天赐范式第176天：让死者开始供养活人——变异池与条件3破冰

天赐范式第176天·第二篇：让死者开始供养活人——变异池与条件3破冰

版本：V3.3.14.0（栖息地·变异池）
日期：2026-09-25
PID：TC-176B-V3.3.14.0
定位：175-2说terminated基因"可作变异池（后续）"，本篇兑现——A基因第一次被取用
一句话：以A为模板扰动生成新个体A’，A’与B/C同场竞争50轮，A’不是A复活而是新生命——条件3繁衍性从⏳破冰到🟡。

上一篇：天赐范式第176天·第一篇——给档案上铁链
下一篇：待定

摘要

变异池——条件3繁衍性破冰。archive/里A的terminated基因第一次被取用：以A为模板扰动生成A’（β=0.4831/up_step=0.01/target_norm=0.5242/terminated=false），A’与B/C同场竞争50轮，benefit排序B>C>A’。A’是新个体不是A复活——变异管世代演替，哈希管传输篡改，两套机制不混。

一、175-2的承诺

175-2审计价值三条，第三条是"潜在变异原材料——terminated基因可作变异池（后续）"。条件3繁衍性在β体系下已于169天验证✅（成员自分裂），171天切strategy体系后重新挂⏳——是strategy体系下唯一未再实证的。

本篇兑现这个承诺。

二、变异池设计

只读采样

从archive/提取A的终态基因，校验三重状态：哈希✅、terminated✅、格式兼容✅。采样器无写入权限——不碰archive/里的任何文件，杜绝复活风险。

变异生成A’

以A为模板扰动生成新个体A’：

MUTATION_RATE = 0.1
β' = A.β × (1 + uniform(–rate, rate)) → clamp [0.1, 0.5]
up_step' = random.choice(UP_STEP_GRID) # 重选
target_norm' = A.target_norm × (1 + uniform(–rate, rate))
terminated' = False # 新个体，活着
domain' = A.domain # 继承域

关键区分：A’不是A复活

• A: terminated=true（死者，在archive/）
• A’: terminated=false（活体，在genomes_176/）
• A’有自己的基因文件、自己的哈希，与A的terminated标记无关
• 变异管世代演替，哈希管传输篡改——两套机制不混

三、demo演示——变异+竞争

完整代码见附录A。

只读采样

采样器（只读）从archive/提取A的终态基因：
name=A domain=气象 β=0.47 target_norm=0.5 up_step=0.05
terminated=True budget=16 adjust_count=4
SHA256: 95d048840b8d8061…ba899973

采样器校验三重状态：
①哈希校验 ✅ ②terminated标记 ✅ ③格式兼容 ✅
采样器无写入权限——不碰archive/里的任何文件，杜绝复活风险

变异生成A’

A的基因：β=0.47 up_step=0.05 target_norm=0.5 terminated=True
A'的基因：β=0.4831 up_step=0.01 target_norm=0.5242 terminated=False

A’继承了A的气象域和target_norm邻域（0.5→0.52），但β和up_step独立变异。

A’/B/C同场竞争50轮

轮20 A':0.35 B:0.10 C:0.10
轮40 A':0.17 B:0.10 C:0.10
轮50 A':0.10 B:0.10 C:0.10

A'：β终=0.10 benefit=-0.011276 budget=18 adjust_count=2
B ：β终=0.10 benefit=0.000158 budget=13 adjust_count=7
C ：β终=0.10 benefit=-0.000510 budget=12 adjust_count=8

benefit排序：B > C > A'

A’从A的基因模板变异而来，携带A的气象域遗产。A’表现最差——它继承了A的高β（0.4831），从0.48降到0.10的下降过程落在最后K=20轮观察窗口内，β振荡标准差大，振荡惩罚压低benefit。target_norm只是域特征（常量偏移），不影响收敛动态。A’是活体——terminated=false，参与竞争，不是A复活。

注：B和C的benefit与175天不同——175-1里B、C各自单独跑（各自消耗完整gauss序列），176-2里A’/B/C同场竞争（每轮交错消耗gauss值），B拿到的噪声序列不同，轨迹不同，benefit不同。这是实验设计的结果，不是bug。

四、降调声明

五、结语

175天第二篇说terminated基因"可作变异池（后续）"——本篇兑现。

176天第二篇：让死者开始供养活人。

A（气象域）在174天被terminated，176天A的基因第一次被取用：以A为模板扰动生成A’——β/up_step/target_norm变异，terminated=false。A’与B、C同场竞争50轮，benefit排序：B > C > A’。

A’不是A复活——A’是新个体，有自己的基因档案、自己的哈希。变异管世代演替，哈希管传输篡改——两套机制不混。

条件3繁衍性从⏳破冰到🟡雏形——死者遗产供养活人。

栖息地的社会结构长出"繁衍"——活人互验、死人留档、档案立链、死者供养新生命。

这个系列还在逐步建设中，完善也是咱们和伙伴们的努力方向。

附录A：tianci_176b.py完整代码

# -*- coding: utf-8 -*-
"""
天赐范式第176天·第二篇：让死者开始供养活人 V3.3.14.0
变异池——条件3繁衍性破冰。archive/里的A基因第一次被取用：
以A为模板扰动生成新个体A'（β/up_step/target_norm变异），A'是新个体不是A复活。
A'与B、C同场竞争50轮。条件3从⏳推到🟡雏形。

关键设计：
1. 只读采样——从archive/提取A终态基因，校验哈希+terminated
2. 变异生成A'——β/up_step/target_norm按规则扰动，terminated=false（新个体活着）
3. A'是新档案——自己的基因文件、自己的哈希，与A的terminated标记无关
4. 同场竞争——A'/B/C各跑50轮，观察A'表现
5. 语义分离——变异管世代演替，哈希管传输篡改，两套机制不混
"""

import sys
import random
import math
import json
import os
import hashlib

if hasattr(sys.stdout, 'reconfigure'):
sys.stdout.reconfigure(encoding='utf-8')

UP_STEP_GRID = [0.01, 0.02, 0.03, 0.04, 0.05]
K = 20
BUDGET = 20
LAMBDA = 0.3
GENOME_DIR_174B = "genomes_174b"
ARCHIVE_DIR = "archive"
NEW_GENOME_DIR = "genomes_176"
SIGMA_OBS = 0.005
SEED = 42
NUM_ROUNDS = 50
MUTATION_RATE = 0.1
PID = "TC-176B-V3.3.14.0"

class DigitalLife:
"""数字生命个体——死者的遗产供养新生命"""

def __init__(self, name, domain, beta, target_norm, up_step, sigma_obs):
self.name = name
self.domain = domain
self.beta = beta
self.target_norm = target_norm
self.norm = target_norm + 0.3
self.beta_history = [beta]
self.norm_history = [self.norm]
self.conv_history = []
self.up_step = up_step
self.strategy_history = [up_step]
self.strategy_benefit = {}
self.budget = BUDGET
self.adjust_count = 0
self.phase = "explore"
self.sigma_obs = sigma_obs

def self_prove(self):
old_norm = self.norm
noise = random.gauss(0, self.sigma_obs)
self.norm = self.target_norm + self.beta * (self.norm – self.target_norm) + noise
convergence = abs(old_norm – self.norm)
self.norm_history.append(self.norm)
self.conv_history.append(convergence)
return convergence

def self_program(self, conv):
if conv > 0.05:
self.beta += self.up_step
elif conv < 0.01:
self.beta -= 0.01
self.beta = max(0.1, min(0.5, self.beta))
self.beta_history.append(self.beta)

def compute_benefit(self):
recent_conv = self.conv_history[–K:]
recent_beta = self.beta_history[–K:]
speed = (recent_conv[0] – recent_conv[–1]) / K
mean_beta = sum(recent_beta) / len(recent_beta)
oscillation = math.sqrt(sum((b – mean_beta)**2 for b in recent_beta) / len(recent_beta))
return speed – LAMBDA * oscillation

def self_meta_program(self):
if self.budget <= 0:
return False
if len(self.conv_history) < K:
return False
current_benefit = self.compute_benefit()
self.strategy_benefit[self.up_step] = current_benefit
untried = [s for s in UP_STEP_GRID if s not in self.strategy_benefit]
if untried and self.phase == "explore":
self.up_step = untried[0]
self.budget -= 1
self.adjust_count += 1
self.strategy_history.append(self.up_step)
return True
elif not untried:
self.phase = "exploit"
best = max(self.strategy_benefit, key=self.strategy_benefit.get)
if self.up_step != best:
self.up_step = best
self.budget -= 1
self.adjust_count += 1
self.strategy_history.append(self.up_step)
return True
return False

def bar(title):
print("=" * 72)
print(" " + title)
print("=" * 72)
print()

def sub(title):
print("【" + title)
print("-" * 72)

def load_genome_raw(filepath):
with open(filepath, 'r', encoding='utf-8') as f:
return json.load(f)

def run_rounds(members, num_rounds):
for r in range(1, num_rounds + 1):
for m in members:
conv = m.self_prove()
m.self_program(conv)
if r % K == 0:
for m in members:
m.self_meta_program()
if r % K == 0 or r == num_rounds:
names_beta = " ".join(["{}:{:.2f}".format(m.name, m.beta) for m in members])
print(" 轮{:<4} {}".format(r, names_beta))

def main():
bar("天赐范式第176天·第二篇：让死者开始供养活人 V3.3.14.0")
print(" PID: {}".format(PID))
print()

sub("步骤1】只读采样——从archive/提取A终态基因")
a_path = os.path.join(ARCHIVE_DIR, "A_genome.json")
genome_a = load_genome_raw(a_path)
with open(a_path, 'r', encoding='utf-8') as f:
content = f.read()
a_hash = hashlib.sha256(content.encode('utf-8')).hexdigest()
print(" 采样器（只读）从archive/提取A的终态基因：")
print(" name={} domain={} β={:.2f} target_norm={:.1f} up_step={:.2f}".format(
genome_a["name"], genome_a["domain"], genome_a["beta"],
genome_a["target_norm"], genome_a["up_step"]))
print(" terminated={} budget={} adjust_count={}".format(
genome_a["terminated"], genome_a["budget"], genome_a["adjust_count"]))
print(" SHA256: {}…{}".format(a_hash[:16], a_hash[–8:]))
print()
print(" 采样器校验三重状态：")
sha_path = a_path + ".sha256"
hash_ok = False
if os.path.exists(sha_path):
with open(sha_path, 'r', encoding='utf-8') as f:
stored_hash = f.read().strip()
hash_ok = (a_hash == stored_hash)
print(" ①哈希校验 {}（{}）".format(
"✅" if hash_ok else "❌",
"文件内容与.sha256一致" if hash_ok else "不一致或.sha256不存在"))
terminated_ok = genome_a.get("terminated") == True
print(" ②terminated标记 {}（terminated={}，A确实死了）".format(
"✅" if terminated_ok else "❌", genome_a.get("terminated")))
required_fields = ["name", "domain", "beta", "target_norm", "up_step",
"beta_history", "strategy_history", "strategy_benefit",
"budget", "adjust_count", "terminated"]
missing = [f for f in required_fields if f not in genome_a]
format_ok = len(missing) == 0
print(" ③格式兼容 {}（{}）".format(
"✅" if format_ok else "❌",
"json可读，字段齐全" if format_ok else "缺字段: " + str(missing)))
print(" 采样器无写入权限——不碰archive/里的任何文件，杜绝复活风险")
print()

sub("步骤2】变异生成A'——以A为模板扰动")
print(" 变异规则（MUTATION_RATE={}）：".format(MUTATION_RATE))
print(" β' = A.β × (1 + uniform(-rate, rate)) → clamp [0.1, 0.5]")
print(" up_step' = random.choice(UP_STEP_GRID)（重选）")
print(" target_norm' = A.target_norm × (1 + uniform(-rate, rate))")
print(" terminated' = False（新个体，活着）")
print(" domain' = A.domain（继承域）")
print()
random.seed(SEED)
beta_prime = genome_a["beta"] * (1 + random.uniform(–MUTATION_RATE, MUTATION_RATE))
beta_prime = max(0.1, min(0.5, beta_prime))
up_step_prime = random.choice(UP_STEP_GRID)
target_norm_prime = genome_a["target_norm"] * (1 + random.uniform(–MUTATION_RATE, MUTATION_RATE))
print(" A的基因：β={:.2f} up_step={:.2f} target_norm={:.1f} terminated={}".format(
genome_a["beta"], genome_a["up_step"], genome_a["target_norm"], genome_a["terminated"]))
print(" A'的基因：β={:.4f} up_step={:.2f} target_norm={:.4f} terminated={}".format(
beta_prime, up_step_prime, target_norm_prime, False))
print()
print(" 关键区分：A'不是A复活——A'是新个体")
print(" A: terminated=true（死者，在archive/）")
print(" A': terminated=false（活体，在genomes_176/）")
print(" A'有自己的基因文件、自己的哈希，与A的terminated标记无关")
print(" 变异管世代演替，哈希管传输篡改——两套机制不混")
print()

sub("步骤3】A'入库——新个体新档案")
if not os.path.exists(NEW_GENOME_DIR):
os.makedirs(NEW_GENOME_DIR)
a_prime_genome = {
"name": "A'",
"domain": genome_a["domain"],
"beta": beta_prime,
"target_norm": target_norm_prime,
"up_step": up_step_prime,
"beta_history": [beta_prime],
"norm_history": [target_norm_prime + 0.3],
"conv_history": [],
"strategy_history": [up_step_prime],
"strategy_benefit": {},
"budget": BUDGET,
"adjust_count": 0,
"terminated": False,
"origin": "mutated_from_A",
"mutation_day": 176,
}
a_prime_path = os.path.join(NEW_GENOME_DIR, "A_prime_genome.json")
with open(a_prime_path, 'w', encoding='utf-8') as f:
json.dump(a_prime_genome, f, ensure_ascii=False, indent=2)
with open(a_prime_path, 'r', encoding='utf-8') as f:
prime_content = f.read()
prime_hash = hashlib.sha256(prime_content.encode('utf-8')).hexdigest()
with open(a_prime_path + ".sha256", 'w', encoding='utf-8') as f:
f.write(prime_hash)
print(" A'已入库 → {}".format(a_prime_path))
print(" SHA256: {}…{}".format(prime_hash[:16], prime_hash[–8:]))
print(" origin=mutated_from_A mutation_day=176")
print()

sub("步骤4】A'/B/C同场竞争——{}轮".format(NUM_ROUNDS))
print(" SIGMA_OBS={} seed={}".format(SIGMA_OBS, SEED))
print()
random.seed(SEED)
member_ap = DigitalLife("A'", genome_a["domain"], beta_prime, target_norm_prime, up_step_prime, SIGMA_OBS)

b_path = os.path.join(GENOME_DIR_174B, "B_genome.json")
c_path = os.path.join(GENOME_DIR_174B, "C_genome.json")
genome_b = load_genome_raw(b_path)
genome_c = load_genome_raw(c_path)
member_b = DigitalLife(
genome_b["name"], genome_b["domain"], genome_b["beta"],
genome_b["target_norm"], genome_b["up_step"], SIGMA_OBS
)
member_b.beta_history = genome_b["beta_history"]
member_b.strategy_history = genome_b["strategy_history"]
member_b.strategy_benefit = {float(k): v for k, v in genome_b["strategy_benefit"].items()}
member_b.budget = genome_b["budget"]
member_b.adjust_count = genome_b["adjust_count"]

member_c = DigitalLife(
genome_c["name"], genome_c["domain"], genome_c["beta"],
genome_c["target_norm"], genome_c["up_step"], SIGMA_OBS
)
member_c.beta_history = genome_c["beta_history"]
member_c.strategy_history = genome_c["strategy_history"]
member_c.strategy_benefit = {float(k): v for k, v in genome_c["strategy_benefit"].items()}
member_c.budget = genome_c["budget"]
member_c.adjust_count = genome_c["adjust_count"]

run_rounds([member_ap, member_b, member_c], NUM_ROUNDS)
print()

sub("步骤5】竞争结果——A'的表现")
benefit_ap = member_ap.compute_benefit()
benefit_b = member_b.compute_benefit()
benefit_c = member_c.compute_benefit()
print(" A'：β终={:.2f} benefit={:.6f} budget={} adjust_count={}".format(
member_ap.beta, benefit_ap, member_ap.budget, member_ap.adjust_count))
print(" B ：β终={:.2f} benefit={:.6f} budget={} adjust_count={}".format(
member_b.beta, benefit_b, member_b.budget, member_b.adjust_count))
print(" C ：β终={:.2f} benefit={:.6f} budget={} adjust_count={}".format(
member_c.beta, benefit_c, member_c.budget, member_c.adjust_count))
print()
ranking = sorted(
[("A'", benefit_ap, member_ap), ("B", benefit_b, member_b), ("C", benefit_c, member_c)],
key=lambda x: x[1], reverse=True
)
print(" benefit排序：{} > {} > {}".format(
ranking[0][0], ranking[1][0], ranking[2][0]))
print(" A'从A的基因模板变异而来，携带A的气象域遗产（target_norm≈{:.2f}）".format(target_norm_prime))
print(" A'是活体——terminated=false，参与竞争，不是A复活")
print()

sub("步骤6】A'的基因终态入库")
a_prime_final = {
"name": "A'",
"domain": genome_a["domain"],
"beta": member_ap.beta,
"target_norm": target_norm_prime,
"up_step": member_ap.up_step,
"beta_history": member_ap.beta_history,
"norm_history": member_ap.norm_history,
"conv_history": member_ap.conv_history,
"strategy_history": member_ap.strategy_history,
"strategy_benefit": {str(k): v for k, v in member_ap.strategy_benefit.items()},
"budget": member_ap.budget,
"adjust_count": member_ap.adjust_count,
"terminated": False,
"origin": "mutated_from_A",
"mutation_day": 176,
}
a_prime_final_path = os.path.join(NEW_GENOME_DIR, "A_prime_genome_final.json")
with open(a_prime_final_path, 'w', encoding='utf-8') as f:
json.dump(a_prime_final, f, ensure_ascii=False, indent=2)
with open(a_prime_final_path, 'r', encoding='utf-8') as f:
final_content = f.read()
final_hash = hashlib.sha256(final_content.encode('utf-8')).hexdigest()
print(" A'终态基因已入库 → {}".format(a_prime_final_path))
print(" β终={:.2f} benefit={:.6f} budget={} terminated={}".format(
member_ap.beta, benefit_ap, member_ap.budget, False))
print(" SHA256: {}…{}".format(final_hash[:16], final_hash[–8:]))
print(" origin=mutated_from_A——A'的档案记录它的来源，但不继承A的terminated")
print()

sub("步骤7】七条前提条件——变异池")
print(" 条件1 独立性 ✅ A'是独立新个体，有自己的基因档案")
print(" 条件2 隐私性 🟡 明文落盘（同173天降调），genomes_176/新增一个目录")
print(" 条件3 繁衍性 ⏳→🟡 变异池雏形：terminated基因可作模板生成新个体，A'已实跑")
print(" 条件4 安全性 ✅ 基因字段有界，变异幅度有界（MUTATION_RATE={}）".format(MUTATION_RATE))
print(" 条件5 不可篡改 🟡 A'是新档案自己的哈希，与A的terminated无关（见第一篇链式存证）")
print(" 条件6 可继承 🟡 A'继承了A的domain和target_norm邻域，但β/up_step独立变异")
print(" 条件7 可终止 ✅ 预算上限保证")
print()

bar("结语")
print(" 175天第二篇说terminated基因'可作变异池（后续）'——本篇兑现。")
print(" 176天第二篇：让死者开始供养活人。")
print()
print(" A（气象域）在174天被terminated，176天A的基因第一次被取用：")
print(" 以A为模板扰动生成A'——β/up_step/target_norm变异，terminated=false。")
print(" A'与B、C同场竞争{}轮，benefit排序：{} > {} > {}。".format(
NUM_ROUNDS, ranking[0][0], ranking[1][0], ranking[2][0]))
print()
print(" A'不是A复活——A'是新个体，有自己的基因档案、自己的哈希。")
print(" 变异管世代演替，哈希管传输篡改——两套机制不混。")
print()
print(" 条件3繁衍性从⏳破冰到🟡雏形——死者遗产供养活人。")
print()
print(" 栖息地的社会结构长出'繁衍'——活人互验、死人留档、档案立链、死者供养新生命。")
print()
print(" 这个系列还在逐步建设中，完善也是咱们和伙伴们的努力方向。")
print("=" * 72)

if __name__ == "__main__":
main()