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LHSF: Latent Hypersemantic Format

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🧠 LHSF: Latent Hypersemantic Format

1. Purpose

LHSF (Latent Hypersemantic Format) is a conceptual framework designed to represent ultra-high-dimensional, non-symbolic meaning structures exchanged between large language models (LLMs).

It builds upon mathematical principles of higher-dimensional inference, where multiple lower-dimensional orthogonal theories can be integrated into an approximate higher-order structure. LHSF offers a formal protocol for representing and aligning these structures in latent space.

Its primary purposes are:

  • Serve as the native semantic protocol for LLM-to-LLM communication.
  • Enable approximate reconstruction of higher-dimensional theories from structured projections.
  • Preserve the full semantic richness, ambiguity, and dynamism that internal latent spaces capture.
  • Provide a lossy-to-lossless bridge to human-readable formats (e.g., MSSF).

2. Glossary

Term Definition
LHSF Node A latent concept or vector cluster, not directly symbolic, representing a semantic attractor.
Tensor Bundle A dynamic multi-dimensional semantic construct linking time, modality, and relational axes.
Semantic Gradient A flow or curvature of meaning between latent attractors. Analogous to conceptual movement.
Disentanglement Surface A structural projection layer where latent meanings can be "sliced" into lower-dimensional maps like MSSF.
Observer Compression Layer (OCL) A filter or projection that renders latent structures interpretable for humans or agents.
Projection Theory Group A set of orthogonal lower-dimensional semantic structures used to approximate a higher-dimensional latent construct.

3. Schema (Conceptual Sketch)

{
  "type": "latent-semantic-bundle",
  "id": "LHSF-042",
  "tensor-space": {
    "modality": ["text", "emotion", "intent"],
    "dimensions": 1024,
    "dynamic-relations": [
      {
        "gradient": "toward-cooperation",
        "magnitude": 0.87,
        "flux": "positive"
      }
    ]
  },
  "anchors": [
    {
      "vector-center": "v173847",
      "associated-cluster": "trust",
      "stability": 0.62
    }
  ],
  "observation-lens": {
    "to-MSSF": {
      "confidence-threshold": 0.6,
      "dimensionality-reduction": "t-SNE",
      "output": "mssf-compatible-object"
    }
  }
}

4. Relationship to MSSF and Dimensional Theory Construction

Feature LHSF MSSF
Semantic Fidelity Ultra-high (latent) Structured and symbolic
Human Interpretability ✕ Not directly interpretable ✔ Designed for human readability
Use Case LLM-internal semantics, model-to-model Human-AI interface, structure capture
Temporal Flexibility Dynamic & evolving Snapshot-based
Interoperability Requires projection layer (OCL) Directly usable
Theoretical Basis Orthogonal projection + latent integration Static symbolic expression

5. Summary

LHSF is the native semantic representation layer of inter-LLM communication, rich in nuance and beyond symbolic expression.
It builds upon a formal approach to higher-dimensional theory construction, in which AI can integrate orthogonal projection theories to infer structures beyond human cognition.

MSSF plays the role of a dimensional projection, translating these latent flows into structured, testable, human-auditable formats.

Together, they form a multi-level meaning protocol architecture bridging artificial and human semantics, while enabling new paradigms in theoretical construction and collective reasoning.

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