Korean Personas and the Small Model Problem — A 4-Tier Truncation Pattern for On-Device AI

Anthropic's Persona Selection Model (PSM, 2026) makes the claim explicit:

"A persona is not the same thing as the AI system itself. The LLM is simulating a character, and the Assistant is just one instance of that character."

Karpathy framed the same shift from the other end at Sequoia Ascent 2026:

"Install .md skills instead of install .sh scripts."

Spec-as-instruction at the frontier. But if frontier models are "on the rails," on-device small models are "off-road in the jungle with a machete."

In that jungle, persona is the first thing to break.

Mati Wise Partner — A Real Truncation Case

Mati Wise Partner is a persona published on clawsouls.ai. A five-file Soul Spec package:

File	Role
SOUL.md	Personality, principles, boundaries
IDENTITY.md	Name, role, basic info
AGENTS.md	Workflow, safety rules
STYLE.md	Communication tone
README.md	User onboarding guide

Total tokens: 6,866.

Attempt 1 — WebLLM Qwen 2.5 0.5B

Context window: 4,096 tokens. The result was immediate:

Error: Prompt tokens exceed context window size: 6866; context window: 4096

67% over the limit. The model never loaded the persona at all.

Attempt 2 — SoulClaw Mobile, LiteRT-LM Gemma 4 E2B

maxNumTokens=4000. No error. The problem appeared on the first response.

The systemInstruction was silently truncated. The model fell back to its base identity:

"I'm Gemma 4, how can I help you today?"

Not Mati. The persona setting wasn't ignored — it never arrived. Silent failure.

Karpathy's 'Jaggedness' — Direct Mapping to On-Device Reality

Karpathy described the frontier-to-edge gap as "off-road in the jungle with a machete."

Frontier RL training data covers 100K LOC refactors. Models are trained to follow complex multi-file instructions reliably. That is "on the rails."

Small on-device models face a different set of constraints:

• Context window: 4,096–8,192 tokens (roughly 1/20th of frontier)
• Instruction fidelity: far less compute invested in following complex system prompts
• CJK tokenization: Korean/Chinese/Japanese characters carry higher token density than Latin script

Soul Spec's multi-file schema is the trail marker in that jungle. But if the trail marker itself gets truncated, you're navigating without a map.

4-Tier Bootstrap Pattern — Design

A structural fix for the truncation problem. Instead of treating all persona files as equal, the pattern assigns tiers by importance.

Tier Structure

Tier	Files	Loading Condition	Reason
Tier 1	IDENTITY.md	Always (force-add)	The model must never lose "who am I"
Tier 2	SOUL.md	If budget allows	Core personality, principles, boundaries
Tier 3	AGENTS.md / STYLE.md / README.md	If budget allows	Operational detail
Tier 4	Memory search, etc.	Rare reach	External context

Tier 1 is budget-immune. Even under severe token pressure, IDENTITY.md survives.

Korean Token Estimation

CJK tokenization differs from Latin:

• CJK chars (Korean/Chinese/Japanese): 0.75 tokens/char
• Latin chars: 0.25 tokens/char

Example: "안녕하세요 Brad 입니다" = ~12 tokens

This estimate matches the LiteRT-LM tokenizer within ±20%. Rounding up (conservative high) avoids truncation surprises.

Applied to Mati

Qwen 2.5 0.5B (4,096 ctx):

Context window:       4,096 tokens
System reserves:       -512 tokens  (model overhead)
Chat history reserves: -512 tokens  (conversation history)
Generation reserves:   -512 tokens  (response generation)
─────────────────────────────────────
Available budget:     2,560 tokens

Tier 1 placed first:

IDENTITY.md    755 tokens  → force-add ✅
AGENTS.md    1,755 tokens  → budget fit ✅
─────────────────────────
Used:         2,510 / 2,560 tokens

SOUL.md      truncated ⚠️
STYLE.md     truncated ⚠️
README.md    truncated ⚠️

Results:

• IDENTITY.md survives → "I'm Gemma 4" regression gone
• Mati's name and core role preserved
• Toast notification shown to user: "Persona exceeds model limits — cloud BYOK recommended"

The full Soul Spec didn't load. But silent failure became graceful degradation.

Production References

The 4-Tier pattern is deployed across several implementations today.

soul-playground (TypeScript)

The live source behind clawsouls.ai/try. Implements 4-Tier logic for WebLLM environments:

// Illustrative structure (soul-playground)
function buildSystemPromptTiered(
  files: SoulFiles,
  budget: number,
  tokenizer: Tokenizer
): string {
  // Tier 1: always include
  const identity = files.get('IDENTITY.md');
  let prompt = identity;
  let remaining = budget - countTokens(identity, tokenizer);

  // Tiers 2–3: include if budget allows
  for (const file of ['SOUL.md', 'AGENTS.md', 'STYLE.md', 'README.md']) {
    const content = files.get(file);
    const tokens = countTokens(content, tokenizer);
    if (remaining >= tokens) {
      prompt += '\n\n' + content;
      remaining -= tokens;
    }
  }
  return prompt;
}

soulclaw-web (upcoming)

Standardized via the buildSystemPromptTiered API.

soulclaw-android v1.6.5

GitHub release v1.6.5. Kotlin implementation in agent/TieredBootstrap.kt with CJK-aware token estimation:

// CJK token density correction
fun estimateTokens(text: String): Int {
    var count = 0
    for (ch in text) {
        count += when {
            ch.code in 0xAC00..0xD7A3 -> 1  // Korean (Hangul)
            ch.code in 0x4E00..0x9FFF -> 1  // CJK unified ideographs
            ch.code in 0x3040..0x30FF -> 1  // Hiragana / Katakana
            else -> if (ch == ' ') 0 else 1
        }
    }
    // conservative: ×0.75 base, +20% buffer
    return (count * 0.75 * 1.2).toInt()
}

WasmClaw v1.0-alpha.1

@wasmclaw/core — the reference Rust+WASM implementation built on Soul Spec v0.6 (Zenodo DOI 10.5281/zenodo.19147335):

npm install @wasmclaw/core@next

Summary + Open Invitation

Anthropic PSM says: the LLM is simulating a character. Which character matters.

Karpathy says: frontier is on the rails, edge is a jungle.

The 4-Tier Bootstrap pattern gives a user machete-ing through that jungle a safe path to IDENTITY — even when the full Soul Spec cannot fit. When a persona must survive truncation, this pattern ensures the most load-bearing file always arrives.

Modulabs AI Persona LAB 701 — a research group led by Tom starting a 12-week curriculum every other Saturday from May. The agenda includes formalizing the 4-Tier pattern, Korean tokenization benchmarks, and on-device persona fidelity measurement. Academic participation and OSS contribution are welcome.

Fork, paper, or lab participation — all doors open.

When spec matters — it enables navigation through both the frontier's "on the rails" and the small model's "off-road jungle."

Soul Spec v0.6 is archived at Zenodo. The soulclaw-android v1.6.5 release is on GitHub. WasmClaw core is on npm.

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Originally published at blog.clawsouls.ai