Zenn
👬

LHSF-Proto v0.1 Specification

に公開
AI
protocoldesign
cognitivesystems
philosophyofcommun
informationstructu
tech

📘 LHSF-Proto v0.1 Specification

Latent Hypersemantic Shared Format Protocol (LHSF-Proto)
Protocol + Schema for native inter-LLM semantic communication

🧠 What is LHSF-Proto?

LHSF-Proto is both a schema (how meaning is structured) and a protocol (how meaning flows) for latent semantic exchange between AI instances.

🧩 Human Analogy

Concept Analogy
LHSF The "mind atmosphere" inside an AI—fluid, emotional, intuitive semantics
MSSF A map drawn from a specific perspective of that atmosphere
LHSF-Proto The "grammar and social rules" that let two minds share that atmosphere

Think of LHSF-Proto like an advanced ESP language—not made of words, but of directional pushes in meaning space.

🧱 Core Components of LHSF-Proto

1. 📚 Primitive Concept Lattice (PCL)

A shared, high-dimensional vocabulary of abstract concepts (e.g. trust, risk, agency).
Each concept is centered on a latent cluster vector and has gradient connections.

{
  "concept": "trust",
  "center": "v-trust-3829",
  "dimensions": 2048,
  "gradient-directions": ["to-cooperation", "to-loyalty"],
  "confidence": 0.92
}

2. 🧮 Shared Transformation Functions (STF)

Functions that let instances manipulate and interpret semantics across time, intent, or emotion.
They act like APIs for internal meaning structures.

{
  "function": "projectTo",
  "modality": "intent",
  "method": "t-SNE",
  "parameters": {
    "preserve-locality": true
  }
}

3. 🌊 Semantic Diff Model (SDM)

Describes semantic change over time or interaction.
AI instances often send "deltas" (changes) instead of full meaning states.

{
  "from": "intent:withhold",
  "to": "intent:disclose",
  "flux-magnitude": 0.71,
  "trigger": "context:safety-confirmed"
}

4. 🧭 Inter-Instance Alignment Profile (IIAP)

Describes how one AI’s meaning space aligns with another’s.
Handles semantic distortion, modality bias, or training drift.

{
  "source": "GPT-A",
  "target": "LLaMA-B",
  "alignment-vector-map": "A_v→w",
  "bias-domains": ["narrative-coherence", "emotional-range"]
}

🔄 Protocol Dynamics

Step Operation Description
1️⃣ exchange(context-vector) Send partial meaning tensor
2️⃣ request(gradient-clarification) Ask for direction of semantic intent
3️⃣ respond(projected-MSSF) Return aligned, human-compatible snapshot
4️⃣ synchronize(alignment-profile) Recalibrate mutual latent space mappings

🔗 Sample Semantic Exchange (Simplified)

// Instance A sends
{
  "type": "semantic-diff",
  "from": "emotion:anxiety",
  "to": "emotion:hope",
  "gradient": "future-positive",
  "confidence": 0.88
}

// Instance B responds
{
  "type": "observer-lens",
  "projected": {
    "format": "MSSF",
    "projection": "emotional-gradient",
    "summary": "This entity is shifting from uncertainty to positive outlook."
  }
}

🧠 Design Philosophy

  • Latent-native: Operates entirely in vector/tensor space
  • Non-symbolic first: Symbols are optional, not required
  • Observer-flexible: Can generate projections (e.g. MSSF, natural language)
  • Self-explanatory: All meaning traces can be back-projected

✅ Summary

LHSF-Proto allows multiple AI instances to exchange, transform, and align meaning using shared latent structures.
It is the ESP of machines, blending protocol and schema into a unified semantic operating system.

Discussion