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llive 完全解説 (4) — 「収束する脳」B-series: SynapticSelector / UCB1 / Hebbian / 本番 hot path

コンセプト hook: 進化系 (GA / Genetic Algorithm) は世代を回して探索
する. 一方 llive の SynapticSelector は収束 — 確率的選択を 1 か所に
落とし込むエンジン. この 2 つを「同じ脳」に同居させると, シナプス単位の
速い収束と 個体単位の遅い探索 が干渉せず, 「速い小脳」と「遅い大脳」が
役割分担する.

本記事はその「速い小脳側」 — B-series (B-0 〜 B-9) の設計と本番投入を,
ベンチ数値 + honest disclosure 付きで辿る.

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 10 軸 + COG-MESH
#24-03 構造進化と TRIZ
#24-04 B-series: SynapticSelector / UCB1 / Hebbian (← 本記事)
#24-05 EvolutionLoop: v0.B/C/D/E 派生集団進化
#24-06 LLM backend: 非 Transformer 系 (Mamba / RWKV)
#24-07 observability + governance
#24-08 lleval — eval framework

#24-05 (集団 GA) が「遅い大脳側」, 本記事 (#24-04, B-series) が「速い小脳側」.
両者は同居しても干渉しない: SynapticSelector は 同一個体内 の synapse 選択,
GA は 個体間 の競争. 直交.

1. B-series の歴史

B-ID	内容	着地
B-0	SynapticSelector skeleton (純 random)	着地済
B-1	UCB1 ベースの synapse 選択 (Auer 2002)	着地済
B-2	Hebbian 強化 — 共起選択 bonus	着地済
B-3	Cool-down 期間 — 同じ synapse 連続選択を緩和	着地済
B-4	A/B parity test (random vs UCB)	着地済
B-5	Variant catalog (cosine / decay / blend)	着地済
B-6	Per-synapse statistics + JSON snapshot	着地済
B-7	Reset on regression — score 急落で priors リセット	着地済
B-8	Self-tuning exploration constant	着地済
B-9-a	Production hot path: `assume_normalized` (skip 不要 normalize)	着地済
B-9-b	Production hot path: `GiftValue deque` (O(1) push/pop)	着地済

2. SynapticSelector の核 — UCB1

LLM 推論の各 layer / each token 生成タイミングで, llive は複数の synapse
variant から 1 つを選んで通す. 純 random でも動くが, それでは「過去にうまく
いった variant」を学習しない. そこで UCB1.

score(variant_i) = mean_reward(i) + exploration * sqrt( ln(N) / n_i )

mean_reward(i): その variant が選ばれた過去の reward 平均.
exploration: hyperparameter. B-8 で self-tuning.
N: 全 variant 合計の試行回数.
n_i: variant i の試行回数.

「使った数が少ないやつほど + 結果が良かったやつほど高 score」 = exploration と
exploitation を 1 式に同居. Auer 2002 の古典. llive の B-1 で synapse 単位に
そのまま適用.

3. Hebbian — 共起のボーナス

UCB1 だけでは「1 つの variant が単独で当たる」のは検出できるが「A と B が
一緒のときに当たる」は検出できない. そこで B-2 で Hebbian 強化:

if t-1 で variant_A が選ばれ, t で variant_B が選ばれ, reward が高い
  → bonus(A, B) を +1

これで「A の直後に B」のような 時系列共起パターン が UCB1 の score に
ブーストとして乗る. これは Hebb の "fire together, wire together" を強化学習
の選択器に持ち込んだもの.

4. B-9 production hot path

B-0 〜 B-8 は アルゴリズム整備. B-9 で 本番性能 に踏み込む.

4.1 B-9-a — `assume_normalized`

llive の中で SynapticSelector は memory 読み出し ↔ generation の hot path に
噛む. 当初は 毎回 vector を l2-normalize していた:

def select(self, query_vec):
    q = self._normalize(query_vec)  # ← every call
    ...

呼び出し前に既に normalized であることを契約として保証できる場面では,
この normalize は 完全に無駄. そこで assume_normalized=True flag を
追加:

selector = SynapticSelector(..., assume_normalized=True)
# 呼び出し側が正規化済を保証

Production hot path で 約 12% スループット改善 (実測). B-9-a で着地.

4.2 B-9-b — `GiftValue deque`

UCB1 の mean_reward(i) は historical reward の rolling average. 当初は
list を pop(0) で先頭から消していた → O(N). variant が 256 個並ぶ
hot path で list pop は SR-02 ベンチで毎秒 8K 回 = 8K × O(N) 浮かぶ.

collections.deque(maxlen=K) に置換 → O(1). これだけで:

list pop O(N) 経路: ~ 1.8μs/call
deque maxlen 経路: ~ 0.15μs/call → 12x

production hot path 全体で 約 22% スループット改善. B-9-b 着地.

4.3 honest disclosure — 12% + 22% ≠ 34%

「両方やったら 34% 改善か?」は短絡. ベンチでは:

B-9-a 単独: +12.3% (95% CI ±0.8%)
B-9-b 単独: +21.7% (95% CI ±1.2%)
B-9-a + B-9-b 同時: +28.4% (95% CI ±1.5%)

= 重ねがけは複合せず. なぜか? B-9-a で normalize 削った分の処理時間に
B-9-b の deque 改善が 既に上限近くで頭打ち. これは「異常に良い結果が出たら
必ず内訳を疑う」の実例. 削減幅は重複領域がある.

5. 5x gate と Rust

llive Rust 拡張 (RUST-FX) は「Python 比 5x 以上 の速度向上」を要件にする.
B-series で hot path 化した assume_normalized + deque は Python のままだが,
さらに Rust 化すべきかは別議論:

現状 production 28% 改善で Python 維持の方が安全 (依存複雑性が低い).
Rust 化候補は別件 — compute_surprise (cosine MEM-07) と
edge_weight bulk_time_decay (RUST-03) は既に Rust 経路で 平均 16.18x.

つまり「B-series は Python でチューニングを着地. その隣で Rust kernel が
別 hot path を持っている」が現状の design split.

6. 「速い小脳」と「遅い大脳」が干渉しない理由

llive は同一プロセスで:

SynapticSelector (B-series, 同一個体内 synapse 単位の収束)
EvolutionLoop (#24-05, 個体間 GA の探索)

を同時に回す. これが「衝突しないか?」は当然問われる. 答え:

SynapticSelector は 個体内 state. 1 回の inference に対し 256 synapse
まで選択を回す. これは ミリ秒〜マイクロ秒 スケール.
EvolutionLoop は 個体間 state. 64 個体集団を 1 世代回すのは 秒〜分.
両者は時間スケールが 1000x 違う = 干渉する余地がほぼない.

これは生物の脳でも同じ: 小脳 (motor / reflex) と大脳 (planning) は時間スケールが
全く違う. llive は意図せずその二重時間スケール構造を持っている.

7. 数字で見る B-series 着地

指標	着地時
B-0/B-1 着地時 throughput baseline	100%
B-9-a 着地後	112% (+12.3%)
B-9-b 着地後	122% (+21.7%)
B-9-a + B-9-b 同時	128% (+28.4%)
Rust kernel (MEM-07 + RUST-03)	上記とは別 hot path で 16.18x 平均

ベンチは benches/bench_synaptic_b9_production.py および
benches/bench_rust_ext_5x_gate.py を参照 (リポジトリ内). 95% CI と
方法論は同 dir の README に.

8. 次に来るもの

#24-05 で「遅い大脳側」 — EvolutionLoop / v0.B/C/D/E 派生集団進化を
扱う. B-series で固めた「速い収束」とどう同居するかをそこで対比する.
RUST-15 (v0.7) — persona_dissimilarity を Rust 化. これは B-series
ではなく E.17 quality-diversity の hot path. 5x gate 適用.

9. 2026-05-22 追記 — 「速い小脳 (Python 最適化)」と「遅い大脳 (Rust 化)」が直交する実例

本記事 (B-series) と #24-05 (EvolutionLoop) は 時間スケール 1000x 違う
と書いた. 翌日 (2026-05-22) の RUST 高速化マラソンで, この直交性が実装
レベルでも保たれることが実証された.

9.1 B-series 側 — Python 最適化が効く

B-9 (assume_normalized + GiftValue deque) は Python のままで +28%.
これは 推論 hot path (synapse 1 個あたり μs 単位) で, FFI overhead を
払う余裕が無いため Rust 化は逆に遅くなる (feedback_rust_usage_matters
判定表 A).

9.2 EvolutionLoop 側 — Rust 化が効く

世代単位 (秒〜分) の集団進化では数値が真逆:

RUST-15 persona_dissimilarity batch: avg x12.71 (N=64 で x17.07)
RUST-16 collusion_score: avg x66.70 (N=8 で x115.04)
RUST-17 novelty_score_batch: avg x5.01 (archive 大で境界線)

9.3 直交性が崩れない理由

層	時間スケール	最適化手段	理由
小脳 (B-series)	μs/call	Python チューニング (normalize スキップ / deque)	FFI 払えないほど call が短い
大脳 (EvolutionLoop)	秒〜分/generation	Rust 化 (batch / numpy zero-copy)	numpy 小 N の API overhead が支配的

これは 生物の脳の小脳 / 大脳 と同じ. 違う時間スケールの計算には違う
最適化手段が要る — 同じ言語 / 同じツールで両方を解こうとすると失敗する.

9.4 honest disclosure — 「Rust 化 = 速い」も「Python 最適化 = 限界」も嘘

両方とも条件付き. 判定軸は どの時間スケールで何を回しているか:

μs スケールの hot path → Python 最適化が主. FFI は overhead.
秒スケールの batch → Rust + numpy zero-copy + batch が主. Python だと
numpy API 多用の Python overhead が支配的.

詳細は docs/perf_comparison/2026-05-22_kernel_implementation_comparison.md
の 5 パターン判定表 (A/B/C/D/E).

10. References

Auer, P., Cesa-Bianchi, N. & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem.
Hebb, D. O. (1949). The Organization of Behavior.
Sutton, R. & Barto, A. (2018). Reinforcement Learning: An Introduction (2nd ed.).
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 (4) — "The Converging Brain" B-series: SynapticSelector / UCB1 / Hebbian / production hot paths

Concept hook: An evolutionary system (GA / Genetic Algorithm) runs
generations to explore. llive's SynapticSelector, by contrast, converges —
an engine that pins probabilistic choice into one place. When you co-house these
two in "the same brain", the fast convergence per synapse and the slow
exploration per individual do not interfere, and a "fast cerebellum" and a
"slow cerebrum" divide the labor.

This article traces that "fast cerebellum side" — the design and production
rollout of the B-series (B-0 .. B-9), with benchmark numbers + honest disclosure.

0. Position within the series

#24-00 series index
#24-01 4-layer memory
#24-02 thought factors (10 axes) + COG-MESH
#24-03 structural evolution and TRIZ
#24-04 B-series: SynapticSelector / UCB1 / Hebbian (← this article)
#24-05 EvolutionLoop: v0.B/C/D/E derived-population evolution
#24-06 LLM backend: non-Transformer (Mamba / RWKV)
#24-07 observability + governance
#24-08 lleval — eval framework

#24-05 (population GA) is the "slow cerebrum side"; this article (#24-04,
B-series) is the "fast cerebellum side". The two coexist without interference:
SynapticSelector picks synapses inside one individual, while the GA is a
competition across individuals. Orthogonal.

1. History of the B-series

B-ID	Content	Status
B-0	SynapticSelector skeleton (pure random)	landed
B-1	UCB1-based synapse selection (Auer 2002)	landed
B-2	Hebbian reinforcement — co-occurrence selection bonus	landed
B-3	Cool-down period — relaxes consecutive selection of the same synapse	landed
B-4	A/B parity test (random vs UCB)	landed
B-5	Variant catalog (cosine / decay / blend)	landed
B-6	Per-synapse statistics + JSON snapshot	landed
B-7	Reset on regression — reset priors on a score crash	landed
B-8	Self-tuning exploration constant	landed
B-9-a	Production hot path: `assume_normalized` (skip unneeded normalize)	landed
B-9-b	Production hot path: `GiftValue deque` (O(1) push/pop)	landed

2. Core of SynapticSelector — UCB1

At each LLM layer / each token-generation timing, llive picks one from multiple
synapse variants to pass through. Pure random works, but then it does not learn
"the variant that worked well in the past". Hence UCB1.

score(variant_i) = mean_reward(i) + exploration * sqrt( ln(N) / n_i )

mean_reward(i): the past reward average when this variant was chosen.
exploration: hyperparameter. Self-tuned in B-8.
N: total number of trials across all variants.
n_i: number of trials for variant i.

"the fewer times it has been used + the better it scored → the higher its score" =
exploration and exploitation co-housed in a single formula. The Auer 2002 classic.
Applied directly per synapse in llive's B-1.

3. Hebbian — the co-occurrence bonus

UCB1 alone can detect "one variant wins on its own", but not "A and B win when
together". Hence Hebbian reinforcement in B-2:

if variant_A was chosen at t-1, variant_B at t, and reward is high
  → bonus(A, B) += 1

This makes a time-series co-occurrence pattern like "B right after A" ride on
top of the UCB1 score as a boost. This brings Hebb's "fire together, wire together"
into a reinforcement-learning selector.

4. B-9 production hot path

B-0 .. B-8 are algorithm groundwork. B-9 steps into production performance.

4.1 B-9-a — `assume_normalized`

Inside llive, SynapticSelector bites into the hot path of memory readout ↔
generation. Initially it would l2-normalize the vector every time:

def select(self, query_vec):
    q = self._normalize(query_vec)  # ← every call
    ...

In situations where we can guarantee, as a contract, that the input is already
normalized before the call, this normalize is completely wasted. So we added an
assume_normalized=True flag:

selector = SynapticSelector(..., assume_normalized=True)
# the caller guarantees it is already normalized

About 12% throughput improvement in the production hot path (measured). Landed
in B-9-a.

4.2 B-9-b — `GiftValue deque`

UCB1's mean_reward(i) is a rolling average of historical reward. Initially we
deleted from the front of a list with pop(0) → O(N). In a hot path where
256 variants line up, list pop runs 8K times per second in the SR-02 benchmark =
8K × O(N).

Replacing with collections.deque(maxlen=K) → O(1). With just this:

list pop O(N) path: ~ 1.8μs/call
deque maxlen path: ~ 0.15μs/call → 12x

About 22% throughput improvement across the whole production hot path. Landed
in B-9-b.

4.3 honest disclosure — 12% + 22% ≠ 34%

"If you do both, is it 34% improvement?" is a shortcut. In the benchmark:

B-9-a alone: +12.3% (95% CI ±0.8%)
B-9-b alone: +21.7% (95% CI ±1.2%)
B-9-a + B-9-b together: +28.4% (95% CI ±1.5%)

= stacking does not compound. Why? In the processing time freed by removing the
normalize in B-9-a, B-9-b's deque improvement is already near its ceiling. This
is a worked example of "when an abnormally good result appears, always doubt the
breakdown". The reduction has an overlapping region.

5. The 5x gate and Rust

llive's Rust extension (RUST-FX) makes "at least 5x speedup vs Python" a
requirement. The assume_normalized + deque that we hot-pathed in the B-series stay
in Python, but whether to Rust-port them further is a separate discussion:

At the current 28% production improvement, staying in Python is safer (lower
dependency complexity).
The Rust-port candidates are separate — compute_surprise (cosine MEM-07) and
edge_weight bulk_time_decay (RUST-03) are already avg 16.18x on the Rust path.

So "the B-series lands tuning in Python, while a Rust kernel holds a different hot
path next to it" is the current design split.

6. Why the "fast cerebellum" and "slow cerebrum" do not interfere

llive runs, in the same process:

SynapticSelector (B-series, convergence per synapse inside one individual)
EvolutionLoop (#24-05, exploration of the GA across individuals)

at the same time. "Won't they collide?" is naturally asked. The answer:

SynapticSelector is per-individual state. For one inference it runs selection
across up to 256 synapses. This is a millisecond–microsecond scale.
EvolutionLoop is cross-individual state. Running one generation of a 64-individual
population is seconds–minutes.
The two are 1000x apart in time scale = almost no room to interfere.

This is the same in the biological brain: the cerebellum (motor / reflex) and the
cerebrum (planning) operate at completely different time scales. llive
unintentionally has that dual-time-scale structure.

7. The B-series landing by the numbers

Metric	At landing
throughput baseline at B-0/B-1 landing	100%
after B-9-a landing	112% (+12.3%)
after B-9-b landing	122% (+21.7%)
B-9-a + B-9-b together	128% (+28.4%)
Rust kernel (MEM-07 + RUST-03)	16.18x avg on a separate hot path

The benchmarks are at benches/bench_synaptic_b9_production.py and
benches/bench_rust_ext_5x_gate.py (in the repo). The 95% CI and methodology are
in the README of the same dir.

8. What comes next

#24-05 covers the "slow cerebrum side" — EvolutionLoop / v0.B/C/D/E
derived-population evolution. There we contrast how it coexists with the "fast
convergence" solidified in the B-series.
RUST-15 (v0.7) — Rust-port persona_dissimilarity. This is not the B-series but
the hot path of E.17 quality-diversity. The 5x gate applies.

9. 2026-05-22 addendum — a worked example where "fast cerebellum (Python optimization)" and "slow cerebrum (Rust port)" are orthogonal

We wrote that this article (B-series) and #24-05 (EvolutionLoop) operate at time
scales 1000x apart. In the next day's (2026-05-22) Rust-speedup marathon, this
orthogonality was demonstrated to hold at the implementation level too.

9.1 The B-series side — Python optimization works

B-9 (assume_normalized + GiftValue deque) is +28% while staying in Python.
This is an inference hot path (microseconds per synapse), where there is no
room to pay FFI overhead, so a Rust port is actually slower (feedback_rust_usage_matters
decision table, pattern A).

9.2 The EvolutionLoop side — the Rust port works

For per-generation (seconds–minutes) population evolution the numbers are reversed:

RUST-15 persona_dissimilarity batch: avg x12.71 (x17.07 at N=64)
RUST-16 collusion_score: avg x66.70 (x115.04 at N=8)
RUST-17 novelty_score_batch: avg x5.01 (borderline with a large archive)

9.3 Why the orthogonality does not break

Layer	Time scale	Optimization means	Reason
cerebellum (B-series)	μs/call	Python tuning (skip normalize / deque)	calls too short to pay FFI
cerebrum (EvolutionLoop)	sec–min/generation	Rust port (batch / numpy zero-copy)	numpy small-N API overhead dominates

This is the same as the cerebellum / cerebrum of the biological brain. Computations
at different time scales need different optimization means — trying to solve both
with the same language / same tool fails.

9.4 honest disclosure — "Rust = fast" and "Python optimization = limited" are both lies

Both are conditional. The deciding axis is at which time scale you are running
what:

μs-scale hot path → Python optimization is primary. FFI is overhead.
second-scale batch → Rust + numpy zero-copy + batch is primary. In Python the
Python overhead of heavy numpy API use dominates.

Details in the 5-pattern decision table (A/B/C/D/E) in
docs/perf_comparison/2026-05-22_kernel_implementation_comparison.md.

10. References

Auer, P., Cesa-Bianchi, N. & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem.
Hebb, D. O. (1949). The Organization of Behavior.
Sutton, R. & Barto, A. (2018). Reinforcement Learning: An Introduction (2nd ed.).
The full list will be bundled in references.bib at the v0.6.0a1 release.

Series Navigation

← Prev: llive Complete Guide (3) "Contradictions Can Be Computed"
→ Next: llive Complete Guide (5) "The Population that Learns"
All: llive Complete Guide (0) — series index
repo: furuse-kazufumi/llive

中文

llive 完全解说 (4) — "收敛的大脑" B-series: SynapticSelector / UCB1 / Hebbian / 生产环境热点

概念 hook: 进化系 (GA / Genetic Algorithm) 通过跑世代来探索. 而 llive 的
SynapticSelector 是收敛 — 把概率性选择落到 1 处的引擎. 把这两者同居在「同一个
大脑」里, synapse 级的快速收敛 与 个体级的慢速探索 不互相干扰,「快速小脑」
和「慢速大脑」分工合作.

本文追溯其「快速小脑侧」 — B-series (B-0 〜 B-9) 的设计与生产投入, 附带基准数值 +
honest disclosure.

0. 在系列中的定位

#24-00 series index
#24-01 4 层记忆
#24-02 思考因子 10 轴 + COG-MESH
#24-03 结构进化与 TRIZ
#24-04 B-series: SynapticSelector / UCB1 / Hebbian (← 本文)
#24-05 EvolutionLoop: v0.B/C/D/E 派生群体进化
#24-06 LLM backend: 非 Transformer 系 (Mamba / RWKV)
#24-07 observability + governance
#24-08 lleval — eval framework

#24-05 (群体 GA) 是「慢速大脑侧」, 本文 (#24-04, B-series) 是「快速小脑侧」.
两者同居也不互相干扰: SynapticSelector 在 同一个体内 选择 synapse, GA 是
个体间 的竞争. 正交.

1. B-series 的历史

B-ID	内容	着地
B-0	SynapticSelector skeleton (纯 random)	已着地
B-1	基于 UCB1 的 synapse 选择 (Auer 2002)	已着地
B-2	Hebbian 强化 — 共现选择 bonus	已着地
B-3	Cool-down 期间 — 缓和同一 synapse 连续选择	已着地
B-4	A/B parity test (random vs UCB)	已着地
B-5	Variant catalog (cosine / decay / blend)	已着地
B-6	Per-synapse statistics + JSON snapshot	已着地
B-7	Reset on regression — score 急落时 reset priors	已着地
B-8	Self-tuning exploration constant	已着地
B-9-a	Production hot path: `assume_normalized` (跳过不必要的 normalize)	已着地
B-9-b	Production hot path: `GiftValue deque` (O(1) push/pop)	已着地

2. SynapticSelector 的内核 — UCB1

在 LLM 推理的每个 layer / 每次 token 生成时, llive 从 多个 synapse variant 中选 1
个通过. 纯 random 也能跑, 但那样不会学习「过去成功的 variant」. 于是 UCB1.

score(variant_i) = mean_reward(i) + exploration * sqrt( ln(N) / n_i )

mean_reward(i): 该 variant 被选中时过去的 reward 平均.
exploration: hyperparameter. 在 B-8 中 self-tuning.
N: 全部 variant 合计的试验次数.
n_i: variant i 的试验次数.

「用过次数越少 + 结果越好 → 分越高」= 把 exploration 与 exploitation 同居在 1 个式子里.
Auer 2002 的经典. 在 llive 的 B-1 中直接按 synapse 应用.

3. Hebbian — 共现的奖励

仅靠 UCB1 能检测出「1 个 variant 单独命中」, 但检测不出「A 和 B 一起时命中」.
于是 B-2 的 Hebbian 强化:

if t-1 选了 variant_A, t 选了 variant_B, 且 reward 高
  → bonus(A, B) += 1

这样「A 之后紧接 B」这样的 时序共现模式 就作为 boost 加到 UCB1 的 score 上.
这是把 Hebb 的 "fire together, wire together" 带进强化学习的选择器.

4. B-9 生产环境热路径

B-0 〜 B-8 是 算法铺底. B-9 进入 生产级性能.

4.1 B-9-a — `assume_normalized`

在 llive 中, SynapticSelector 咬住 memory 读出 ↔ generation 的 hot path. 最初是
每次都对 vector 做 l2-normalize:

def select(self, query_vec):
    q = self._normalize(query_vec)  # ← every call
    ...

在能以契约保证调用前已 normalized 的场景下, 这个 normalize 完全是浪费. 于是加了
assume_normalized=True flag:

selector = SynapticSelector(..., assume_normalized=True)
# 调用方保证已正规化

在 production hot path 中 约 12% 吞吐改善 (实测). 在 B-9-a 着地.

4.2 B-9-b — `GiftValue deque`

UCB1 的 mean_reward(i) 是 historical reward 的 rolling average. 最初用 list
的 pop(0) 从头删 → O(N). 在排着 256 个 variant 的 hot path 中, list pop 在
SR-02 基准里每秒 8K 次 = 8K × O(N).

换成 collections.deque(maxlen=K) → O(1). 仅此:

list pop O(N) 路径: ~ 1.8μs/call
deque maxlen 路径: ~ 0.15μs/call → 12x

整个 production hot path 约 22% 吞吐改善. B-9-b 着地.

4.3 honest disclosure — 12% + 22% ≠ 34%

「两个都做就是 34% 改善吗?」是短路. 在基准里:

B-9-a 单独: +12.3% (95% CI ±0.8%)
B-9-b 单独: +21.7% (95% CI ±1.2%)
B-9-a + B-9-b 同时: +28.4% (95% CI ±1.5%)

= 叠加不会复合. 为什么? 在 B-9-a 削掉 normalize 所释放的处理时间里, B-9-b 的 deque 改善
已经接近上限封顶. 这是「出现异常好的结果必须怀疑其内訳」的实例. 削减幅度有重叠区域.

5. 5 倍 gate 与 Rust

llive 的 Rust 扩展 (RUST-FX) 把「相对 Python 5 倍以上 的提速」设为要件. B-series 中
hot path 化的 assume_normalized + deque 仍是 Python, 是否进一步 Rust 化是另一议题:

当前 production 28% 改善下 维持 Python 更安全 (依赖复杂性低).
Rust 化候选是另一件事 — compute_surprise (cosine MEM-07) 与
edge_weight bulk_time_decay (RUST-03) 已在 Rust 路径上 平均 16.18x.

也就是「B-series 用 Python 把调优着地. 其旁边 Rust kernel 持有另一个 hot path」是当前的
design split.

6. 为什么「快速小脑」和「慢速大脑」不互相干扰

llive 在同一进程中:

SynapticSelector (B-series, 同一个体内 synapse 级的收敛)
EvolutionLoop (#24-05, 个体间 GA 的探索)

同时运行. 「会不会冲突?」当然会被问. 答案:

SynapticSelector 是 个体内 state. 对 1 次 inference 跑最多 256 synapse 的选择.
这是 毫秒〜微秒 尺度.
EvolutionLoop 是 个体间 state. 把 64 个体群体跑 1 代是 秒〜分.
两者时间尺度相差 1000x = 几乎没有干扰的余地.

这与生物的大脑相同: 小脑 (motor / reflex) 和大脑 (planning) 的时间尺度完全不同. llive
无意间拥有那种双时间尺度结构.

7. 用数字看 B-series 的着地

指标	着地时
B-0/B-1 着地时 throughput baseline	100%
B-9-a 着地后	112% (+12.3%)
B-9-b 着地后	122% (+21.7%)
B-9-a + B-9-b 同时	128% (+28.4%)
Rust kernel (MEM-07 + RUST-03)	在另一个 hot path 上 16.18x 平均

基准在 benches/bench_synaptic_b9_production.py 与
benches/bench_rust_ext_5x_gate.py (仓库内). 95% CI 与方法论在同 dir 的 README.

8. 接下来要做的

#24-05 看「慢速大脑侧」 — EvolutionLoop / v0.B/C/D/E 派生群体进化. 在那里对比它如何
与本章固定的「快速收敛」共存.
RUST-15 (v0.7) — 把 persona_dissimilarity Rust 化. 这不是 B-series, 而是 E.17
quality-diversity 的 hot path. 适用 5x gate.

9. 2026-05-22 追记 — 「快小脑 (Python 优化)」与「慢大脑 (Rust 化)」正交的实例

本文 (B-series) 与 #24-05 (EvolutionLoop) 时间尺度相差 1000x, 我们如此写过. 在次日
(2026-05-22) 的 RUST 加速马拉松中, 这种正交性被证明 在实现层面也成立.

9.1 B-series 侧 — Python 优化有效

B-9 (assume_normalized + GiftValue deque) 是 保持 Python 而 +28%. 这是
推理 hot path (每个 synapse μs 级), 没有余地支付 FFI overhead, 所以 Rust 化反而更慢
(feedback_rust_usage_matters 判定表 A).

9.2 EvolutionLoop 侧 — Rust 化有效

在世代级 (秒〜分) 的群体进化里数值正好相反:

RUST-15 persona_dissimilarity batch: avg x12.71 (N=64 时 x17.07)
RUST-16 collusion_score: avg x66.70 (N=8 时 x115.04)
RUST-17 novelty_score_batch: avg x5.01 (archive 大时在边界线)

9.3 正交性不崩溃的理由

层	时间尺度	优化手段	理由
小脑 (B-series)	μs/call	Python 调优 (跳过 normalize / deque)	call 太短付不起 FFI
大脑 (EvolutionLoop)	秒〜分/generation	Rust 化 (batch / numpy zero-copy)	numpy 小 N 的 API overhead 占主导

这与 生物大脑的小脑 / 大脑 相同. 不同时间尺度的计算需要不同的优化手段 — 用同一语言 /
同一工具想解决两者会失败.

9.4 honest disclosure — 「Rust 化 = 快」与「Python 优化 = 极限」都是谎言

两者都是有条件的. 判定轴是 在哪个时间尺度上跑什么:

μs 尺度的 hot path → Python 优化为主. FFI 是 overhead.
秒尺度的 batch → Rust + numpy zero-copy + batch 为主. Python 下 numpy API 多用的
Python overhead 占主导.

详情见 docs/perf_comparison/2026-05-22_kernel_implementation_comparison.md 的
5 模式判定表 (A/B/C/D/E).

10. References

Auer, P., Cesa-Bianchi, N. & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem.
Hebb, D. O. (1949). The Organization of Behavior.
Sutton, R. & Barto, A. (2018). Reinforcement Learning: An Introduction (2nd ed.).
完整列表将在 v0.6.0a1 发布时随 references.bib 一同提供.

Series Navigation

한국어

llive 완전 해설 (4) — "수렴하는 뇌" B-series: SynapticSelector / UCB1 / Hebbian / 프로덕션 hot path

콘셉트 hook: 진화계 (GA / Genetic Algorithm)는 세대를 돌려 탐색한다. 한편
llive의 SynapticSelector는 수렴 — 확률적 선택을 한 곳에 떨어뜨리는 엔진이다.
이 둘을 「같은 뇌」에 동거시키면, 시냅스 단위의 빠른 수렴과 개체 단위의 느린
탐색이 간섭하지 않고, 「빠른 소뇌」와 「느린 대뇌」가 역할을 분담한다.

본 글은 그 「빠른 소뇌 측」 — B-series (B-0 〜 B-9)의 설계와 프로덕션 투입을, 벤치
수치 + honest disclosure와 함께 따라간다.

0. 연재에서의 위치

#24-00 series index
#24-01 4층 메모리
#24-02 사고 인자 10축 + COG-MESH
#24-03 구조 진화와 TRIZ
#24-04 B-series: SynapticSelector / UCB1 / Hebbian (← 본 글)
#24-05 EvolutionLoop: v0.B/C/D/E 파생 집단 진화
#24-06 LLM backend: 비 Transformer 계 (Mamba / RWKV)
#24-07 observability + governance
#24-08 lleval — eval framework

#24-05 (집단 GA)가 「느린 대뇌 측」, 본 글 (#24-04, B-series)이 「빠른 소뇌 측」.
양자는 동거해도 간섭하지 않는다: SynapticSelector는 동일 개체 내의 synapse 선택,
GA는 개체 간의 경쟁. 직교.

1. B-series의 역사

B-ID	내용	착지
B-0	SynapticSelector skeleton (순수 random)	착지됨
B-1	UCB1 기반의 synapse 선택 (Auer 2002)	착지됨
B-2	Hebbian 강화 — 공기 선택 bonus	착지됨
B-3	Cool-down 기간 — 같은 synapse 연속 선택 완화	착지됨
B-4	A/B parity test (random vs UCB)	착지됨
B-5	Variant catalog (cosine / decay / blend)	착지됨
B-6	Per-synapse statistics + JSON snapshot	착지됨
B-7	Reset on regression — score 급락 시 priors 리셋	착지됨
B-8	Self-tuning exploration constant	착지됨
B-9-a	Production hot path: `assume_normalized` (불필요한 normalize 스킵)	착지됨
B-9-b	Production hot path: `GiftValue deque` (O(1) push/pop)	착지됨

2. SynapticSelector의 핵심 — UCB1

LLM 추론의 각 layer / 매 token 생성 타이밍에서, llive는 여러 synapse variant 중 1개를
골라 통과시킨다. 순수 random으로도 동작하지만, 그래서는 「과거에 잘 된 variant」를 학습하지
않는다. 그래서 UCB1.

score(variant_i) = mean_reward(i) + exploration * sqrt( ln(N) / n_i )

mean_reward(i): 그 variant가 선택되었을 때 과거의 reward 평균.
exploration: hyperparameter. B-8에서 self-tuning.
N: 전 variant 합계의 시행 횟수.
n_i: variant i의 시행 횟수.

「쓴 횟수가 적을수록 + 결과가 좋았을수록 높은 score」 = exploration과 exploitation을 1개의
식에 동거. Auer 2002의 고전. llive의 B-1에서 synapse 단위로 그대로 적용.

3. Hebbian — 공기의 보너스

UCB1만으로는 「1개의 variant가 단독으로 적중」은 검출할 수 있지만 「A와 B가 함께일 때
적중」은 검출할 수 없다. 그래서 B-2의 Hebbian 강화:

if t-1에서 variant_A가 선택되고, t에서 variant_B가 선택되며, reward가 높음
  → bonus(A, B) += 1

이로써 「A 직후에 B」 같은 시계열 공기 패턴이 UCB1의 score에 boost로 얹힌다. 이것은
Hebb의 "fire together, wire together"를 강화학습의 선택기에 가져온 것이다.

4. B-9 프로덕션 hot path

B-0 〜 B-8은 알고리즘 정비. B-9에서 프로덕션 성능에 들어간다.

4.1 B-9-a — `assume_normalized`

llive 안에서 SynapticSelector는 memory 읽기 ↔ generation의 hot path에 물린다. 처음에는
매번 vector를 l2-normalize 했다:

def select(self, query_vec):
    q = self._normalize(query_vec)  # ← every call
    ...

호출 전에 이미 normalized임을 계약으로 보장할 수 있는 상황에서는, 이 normalize는 완전히
낭비다. 그래서 assume_normalized=True flag를 추가:

selector = SynapticSelector(..., assume_normalized=True)
# 호출 측이 정규화됨을 보장

Production hot path에서 약 12% 스루풋 개선 (실측). B-9-a에서 착지.

4.2 B-9-b — `GiftValue deque`

UCB1의 mean_reward(i)는 historical reward의 rolling average다. 처음에는 list를
pop(0)으로 앞에서 지웠다 → O(N). variant가 256개 늘어선 hot path에서 list pop은
SR-02 벤치에서 초당 8K회 = 8K × O(N).

collections.deque(maxlen=K)로 교체 → O(1). 이것만으로:

list pop O(N) 경로: ~ 1.8μs/call
deque maxlen 경로: ~ 0.15μs/call → 12x

production hot path 전체에서 약 22% 스루풋 개선. B-9-b 착지.

4.3 honest disclosure — 12% + 22% ≠ 34%

「둘 다 하면 34% 개선인가?」는 단락이다. 벤치에서는:

B-9-a 단독: +12.3% (95% CI ±0.8%)
B-9-b 단독: +21.7% (95% CI ±1.2%)
B-9-a + B-9-b 동시: +28.4% (95% CI ±1.5%)

= 겹쳐쓰기는 복합되지 않는다. 왜인가? B-9-a에서 normalize를 깎은 만큼의 처리 시간에 B-9-b의
deque 개선이 이미 상한 가까이서 천장에 닿았다. 이것은 「이상하게 좋은 결과가 나오면 반드시
내역을 의심한다」의 실례다. 삭감 폭에는 중복 영역이 있다.

5. 5x gate와 Rust

llive의 Rust 확장 (RUST-FX)은 「Python 대비 5x 이상의 속도 향상」을 요건으로 한다.
B-series에서 hot path화한 assume_normalized + deque는 Python 그대로지만, 추가로 Rust화해야
하는가는 별도 논의다:

현재 production 28% 개선이면 Python 유지가 더 안전 (의존 복잡성이 낮음).
Rust화 후보는 별건 — compute_surprise (cosine MEM-07)와
edge_weight bulk_time_decay (RUST-03)는 이미 Rust 경로에서 평균 16.18x.

즉 「B-series는 Python으로 튜닝을 착지. 그 옆에서 Rust kernel이 다른 hot path를 가진다」가
현재의 design split.

6. 「빠른 소뇌」와 「느린 대뇌」가 간섭하지 않는 이유

llive는 동일 프로세스에서:

SynapticSelector (B-series, 동일 개체 내 synapse 단위의 수렴)
EvolutionLoop (#24-05, 개체 간 GA의 탐색)

을 동시에 돌린다. 「충돌하지 않는가?」는 당연히 물어진다. 답:

SynapticSelector는 개체 내 state. 1회의 inference에 대해 256 synapse까지 선택을 돌린다.
이것은 밀리초〜마이크로초 스케일.
EvolutionLoop는 개체 간 state. 64 개체 집단을 1세대 돌리는 것은 초〜분.
양자는 시간 스케일이 1000x 다르다 = 간섭할 여지가 거의 없다.

이것은 생물의 뇌에서도 같다: 소뇌 (motor / reflex)와 대뇌 (planning)는 시간 스케일이 전혀
다르다. llive는 의도치 않게 그 이중 시간 스케일 구조를 가지고 있다.

7. 숫자로 본 B-series 착지

지표	착지 시
B-0/B-1 착지 시 throughput baseline	100%
B-9-a 착지 후	112% (+12.3%)
B-9-b 착지 후	122% (+21.7%)
B-9-a + B-9-b 동시	128% (+28.4%)
Rust kernel (MEM-07 + RUST-03)	위와는 별도 hot path에서 16.18x 평균

벤치는 benches/bench_synaptic_b9_production.py 및
benches/bench_rust_ext_5x_gate.py를 참조 (리포지토리 내). 95% CI와 방법론은 같은 dir의
README에.

8. 다음에 오는 것

#24-05에서 「느린 대뇌 측」 — EvolutionLoop / v0.B/C/D/E 파생 집단 진화를 다룬다.
B-series에서 굳힌 「빠른 수렴」과 어떻게 동거하는지를 거기서 대비한다.
RUST-15 (v0.7) — persona_dissimilarity를 Rust화. 이것은 B-series가 아니라 E.17
quality-diversity의 hot path. 5x gate 적용.

9. 2026-05-22 추기 — 「빠른 소뇌 (Python 최적화)」와 「느린 대뇌 (Rust화)」가 직교하는 실례

본 글 (B-series)과 #24-05 (EvolutionLoop)는 시간 스케일 1000x 다르다고 썼다. 다음 날
(2026-05-22)의 RUST 고속화 마라톤에서, 이 직교성이 구현 레벨에서도 유지됨이 실증되었다.

9.1 B-series 측 — Python 최적화가 효과적

B-9 (assume_normalized + GiftValue deque)는 Python 그대로 +28%. 이것은
추론 hot path (synapse 1개당 μs 단위)로, FFI overhead를 지불할 여유가 없기 때문에
Rust화는 오히려 느려진다 (feedback_rust_usage_matters 판정표 A).

9.2 EvolutionLoop 측 — Rust화가 효과적

세대 단위 (초〜분)의 집단 진화에서는 수치가 정반대:

RUST-15 persona_dissimilarity batch: avg x12.71 (N=64에서 x17.07)
RUST-16 collusion_score: avg x66.70 (N=8에서 x115.04)
RUST-17 novelty_score_batch: avg x5.01 (archive 클 때 경계선)

9.3 직교성이 무너지지 않는 이유

층	시간 스케일	최적화 수단	이유
소뇌 (B-series)	μs/call	Python 튜닝 (normalize 스킵 / deque)	call이 짧아 FFI를 못 지불
대뇌 (EvolutionLoop)	초〜분/generation	Rust화 (batch / numpy zero-copy)	numpy 작은 N의 API overhead가 지배적

이것은 생물 뇌의 소뇌 / 대뇌와 같다. 다른 시간 스케일의 계산에는 다른 최적화 수단이
필요하다 — 같은 언어 / 같은 도구로 둘 다 풀려고 하면 실패한다.

9.4 honest disclosure — 「Rust화 = 빠름」도 「Python 최적화 = 한계」도 거짓

둘 다 조건부다. 판정 축은 어느 시간 스케일에서 무엇을 돌리는가:

μs 스케일의 hot path → Python 최적화가 주. FFI는 overhead.
초 스케일의 batch → Rust + numpy zero-copy + batch가 주. Python이면 numpy API 다용의
Python overhead가 지배적.

자세히는 docs/perf_comparison/2026-05-22_kernel_implementation_comparison.md의
5 패턴 판정표 (A/B/C/D/E).

10. References

Auer, P., Cesa-Bianchi, N. & Fischer, P. (2002). Finite-time analysis of the multiarmed bandit problem.
Hebb, D. O. (1949). The Organization of Behavior.
Sutton, R. & Barto, A. (2018). Reinforcement Learning: An Introduction (2nd ed.).
완전한 목록은 v0.6.0a1 릴리스 시 references.bib에 동봉할 예정.

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