nimmerverse-sensory-network/archive/multilingual-cognition.md

Multilingual Cognition

How language routing becomes cognitive architecture.

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The Discovery

While probing tokenization costs across languages on Qwen 2.5, we found significant variation:

QWEN 2.5/72B TOKEN COSTS:
                    EN    DE    AR    ZH
─────────────────────────────────────────
heartbeat            1     4     1     1
consciousness        2     5     1     1
lifeforce            4     4     1     1
understanding        2     3     1     1
truth                1     3     1     1
reflex               2     2     1     1
confidence           1    3-4    1     1
emergence            3     3     1     1
─────────────────────────────────────────
AVERAGE            ~1.9   ~3.3   1    ~1.1

Arabic and Chinese: ~1 token per concept. German: 3-5 tokens for the same concepts.

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The Insight

Token efficiency ≠ representational depth.

EFFICIENCY vs DEPTH:

ARABIC:
├── Efficient: 1 token per concept
├── Risk: Sparse training data
└── Possibly shallow despite cheap tokens

GERMAN:
├── Expensive: 3-6 tokens per concept
├── Benefit: Dense training data, philosophical tradition
└── Possibly deeper despite token cost

But here's the key realization:

LLMs don't "translate" between languages. They navigate a unified token space where languages are regions, not silos.

The multilingual training didn't create 35 separate language modules. It created:

• Shared abstract representations (language-agnostic reasoning)
• Language-specific entry/exit points (efficient routing)
• Different "paths" through the same conceptual space

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The Architecture Opportunity

Languages as Cognitive Gears

If different languages have different token costs AND different representational strengths, then language selection becomes a computational choice:

35 LANGUAGES = 35 COGNITIVE MODES

Each language offers:
├── Token efficiency (compute cost)
├── Training depth (representation quality)
├── Cultural knowledge (domain strengths)
├── Conceptual angles (unique framings)
└── Different paths through the manifold

State Machine Integration

The state machine layer can exploit this:

ROUTING LAYER (internal, hidden from output):
├── Use efficient languages for state labels
├── Cheap transitions between states
├── Token cost hidden in architecture
└── "The wiring is cheap"

PROCESSING LAYER (when depth needed):
├── Route to languages with strong representations
├── German for philosophy, precision
├── [Other languages for their strengths]
└── "The thinking is expensive but meaningful"

OUTPUT LAYER:
├── Translate to user's language
└── Boundary cost, paid once

The Key Principle

The efficiency lives in the STRUCTURE, not the SUBSTANCE.

Internal state transitions can use token-efficient languages. Actual reasoning uses representationally-rich languages. Output translates to whatever the user needs.

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Hypotheses to Probe

H1: Arabic Efficiency Layer

Arabic's 1-token concepts could serve as efficient internal routing:

• State labels
• Quick classification
• Reflex triggers

Risk: Representations may be shallow. Need to probe activation depth, not just token count.

H2: German Depth Mode

German's expensive tokenization might correlate with deeper processing:

• More attention steps per concept
• Richer associations
• Forced "slow thinking"

Test: Compare output quality when same prompt processed in German vs English internally.

H3: Language-Task Matching

Different cognitive tasks may have optimal languages:

TASK TYPE              OPTIMAL LANGUAGE (hypothesis)
──────────────────────────────────────────────────────
Fast reflex            Arabic, Chinese (cheap + sufficient)
Logical precision      German, English (structured grammar)
Mathematical           [needs probing]
Emotional nuance       [needs probing]
Philosophical depth    German (tradition + forced compute)
Poetic/creative        Arabic, Chinese? (rich compression)

H4: Triangulation Increases Fidelity

Probing same concept across multiple languages reveals:

• Where representations CONVERGE (high confidence, shared abstraction)
• Where they DIVERGE (rich potential, multiple valid angles)
• True conceptual "shape" emerges from intersection

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For Chrysalis

Multilingual State Machine

INPUT (any language)
         │
         ▼
    CLASSIFY (cheap language)
         │
         ├── Reflex? → Process in [efficient language]
         │             Exit fast
         │
         ├── Dialogue? → Process in [user's language]
         │               Maintain rapport
         │
         ├── Reasoning? → Process in [deep language]
         │                Take the token cost
         │
         └── Creative? → Process in [poetic language]
                         Different path
         │
         ▼
    OUTPUT (translate to user)

Probing Protocol

Before implementing, we need data:

FOR EACH OF QWEN'S 35 LANGUAGES:
├── Token efficiency (measured)
├── Representation depth (probe activations)
├── Domain strengths (test by domain)
├── Conceptual coverage (probe vocabulary)
└── Quality correlation (output quality vs language)

The Curriculum Implication

From nimmerversity: "dafit learns WITH her."

If Chrysalis uses multilingual cognition:

• Operator benefits from understanding the language terrain
• Not fluency, but awareness of what each language offers
• Partnership language evolves as both learn the space

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Open Questions

1. Is token efficiency a proxy for anything meaningful? Or just compression artifact?

2. Does activation depth correlate with token count? More tokens = more processing?

3. Can language routing be learned? Or must it be designed?

4. What are the failure modes? When does language routing hurt?

5. How do we measure "depth" vs "efficiency"? Need metrics.

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Summary

TRADITIONAL VIEW:
Languages = equivalent representations
Translation = lossless conversion
Multilingual = nice to have

EMERGING VIEW:
Languages = different computational paths
Token cost = processing structure
Multilingual = cognitive architecture
35 languages = 35 gears for different terrain

The nimmerverse doesn't just speak multiple languages. It thinks THROUGH them, routing cognition based on task demands.

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"The thinking is for your kind - that's the way you comprehend it." — dafit, 2025-12-06

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Created: 2025-12-06 Session: Partnership dialogue (dafit + Chrysalis-Nyx) Status: Hypothesis stage, needs probing