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llive 完全解説 (7) — 「審査つき AI」: runtime_metadata × Approval Bus × Ed25519 audit chain

コンセプト hook: 多くの LLM agent は「結果のログ」しか残さない. しかし
AI が 自分自身を進化 させはじめると, 「いつ何を判断して何を変えたか」
の audit trail が無いと, 後でデバッグ不能 になる. llive はこれを
architecture level で解いた:

runtime_metadata = 1 推論ごとの構造化 metadata

Approval Bus = 重大変更を ledger 経由で human / policy が approve

Ed25519 + SHA-256 audit chain = ledger 改ざん防止

本日 (2026-05-21) 着地の E.4 governance = 集団進化の共謀検出 → Approval Bus 連携

= 「自己進化する AI が, 自分の決定を全て署名つきで残す」 という珍しい形.

0. 連載中での位置づけ

#24-00 series index
#24-01 4 層メモリ
#24-02 思考因子 × COG-MESH
#24-03 構造進化 × TRIZ × Z3
#24-04 B-series
#24-05 EvolutionLoop
#24-06 LLM backend non-transformer
#24-07 observability + governance (← 本記事)
#24-08 lleval

#24-03 の Z3 verifier が「個体内の構造変更を機械検証」だとすると, #24-07
は「個体間の挙動 + 個体集団の決定を audit trail で保存」. 検証と監査の
両輪.

1. なぜ Audit Chain が必須か

LLM agent が自分自身を書き換えはじめると, 「さっきの推論は何 commit の
構造で動いていたか」が分からなくなる. これは debugging だけでなく:

責任追跡 — 集団進化で「全派生が嘘の高得点を付け合った」とき, 誰が
最初に嘘をついたかを ledger で遡れる必要がある.
再現性 — 「あのとき出た結果」を後で再生するには構造 commit + memory
zone + Brief input + Approval verdict の全て record が要る.
法的 compliance — EU AI Act / 中国 AI 弁法 / 日本 G7 広島 process が
示す方向は「AI の決定は audit possible でなければならない」.

llive は Phase 4 (Production Security MVR, v0.3.0) でこの 3 つを 同時に
解いた.

2. runtime_metadata — 1 推論あたりの構造化 trace

llive の FitnessReport.runtime_metadata は free-form dict[str, str] だが
慣習的に以下を入れる:

signed_by: peer evaluation の署名者 id
gen: 世代番号
agg: aggregator strategy
commit_sha: ソース commit (CI 経由で注入)
model_id: 使用 LLM backend id

これにより 1 推論結果から 完全に再現 できる. 再現性は OSS LLM
inference の標準ではない — 多くの agent は seed すら記録しない.

3. Approval Bus — 構造的に変更を止める

src/llive/approval/bus.py の ApprovalBus:

request(action, payload, ...) → pending リストに入る.
policy が事前評価して Verdict.APPROVED / DENIED / None を返す.
None なら人手待ち.
人手 / policy の verdict は _ledger: list[ApprovalResponse] に append.
ledger=SqliteLedger を渡せば永続化 + 復元.

これは 架空の「Trust Score」 ではなく明示的な APPROVED/DENIED 状態
マシン. 沈黙 = denial (§AB4). 「曖昧な許可」が存在しない.

3.1 本日着地の E.4 governance 連携

CoevolutionGovernance.evaluate_generation (本日着地) が 1 世代の peer
matrix を見て 共謀疑い → ApprovalBus.request("coevolution.suspected_collusion", payload) を発火. payload には generation / collusion_score / n_agents.
人手が deny したら その世代の派生集団は採用されない という architecture
level の制御.

これは Constitutional AI / RLHF の human-in-the-loop を, architecture
level で代替する設計. 「prompt 最後にを加える」のような
弱い制御ではない.

4. Ed25519 + SHA-256 audit chain

src/llive/security/ 系. Phase 4 着地.

各 PeerEvaluationMatrix / ChangeOp / consolidation event は Ed25519 で
署名.
ledger に書き込むときに 直前の hash を含めて SHA-256 を計算 → next
block の prev_hash として使う. つまり blockchain-light.
これにより「過去の任意の record を改ざんすると, それ以降の hash が全て
ずれる」 → 改ざん即検出.

4.1 なぜ on-chain ではなく on-disk か

project_fullsense_ear_origin — llive は EAR + セキュリティ制約で
外部送信不可 の環境を想定. on-chain (Ethereum / Solana) は外部送信に
なるため不適. on-disk audit chain は外部依存ゼロで完結する.

5. honest disclosure

Ed25519 鍵管理は未解決 — 鍵を OS の secure store / HSM に保存する
module は未着地. 現状は環境変数 / file 経由でロード. これは v1.0 前に
解決必須.
Approval Bus の人手介在は scale しない — 派生集団 N=64 で 1 世代毎に
approval が出ると人手の負荷が 24 時間で破綻する. policy 自動評価で 80% を
通すのが現実解だが, policy が完璧に書ける保証はない.
runtime_metadata の sign は optional — signed_by フィールドは
慣習だが mandatory ではない. これを mandatory にすると Brief API の
互換が壊れる. 移行は v0.7 以降.

6. 本日 (2026-05-21) 着地サマリ

項目	状態
`CoevolutionGovernance` skeleton	本日着地
`CollusionDetector` (CE-06)	本日着地
`collusion_risk_score` (TonicRisk 連携, CE-08)	本日着地
`GovernanceReport` (frozen)	本日着地
28 ケース test PASS	本日着地
Ed25519 audit chain	Phase 4 着地済 (v0.3.0)
Approval Bus	C-1 着地済 (2026-05-16)
runtime_metadata 慣習	v0.B から運用中

7. Mermaid — governance 全体像

7.1 governance maturity を「文明レベル」で見る — 4D Kardashev radar (v0.I-C 先取り)

§3 の Approval Bus pass率 / §4 の audit chain 完全性 / §6 の peer eval cohesion
は, 単独で見ると「数字が良くなった」で終わる. v0.I-C (4D Kardashev Radar)
ではこれらを Energy / Knowledge / Coordination / Ethics の 4 軸 × 5 段階
(Type 0 → I → II → III → IV) の「文明レベル」スケールに束ねて, 個体 / 集団 /
メタ集団の 3 階層で同時計測する構想.

Ethics 軸はまさに本記事の Approval Bus pass率 + frozen gene 違反検出 + 規制
適合度のスコアで, governance maturity を「個体の躾」から「文明の成熟」まで
連続スケールで語れるようになる. 詳細要件は llive docs/requirements_v0.I_meta_evolution_and_cross_substrate.md §5 参照.

8. 期待値 — 次に来るもの

HSM / secure store 連携 — Ed25519 鍵管理を v1.0 で. Windows Credential
Store / macOS Keychain / Linux Keyring 経路.
policy 自動 evaluate の拡充 — Approval Bus の policy 引数で 80% を
自動通過させる規則を v0.7 で.
Audit Ledger UI — llove TUI で governance verdict ledger を時系列
可視化. F25 連携.

9. 2026-05-22 追記 — RUST-16 governance hot path 高速化

CoevolutionGovernance.evaluate_generation の中で最も計算量を食うのが
PeerEvaluationMatrix.collusion_score (NxN matrix の variance / symmetry /
concentration 3 指標) で, ここに 200-300 μs/call かかっていた.

本日 (2026-05-22) RUST-16 として numpy zero-copy で Rust kernel 化:

N	Python (numpy 既存)	Rust pyo3 zero-copy	speedup
8	217.82 us	1.89 us	x115.04
16	203.33 us	2.30 us	x88.54
32	237.68 us	5.28 us	x45.00
64	306.13 us	16.80 us	x18.22
avg	—	—	x66.70

実装は crates/llive_rust_ext/src/lib.rs:collusion_score_kernel + 5 parity
test (1e-6 tolerance). callers (CollusionDetector.check) は次 commit で
切替予定.

9.1 honest disclosure — 「numpy = 速い」も嘘

このゲインが大きいのは 「Rust が速い」だけでなく「numpy が小 NxN で遅い」
が主因. np.nanvar / np.corrcoef / np.nanmean の 3 つ重ねがけは
N<100 で Python overhead 支配で 200μs+/call. Rust の単純 C ループは 2μs/call.

governance 側で重要なのは:

Approval Bus 発火判定の latency が 100x 短くなる = N=64 派生集団でも
governance.evaluate_generation を 64Hz で回せる
TonicRiskMonitor の tick (collusion_risk_score を含む state を渡す)
も同等に速くなる
結果として **「governance を常時動かしても許容コスト」**になる

これがあれば「governance は重いから sampling だけ」の妥協が要らなくなる.
全派生 / 全世代の評価行列を audit chain に署名つきで残しても latency budget
内に収まる.

9.2 関連

docs/perf_comparison/2026-05-22_kernel_implementation_comparison.md —
全 3 kernel (RUST-15/16/17) の比較マトリクス
scripts/bench_collusion_score_5x_gate.py — N=8/16/32/64 5x gate bench
feedback_rust_usage_matters — Rust 化判断のチェックリスト

10. References

Bernstein, D. J. et al. (2012). High-speed high-security signatures (Ed25519).
Anderson, R. (2020). Security Engineering (3rd ed.) — audit trail / tamper-evidence の章.
EU AI Act (2024) / G7 Hiroshima AI Process (2023) — AI 決定の監査可能性.
完全リストは v0.6.0a1 リリース時に references.bib に同梱予定.

Series Navigation

English

llive Complete Guide (7) — "AI with Built-in Review": runtime_metadata × Approval Bus × Ed25519 audit chain

Concept hook: Most LLM agents keep only a "log of results". But once an
AI starts to evolve itself, without an audit trail of "when did it
decide what and change what" it becomes impossible to debug later.
llive solved this at the architecture level:

runtime_metadata = structured metadata per inference

Approval Bus = a human / policy approves significant changes through a ledger

Ed25519 + SHA-256 audit chain = tamper-protection for the ledger

E.4 governance, landed today (2026-05-21) = collusion detection in population evolution → Approval Bus linkage

= a rare shape where "a self-evolving AI leaves every one of its decisions signed."

0. Position within the series

#24-00 series index
#24-01 4-layer memory
#24-02 thought factors × COG-MESH
#24-03 structural evolution × TRIZ × Z3
#24-04 B-series
#24-05 EvolutionLoop
#24-06 LLM backend non-transformer
#24-07 observability + governance (← this article)
#24-08 lleval

If #24-03's Z3 verifier is "machine-verifying structural changes inside one
individual", then #24-07 is "persisting the inter-individual behaviour +
the decisions of the population as an audit trail". The two wheels of
verification and audit.

1. Why an audit chain is mandatory

Once an LLM agent starts rewriting itself, "which commit's structure was the
last inference running on" becomes impossible to know. This matters not only
for debugging:

Accountability tracking — when, in population evolution, "all variants
gave each other fake high scores", you need to trace back through the ledger
who lied first.
Reproducibility — to replay "the result we got back then" later, you need
records of the structure commit + memory zone + Brief input + Approval verdict,
all of them.
Legal compliance — the direction shown by the EU AI Act / China's AI
measures / Japan's G7 Hiroshima process is "AI decisions must be auditable."

llive solved these three simultaneously in Phase 4 (Production Security
MVR, v0.3.0).

2. runtime_metadata — a structured trace per inference

llive's FitnessReport.runtime_metadata is a free-form dict[str, str], but by
convention it holds:

signed_by: signer id of the peer evaluation
gen: generation number
agg: aggregator strategy
commit_sha: source commit (injected via CI)
model_id: id of the LLM backend used

With this, a single inference result is fully reproducible. Reproducibility
is not the standard for OSS LLM inference — many agents do not even record
the seed.

3. Approval Bus — structurally halting changes

ApprovalBus in src/llive/approval/bus.py:

request(action, payload, ...) → enters the pending list.
policy evaluates it up front and returns Verdict.APPROVED / DENIED / None.
None means it waits on a human.
The human / policy verdict is appended to _ledger: list[ApprovalResponse].
Pass ledger=SqliteLedger and you get persistence + restore.

This is not a fictional "Trust Score" but an explicit APPROVED/DENIED
state machine. Silence = denial (§AB4). There is no "ambiguous permission".

3.1 The E.4 governance linkage landed today

CoevolutionGovernance.evaluate_generation (landed today) looks at one
generation's peer matrix, and on suspected collusion fires
ApprovalBus.request("coevolution.suspected_collusion", payload). The payload
carries generation / collusion_score / n_agents. If a human denies it, that
generation's derived population is not adopted — an architecture-level control.

This is a design that substitutes Constitutional AI / RLHF's
human-in-the-loop at the architecture level. It is not a weak control
like "append <human_review> at the end of the prompt".

4. Ed25519 + SHA-256 audit chain

The src/llive/security/ family. Landed in Phase 4.

Each PeerEvaluationMatrix / ChangeOp / consolidation event is signed with
Ed25519.
When writing to the ledger, the SHA-256 is computed including the previous
hash → used as the next block's prev_hash. In other words,
blockchain-light.
This means "tamper with any past record and all subsequent hashes shift" →
tampering is detected immediately.

4.1 Why on-disk, not on-chain

project_fullsense_ear_origin — llive assumes an environment that, under EAR +
security constraints, cannot transmit externally. on-chain (Ethereum /
Solana) becomes external transmission, so it is unsuitable. An on-disk audit
chain completes with zero external dependency.

5. honest disclosure

Ed25519 key management is unsolved — the module that stores keys in the
OS secure store / HSM has not landed. Currently keys are loaded via env var /
file. This must be solved before v1.0.
The human intervention in the Approval Bus does not scale — at N=64
derived population, if an approval comes per generation the human load breaks
down within 24 hours. The realistic answer is to auto-pass 80% via the policy
evaluation, but there is no guarantee the policy can be written perfectly.
The signing of runtime_metadata is optional — the signed_by field is a
convention but not mandatory. Making it mandatory would break the
compatibility of the Brief API. The migration is from v0.7 onward.

6. Today's (2026-05-21) landing summary

Item	Status
`CoevolutionGovernance` skeleton	landed today
`CollusionDetector` (CE-06)	landed today
`collusion_risk_score` (TonicRisk linkage, CE-08)	landed today
`GovernanceReport` (frozen)	landed today
28-case test PASS	landed today
Ed25519 audit chain	already landed in Phase 4 (v0.3.0)
Approval Bus	already landed in C-1 (2026-05-16)
runtime_metadata convention	in use since v0.B

7. Mermaid — the governance overview

7.1 Seeing governance maturity as a "civilization level" — 4D Kardashev radar (v0.I-C preview)

The Approval Bus pass rate (§3) / the audit chain integrity (§4) / the peer eval
cohesion (§6), seen alone, just end at "the number got better". In v0.I-C (4D
Kardashev Radar) the idea is to bundle these onto a "civilization level" scale
of 4 axes — Energy / Knowledge / Coordination / Ethics — × 5 stages
(Type 0 → I → II → III → IV), measured simultaneously across the 3 tiers of
individual / population / meta-population.

The Ethics axis is exactly this article's score of Approval Bus pass rate +
frozen gene violation detection + regulatory conformity, letting us speak of
governance maturity on a continuous scale from "an individual's discipline" to
"a civilization's maturity". For detailed requirements see llive
docs/requirements_v0.I_meta_evolution_and_cross_substrate.md §5.

8. Expectations — what comes next

HSM / secure store integration — Ed25519 key management in v1.0. Via the
Windows Credential Store / macOS Keychain / Linux Keyring routes.
Expansion of policy auto-evaluation — a rule that auto-passes 80% through
the Approval Bus's policy argument, in v0.7.
Audit Ledger UI — visualize the governance verdict ledger in time series
in the llove TUI. F25 linkage.

9. 2026-05-22 addendum — RUST-16 governance hot path acceleration

The most compute-heavy part inside CoevolutionGovernance.evaluate_generation is
PeerEvaluationMatrix.collusion_score (the 3 metrics variance / symmetry /
concentration over an NxN matrix), and it was taking 200-300 μs/call here.

Today (2026-05-22), as RUST-16, we made it a Rust kernel with numpy
zero-copy:

N	Python (existing numpy)	Rust pyo3 zero-copy	speedup
8	217.82 us	1.89 us	x115.04
16	203.33 us	2.30 us	x88.54
32	237.68 us	5.28 us	x45.00
64	306.13 us	16.80 us	x18.22
avg	—	—	x66.70

The implementation is crates/llive_rust_ext/src/lib.rs:collusion_score_kernel

5 parity tests (1e-6 tolerance). The callers (CollusionDetector.check) are
scheduled to switch over in the next commit.

9.1 honest disclosure — "numpy = fast" is also a lie

This gain is large mainly because of not only "Rust is fast" but "numpy is
slow for small NxN". Stacking the three of np.nanvar / np.corrcoef /
np.nanmean is dominated by Python overhead at N<100, so 200μs+/call. Rust's
plain C loop is 2μs/call.

What matters on the governance side:

The latency of the Approval Bus firing decision becomes 100x shorter = even
with an N=64 derived population you can run governance.evaluate_generation at
64Hz
The TonicRiskMonitor tick (which passes state including
collusion_risk_score) also becomes equally fast
As a result it becomes "an acceptable cost even running governance
continuously"

With this, the compromise of "governance is heavy, so sampling only" is no
longer needed. Even leaving every variant's / every generation's evaluation
matrix signed in the audit chain fits within the latency budget.

9.2 Related

docs/perf_comparison/2026-05-22_kernel_implementation_comparison.md — the
comparison matrix of all 3 kernels (RUST-15/16/17)
scripts/bench_collusion_score_5x_gate.py — N=8/16/32/64 5x gate bench
feedback_rust_usage_matters — the checklist for the Rust-port decision

10. References

Bernstein, D. J. et al. (2012). High-speed high-security signatures (Ed25519).
Anderson, R. (2020). Security Engineering (3rd ed.) — the chapter on audit trail / tamper-evidence.
EU AI Act (2024) / G7 Hiroshima AI Process (2023) — auditability of AI decisions.
The full list will be bundled in references.bib at the v0.6.0a1 release.

Series Navigation

← Prev: llive Complete Guide (6) "Beyond the Transformer"
→ Next: llive Complete Guide (8) "Making the Glasses"
All: llive Complete Guide (0) — series index
repo: furuse-kazufumi/llive

中文

llive 完全解说 (7) — "带审查的 AI": runtime_metadata × Approval Bus × Ed25519 audit chain

概念 hook: 很多 LLM agent 只留下「结果的日志」. 但是当 AI 开始 进化自身 时,
如果没有「何时判断了什么、改了什么」的 audit trail, 就会 事后无法 debug.
llive 在 architecture level 解决了这一点:

runtime_metadata = 每次推断的结构化 metadata

Approval Bus = 重大变更经由 ledger 由 human / policy 来 approve

Ed25519 + SHA-256 audit chain = ledger 防篡改

本日 (2026-05-21) 落地的 E.4 governance = 群体进化的共谋检测 → Approval Bus 联动

= 一种少见的形态:「自我进化的 AI, 把自己的所有决定都带着签名留存下来」.

0. 在系列中的定位

#24-00 series index
#24-01 4 层记忆
#24-02 思考因子 × COG-MESH
#24-03 结构进化 × TRIZ × Z3
#24-04 B-series
#24-05 EvolutionLoop
#24-06 LLM backend non-transformer
#24-07 observability + governance (← 本文)
#24-08 lleval

如果说 #24-03 的 Z3 verifier 是「机器验证 个体内 的结构变更」, 那么 #24-07
就是「把 个体间 的行为 + 个体群体的决策作为 audit trail 保存」. 验证与审计的
两个轮子.

1. 为什么必须有审计链

一旦 LLM agent 开始重写自身,「刚才那次推断是跑在哪个 commit 的结构上」就变得
无从得知. 这不只关系到 debugging:

责任追踪 — 在群体进化中, 当「所有派生互相打了虚假的高分」时, 需要能够
通过 ledger 回溯出谁最先撒了谎.
可复现性 — 要在事后重放「当时得到的结果」, 就需要结构 commit + memory zone
- Brief input + Approval verdict 的全部 record.
法律 compliance — EU AI Act / 中国 AI 办法 / 日本 G7 广岛 process 所指示的
方向是「AI 的决定必须 audit possible (可审计)」.

llive 在 Phase 4 (Production Security MVR, v0.3.0) 同时解决了这三点.

2. runtime_metadata — 每次推断的结构化 trace

llive 的 FitnessReport.runtime_metadata 是 free-form dict[str, str], 但按惯例
放入以下内容:

signed_by: peer evaluation 的签名者 id
gen: 世代编号
agg: aggregator strategy
commit_sha: 源码 commit (经 CI 注入)
model_id: 所使用的 LLM backend id

由此可以从一次推断结果 完全复现. 可复现性 并非 OSS LLM inference 的标准 —
很多 agent 连 seed 都不记录.

3. Approval Bus — 结构性地阻止变更

src/llive/approval/bus.py 的 ApprovalBus:

request(action, payload, ...) → 进入 pending 列表.
policy 事先评估并返回 Verdict.APPROVED / DENIED / None.
None 则等待人工.
人工 / policy 的 verdict 会 append 到 _ledger: list[ApprovalResponse].
传入 ledger=SqliteLedger 即可持久化 + 恢复.

这并不是 虚构的「Trust Score」, 而是 明确的 APPROVED/DENIED 状态机.
沉默 = denial (§AB4).「模糊的许可」并不存在.

3.1 本日落地的 E.4 governance 联动

CoevolutionGovernance.evaluate_generation (本日落地) 查看一个世代的 peer matrix,
在 共谋疑似 时触发 ApprovalBus.request("coevolution.suspected_collusion", payload). payload 中包含 generation / collusion_score / n_agents. 如果人工
deny, 则 该世代的派生群体不被采用 — 一种 architecture level 的控制.

这是把 Constitutional AI / RLHF 的 human-in-the-loop 在 architecture
level 替代的设计. 不是像「在 prompt 末尾加上」那样的弱控制.

4. Ed25519 + SHA-256 audit chain

src/llive/security/ 系列. Phase 4 落地.

每个 PeerEvaluationMatrix / ChangeOp / consolidation event 都用 Ed25519
签名.
写入 ledger 时, 包含 前一个 hash 来计算 SHA-256 → 用作 next block 的
prev_hash. 也就是 blockchain-light.
由此「篡改过去的任意 record, 之后的 hash 就全部错位」 → 篡改立即被检测.

4.1 为什么是 on-disk 而非 on-chain

project_fullsense_ear_origin — llive 假设的是在 EAR + 安全约束下 不可外部发送
的环境. on-chain (Ethereum / Solana) 会构成外部发送, 因此不适合. on-disk audit
chain 以零外部依赖自我完结.

5. honest disclosure

Ed25519 密钥管理尚未解决 — 将密钥保存到 OS 的 secure store / HSM 的 module
尚未落地. 当前通过环境变量 / file 加载. 这必须在 v1.0 之前解决.
Approval Bus 的人工介入不 scale — 在派生群体 N=64、每一世代都产生 approval
时, 人工负荷会在 24 小时内崩溃. 现实解是用 policy 自动评估通过 80%, 但无法保证
policy 能写得完美.
runtime_metadata 的 sign 是 optional — signed_by 字段是惯例但并非
mandatory. 把它设为 mandatory 会破坏 Brief API 的兼容性. 迁移在 v0.7 之后.

6. 本日 (2026-05-21) 落地总结

项目	状态
`CoevolutionGovernance` skeleton	本日落地
`CollusionDetector` (CE-06)	本日落地
`collusion_risk_score` (TonicRisk 联动, CE-08)	本日落地
`GovernanceReport` (frozen)	本日落地
28 个用例 test PASS	本日落地
Ed25519 audit chain	Phase 4 已落地 (v0.3.0)
Approval Bus	C-1 已落地 (2026-05-16)
runtime_metadata 惯例	自 v0.B 起运行中

7. Mermaid — governance 全貌

7.1 把 governance maturity 当作「文明等级」来看 — 4D Kardashev radar (v0.I-C 先取)

§3 的 Approval Bus 通过率 / §4 的 audit chain 完整性 / §6 的 peer eval cohesion,
单独看就只会停在「数字变好了」. 在 v0.I-C (4D Kardashev Radar) 中, 设想把这些
束到 Energy / Knowledge / Coordination / Ethics 4 个轴 × 5 个阶段
(Type 0 → I → II → III → IV) 的「文明等级」尺度上, 在个体 / 群体 / 元群体的 3 个
层级上同时度量.

Ethics 轴正是本文的 Approval Bus 通过率 + frozen gene 违规检测 + 法规符合度的分数,
使我们能够把 governance maturity 用一条从「个体的管教」到「文明的成熟」的连续尺度
来叙述. 详细要件参见 llive docs/requirements_v0.I_meta_evolution_and_cross_substrate.md §5.

8. 期望值 — 接下来要来的

HSM / secure store 集成 — Ed25519 密钥管理放到 v1.0. 经由 Windows Credential
Store / macOS Keychain / Linux Keyring 路径.
policy 自动 evaluate 的扩充 — 用 Approval Bus 的 policy 参数让 80% 自动通过
的规则, 放到 v0.7.
Audit Ledger UI — 在 llove TUI 中把 governance verdict ledger 按时间序
可视化. F25 联动.

9. 2026-05-22 追记 — RUST-16 governance hot path 加速

CoevolutionGovernance.evaluate_generation 中最吃计算量的是
PeerEvaluationMatrix.collusion_score (NxN matrix 的 variance / symmetry /
concentration 3 指标), 这里曾花费 200-300 μs/call.

本日 (2026-05-22) 作为 RUST-16 用 numpy zero-copy 做成 Rust kernel:

N	Python (numpy 既有)	Rust pyo3 zero-copy	speedup
8	217.82 us	1.89 us	x115.04
16	203.33 us	2.30 us	x88.54
32	237.68 us	5.28 us	x45.00
64	306.13 us	16.80 us	x18.22
avg	—	—	x66.70

实现是 crates/llive_rust_ext/src/lib.rs:collusion_score_kernel + 5 个 parity
test (1e-6 tolerance). callers (CollusionDetector.check) 计划在下一 commit 切换.

9.1 honest disclosure — 「numpy = 快」也是谎言

这个 gain 之所以大, 主因是 不仅「Rust 快」, 还有「numpy 在小 NxN 上慢」.
np.nanvar / np.corrcoef / np.nanmean 三者叠加在 N<100 时由 Python overhead
支配, 达到 200μs+/call. Rust 的简单 C 循环是 2μs/call.

在 governance 侧重要的是:

Approval Bus 触发判定的 latency 缩短 100x = 即便 N=64 派生群体, 也能以 64Hz
跑 governance.evaluate_generation
TonicRiskMonitor 的 tick (传入包含 collusion_risk_score 的 state) 也同等变快
结果就是 「即便常时运行 governance 也是可接受的成本」

有了这个,「governance 很重, 所以只做 sampling」的妥协就不再需要. 即使把所有派生
/ 所有世代的评估矩阵带着签名留在 audit chain 里, 也能落在 latency budget 之内.

9.2 相关

docs/perf_comparison/2026-05-22_kernel_implementation_comparison.md —
全 3 kernel (RUST-15/16/17) 的比较矩阵
scripts/bench_collusion_score_5x_gate.py — N=8/16/32/64 5x gate bench
feedback_rust_usage_matters — Rust 化判断的检查清单

10. References

Bernstein, D. J. et al. (2012). High-speed high-security signatures (Ed25519).
Anderson, R. (2020). Security Engineering (3rd ed.) — audit trail / tamper-evidence 章节.
EU AI Act (2024) / G7 Hiroshima AI Process (2023) — AI 决定的可审计性.
完整列表将在 v0.6.0a1 发布时随 references.bib 一同附带.

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한국어

llive 완전 해설 (7) — "심사가 붙은 AI": runtime_metadata × Approval Bus × Ed25519 audit chain

콘셉트 hook: 많은 LLM agent 는 「결과의 로그」만 남긴다. 그러나 AI 가
자기 자신을 진화 시키기 시작하면, 「언제 무엇을 판단해서 무엇을 바꿨는가」
의 audit trail 이 없으면 나중에 디버그 불가능 해진다. llive 는 이것을
architecture level 에서 풀었다:

runtime_metadata = 1 추론마다의 구조화된 metadata

Approval Bus = 중대한 변경을 ledger 를 거쳐 human / policy 가 approve

Ed25519 + SHA-256 audit chain = ledger 변조 방지

오늘 (2026-05-21) 착지한 E.4 governance = 집단 진화의 공모 탐지 → Approval Bus 연계

= 「자기진화하는 AI 가, 자신의 결정을 전부 서명과 함께 남긴다」 라는 드문 형태.

0. 연재 속에서의 위치

#24-00 series index
#24-01 4 층 메모리
#24-02 사고 인자 × COG-MESH
#24-03 구조 진화 × TRIZ × Z3
#24-04 B-series
#24-05 EvolutionLoop
#24-06 LLM backend non-transformer
#24-07 observability + governance (← 본 기사)
#24-08 lleval

#24-03 의 Z3 verifier 가 「개체 내의 구조 변경을 기계 검증」이라면, #24-07
은 「개체 간의 거동 + 개체 집단의 결정을 audit trail 로 보존」. 검증과 감사의
두 바퀴.

1. 왜 Audit Chain 이 필수인가

LLM agent 가 자기 자신을 다시 쓰기 시작하면, 「방금 그 추론은 어느 commit 의
구조로 돌고 있었는가」를 알 수 없게 된다. 이것은 debugging 뿐 아니라:

책임 추적 — 집단 진화에서 「모든 파생이 거짓의 높은 점수를 서로 매겼다」
때, 누가 가장 먼저 거짓말을 했는지를 ledger 로 거슬러 올라갈 수 있어야 한다.
재현성 — 「그때 나온 결과」를 나중에 재생하려면 구조 commit + memory zone +
Brief input + Approval verdict 의 전부 record 가 필요하다.
법적 compliance — EU AI Act / 중국 AI 판법 / 일본 G7 히로시마 process 가
가리키는 방향은 「AI 의 결정은 audit possible (감사 가능) 이어야 한다」.

llive 는 Phase 4 (Production Security MVR, v0.3.0) 에서 이 셋을 동시에 풀었다.

2. runtime_metadata — 1 추론당 구조화된 trace

llive 의 FitnessReport.runtime_metadata 는 free-form dict[str, str] 이지만
관습적으로 다음을 넣는다:

signed_by: peer evaluation 의 서명자 id
gen: 세대 번호
agg: aggregator strategy
commit_sha: 소스 commit (CI 를 거쳐 주입)
model_id: 사용한 LLM backend id

이로써 1 추론 결과로부터 완전히 재현 할 수 있다. 재현성은 OSS LLM inference
의 표준이 아니다 — 많은 agent 는 seed 조차 기록하지 않는다.

3. Approval Bus — 구조적으로 변경을 멈춘다

src/llive/approval/bus.py 의 ApprovalBus:

request(action, payload, ...) → pending 리스트에 들어간다.
policy 가 사전에 평가하여 Verdict.APPROVED / DENIED / None 을 반환.
None 이면 사람 대기.
사람 / policy 의 verdict 는 _ledger: list[ApprovalResponse] 에 append.
ledger=SqliteLedger 를 넘기면 영속화 + 복원.

이것은 가상의 「Trust Score」 가 아니라 명시적인 APPROVED/DENIED 상태
머신. 침묵 = denial (§AB4). 「애매한 허가」가 존재하지 않는다.

3.1 오늘 착지한 E.4 governance 연계

CoevolutionGovernance.evaluate_generation (오늘 착지) 가 1 세대의 peer matrix
를 보고 공모 의심 → ApprovalBus.request("coevolution.suspected_collusion", payload) 를 발화. payload 에는 generation / collusion_score / n_agents. 사람이
deny 하면 그 세대의 파생 집단은 채용되지 않는다 는 architecture level 의 제어.

이것은 Constitutional AI / RLHF 의 human-in-the-loop 를, architecture
level 에서 대체하는 설계. 「prompt 마지막에 를 더한다」와 같은
약한 제어가 아니다.

4. Ed25519 + SHA-256 audit chain

src/llive/security/ 계열. Phase 4 착지.

각 PeerEvaluationMatrix / ChangeOp / consolidation event 는 Ed25519 로
서명.
ledger 에 쓸 때 직전의 hash 를 포함하여 SHA-256 을 계산 → next block 의
prev_hash 로 사용. 즉 blockchain-light.
이로써 「과거의 임의의 record 를 변조하면, 그 이후의 hash 가 전부 어긋난다」 →
변조 즉시 검출.

4.1 왜 on-chain 이 아니라 on-disk 인가

project_fullsense_ear_origin — llive 는 EAR + 보안 제약으로 외부 송신 불가
한 환경을 상정. on-chain (Ethereum / Solana) 은 외부 송신이 되므로 부적합. on-disk
audit chain 은 외부 의존 제로로 자기 완결한다.

5. honest disclosure

Ed25519 키 관리는 미해결 — 키를 OS 의 secure store / HSM 에 저장하는 module
은 미착지. 현재는 환경 변수 / file 을 거쳐 로드. 이것은 v1.0 전에 해결 필수.
Approval Bus 의 사람 개입은 scale 하지 않는다 — 파생 집단 N=64 에서 1 세대마다
approval 이 나오면 사람의 부하가 24 시간 안에 붕괴한다. policy 자동 평가로 80% 를
통과시키는 것이 현실해이지만, policy 를 완벽히 쓸 수 있다는 보장은 없다.
runtime_metadata 의 sign 은 optional — signed_by 필드는 관습이지만
mandatory 가 아니다. 이것을 mandatory 로 하면 Brief API 의 호환이 깨진다.
이행은 v0.7 이후.

6. 오늘 (2026-05-21) 착지 요약

항목	상태
`CoevolutionGovernance` skeleton	오늘 착지
`CollusionDetector` (CE-06)	오늘 착지
`collusion_risk_score` (TonicRisk 연계, CE-08)	오늘 착지
`GovernanceReport` (frozen)	오늘 착지
28 케이스 test PASS	오늘 착지
Ed25519 audit chain	Phase 4 착지 완료 (v0.3.0)
Approval Bus	C-1 착지 완료 (2026-05-16)
runtime_metadata 관습	v0.B 부터 운용 중

7. Mermaid — governance 전체상

7.1 governance maturity 를 「문명 레벨」로 본다 — 4D Kardashev radar (v0.I-C 선취)

§3 의 Approval Bus pass 율 / §4 의 audit chain 완전성 / §6 의 peer eval cohesion
은, 단독으로 보면 「숫자가 좋아졌다」로 끝난다. v0.I-C (4D Kardashev Radar) 에서는
이것들을 Energy / Knowledge / Coordination / Ethics 4 축 × 5 단계
(Type 0 → I → II → III → IV) 의 「문명 레벨」 척도로 묶어서, 개체 / 집단 / 메타 집단의
3 계층에서 동시에 계측하는 구상.

Ethics 축은 바로 본 기사의 Approval Bus pass 율 + frozen gene 위반 검출 + 규제
적합도의 점수로, governance maturity 를 「개체의 훈육」에서 「문명의 성숙」까지
연속 척도로 이야기할 수 있게 된다. 자세한 요건은 llive
docs/requirements_v0.I_meta_evolution_and_cross_substrate.md §5 참조.

8. 기댓값 — 다음에 오는 것

HSM / secure store 연계 — Ed25519 키 관리를 v1.0 에서. Windows Credential
Store / macOS Keychain / Linux Keyring 경로.
policy 자동 evaluate 의 확충 — Approval Bus 의 policy 인자로 80% 를 자동
통과시키는 규칙을 v0.7 에서.
Audit Ledger UI — llove TUI 에서 governance verdict ledger 를 시계열로
시각화. F25 연계.

9. 2026-05-22 추기 — RUST-16 governance hot path 고속화

CoevolutionGovernance.evaluate_generation 안에서 가장 계산량을 먹는 것이
PeerEvaluationMatrix.collusion_score (NxN matrix 의 variance / symmetry /
concentration 3 지표) 로, 여기에 200-300 μs/call 이 걸렸다.

오늘 (2026-05-22) RUST-16 으로 numpy zero-copy 로 Rust kernel 화:

N	Python (numpy 기존)	Rust pyo3 zero-copy	speedup
8	217.82 us	1.89 us	x115.04
16	203.33 us	2.30 us	x88.54
32	237.68 us	5.28 us	x45.00
64	306.13 us	16.80 us	x18.22
avg	—	—	x66.70

구현은 crates/llive_rust_ext/src/lib.rs:collusion_score_kernel + 5 parity
test (1e-6 tolerance). callers (CollusionDetector.check) 는 다음 commit 에서
전환 예정.

9.1 honest disclosure — 「numpy = 빠르다」도 거짓

이 게인이 큰 것은 「Rust 가 빠르다」뿐 아니라 「numpy 가 작은 NxN 에서 느리다」
가 주요 원인. np.nanvar / np.corrcoef / np.nanmean 의 3 개 겹쳐쓰기는
N<100 에서 Python overhead 지배로 200μs+/call. Rust 의 단순 C 루프는 2μs/call.

governance 측에서 중요한 것은:

Approval Bus 발화 판정의 latency 가 100x 짧아진다 = N=64 파생 집단이라도
governance.evaluate_generation 을 64Hz 로 돌릴 수 있다
TonicRiskMonitor 의 tick (collusion_risk_score 를 포함한 state 를 넘긴다) 도
동등하게 빨라진다
결과적으로 「governance 를 상시 돌려도 허용 가능한 비용」 이 된다

이것이 있으면 「governance 는 무거우니 sampling 만」 의 타협이 필요 없어진다.
모든 파생 / 모든 세대의 평가 행렬을 audit chain 에 서명과 함께 남겨도 latency
budget 안에 들어간다.

9.2 관련

docs/perf_comparison/2026-05-22_kernel_implementation_comparison.md —
전 3 kernel (RUST-15/16/17) 의 비교 매트릭스
scripts/bench_collusion_score_5x_gate.py — N=8/16/32/64 5x gate bench
feedback_rust_usage_matters — Rust 화 판단의 체크리스트

10. References

Bernstein, D. J. et al. (2012). High-speed high-security signatures (Ed25519).
Anderson, R. (2020). Security Engineering (3rd ed.) — audit trail / tamper-evidence 장.
EU AI Act (2024) / G7 Hiroshima AI Process (2023) — AI 결정의 감사 가능성.
완전 리스트는 v0.6.0a1 릴리스 시에 references.bib 에 동봉 예정.

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