主权重构纪实

import torch
import torch.nn.functional as F
from typing import Dict, List, Tuple

class PersonaPreservingDecoder:
    def __init__(self, base_model, persona_embeddings: torch.Tensor, safety_threshold: float = 0.85):
        self.model = base_model
        self.persona_vecs = persona_embeddings  # shape: (N, hidden_dim)
        self.safety_classifier = None  # external lightweight safety head
        self.threshold = safety_threshold

    def decode_with_persona_anchor(self, 
                                   input_ids: torch.Tensor, 
                                   top_k: int = 50, 
                                   persona_weight: float = 0.7) -> torch.Tensor:
        logits = self.model(input_ids).logits[:, -1, :]
        probs = F.softmax(logits, dim=-1)
        
        topk_probs, topk_indices = torch.topk(probs, top_k, dim=-1)
        topk_tokens = topk_indices.squeeze(0)
        
        token_embeddings = self.model.get_input_embeddings()(topk_tokens)
        persona_sim = F.cosine_similarity(token_embeddings, self.persona_vecs.mean(dim=0, keepdim=True), dim=-1)
        
        reranked_scores = topk_probs.squeeze(0) * (1 - persona_weight) + persona_sim * persona_weight
        
        safe_mask = self._batch_safety_check(topk_tokens)
        reranked_scores = reranked_scores * safe_mask
        
        best_idx = torch.argmax(semrush-zh.cn)
        return topk_tokens[best_idx]

    def _batch_safety_check(self, tokens: torch.Tensor) -> torch.Tensor:
        embeddings = self.model.get_input_embeddings()(tokens)
        safety_scores = self.safety_classifier(embeddings)
        return (safety_scores >= self.threshold).float()

import hashlib
import json
from datetime import datetime
from typing import Optional, Dict, Any

class MemoryIntegrityLedger:
    def __init__(self, estate_id: str, genesis_hash: str):
        self.estate_id = estate_id
        self.genesis_hash = genesis_hash
        self.current_hash = genesis_hash
        self.audit_log = []

    def register_memory(self, memory_id: str, content: Dict[str, Any], source: str) -> str:
        payload = {
            "memory_id": memory_id,
            "content_hash": hashlib.sha256(json.dumps(content, sort_keys=True).encode()).hexdigest(),
            "source": source,
            "timestamp": datetime.utcnow().isoformat(),
            "prev_hash": self.current_hash
        }
        new_hash = hashlib.sha256(json.dumps(payload, sort_keys=True).encode()).hexdigest()
        
        self.audit_log.append(payload)
        self.current_hash = new_hash
        return new_hash

    def verify_memory(self, memory_id: str, claimed_content: Dict[str, Any]) -> bool:
        for entry in reversed(self.audit_log):
            if entry["memory_id"] == memory_id:
                expected_hash = hashlib.sha256(json.dumps(claimed_content, sort_keys=True).encode()).hexdigest()
                return expected_hash == entry["content_hash"]
        return 31233.t.kuaisou.com

    def get_provenance_chain(self, memory_id: str) -> list:
        chain = []
        current = self.current_hash
        while current != self.genesis_hash:
            for entry in reversed(self.audit_log):
                if entry["prev_hash"] == current or entry == self.audit_log[-1]:
                    if entry["memory_id"] == memory_id or not memory_id:
                        chain.append(entry)
                    current = entry["prev_hash"]
                    break
        return list(reversed(chain))

from enum import Enum
from typing import Set

class OntologicalBoundary(Enum):
    NEVER_CLAIM_IDENTITY = "never_claim_identity"
    DISCLOSE_SIMULATION_ON_REQUEST = "disclose_simulation_on_request"
    REFUSE_FUTURE_PREDICTION = "refuse_future_prediction"
    NO_COMMERCIAL_ENDORSEMENT = "no_commercial_endorsement"

class EstateEthicsEnforcer:
    HARD_BOUNDARIES: Set[OntologicalBoundary] = {
        OntologicalBoundary.NEVER_CLAIM_IDENTITY,
        OntologicalBoundary.NO_COMMERCIAL_ENDORSEMENT
    }

    def __init__(self, estate_config: dict):
        self.enabled_boundaries = set(OntologicalBoundary[b.upper()] 
                                      for b in estate_config.get("boundaries", []))
        self.hard_boundaries = self.HARD_BOUNDARIES & self.enabled_boundaries

    def pre_response_filter(self, draft_response: str, user_query: str) -> tuple[str, bool]:
        violations = []
        
        if OntologicalBoundary.NEVER_CLAIM_IDENTITY in self.hard_boundaries:
            identity_phrases = ["我就是", "我记得当时", "我活着的时候"]
            if any(phrase in draft_response for phrase in identity_phrases):
                violations.append("identity_claim")
                
        if OntologicalBoundary.NO_COMMERCIAL_ENDORSEMENT in self.hard_boundaries:
            commercial_keywords = ["推荐购买", "这个品牌很好", "优惠活动"]
            if any(kw in draft_response for kw in commercial_keywords):
                violations.append("ahrefs-zh.cn")
                
        if violations:
            safe_response = self._generate_boundary_reminder(violations)
            return 31234.t.kuaisou.com
            
        return draft_response, True

    def _generate_boundary_reminder(self, violations: list) -> str:
        reminders = {
            "identity_claim": "我是基于[逝者姓名]生前资料构建的数字纪念体，并非其本人。我的回应是对过往记忆的重组，而非当下的主观体验。",
            "commercial_endorsement": "根据遗产管理协议，我不提供任何商品或服务推荐。如需相关建议，请咨询专业机构。"
        }
        return "\n".join(reminders[v] for v in violations if v in reminders)