SAVANT-IcosaGNN-IRM

Repository: antonypamo/SAVANT-IcosaGNN-IRM
Model type: IcosahedralGNNReasoner (graph neural network over an icosahedron)

Este repositorio contiene una GNN icosaédrica simbiótica entrenada sobre un grafo icosaédrico para razonar sobre roles (física, geometría, ética, información, etc.) a partir de salidas de un micro-AGI T5 (t5-small) y embeddings RRFSAVANTMADE (antonypamo/RRFSAVANTMADE).

In other words, this repository provides a small icosahedral graph neural network (GNN) that performs structured reasoning over 12 high-level cognitive/semantic roles, using:

• a micro-AGI language backbone: t5-small
• a resonant embedder: antonypamo/RRFSAVANTMADE

The model is designed as a symbiotic reasoning core within the broader Savant/RRF framework.

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1. Model Details

1.1. Config summary

{
  "model_type": "IcosahedralGNNReasoner",
  "graph": "icosahedron",
  "num_nodes": 12,
  "roles": [
    "física",
    "geometría",
    "información",
    "ética",
    "epistemología",
    "creatividad",
    "simbolismo",
    "entropía",
    "coherencia",
    "musicalidad",
    "cómputo",
    "metacognición"
  ],
  "micro_agi_repo": "t5-small",
  "embedder_repo": "antonypamo/RRFSAVANTMADE",
  "in_dim": 32,
  "hidden_dim": 64
}

Key points:

• Model type: IcosahedralGNNReasoner
• Graph topology: icosahedron (graph: "icosahedron", num_nodes: 12)
• Nodes: 12 labeled cognitive/semantic roles:
  • physics, geometry, information, ethics, epistemology, creativity, symbolism, entropy, coherence, musicality, computation, metacognition
• Backends:
  • micro_agi_repo = "t5-small" → micro-AGI language model
  • embedder_repo = "antonypamo/RRFSAVANTMADE" → RRF-inspired embedding model
• Dimensions:
  • input dimension to the GNN: in_dim = 32
  • hidden dimension in the GNN: hidden_dim = 64

This is not a text generator by itself. It is a graph-based reasoning layer that operates on embeddings derived from text.

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2. Relation to the RRF / Savant framework

This model is architecturally much closer to the RRF (Resonant Reasoning Framework) vision than a plain Transformer:

• It uses an icosahedral graph with 12 nodes, each node explicitly mapped to a cognitive/semantic role (e.g., ethics, coherence, metacognition).
• The GNN implements message passing over this fixed geometry, encouraging structured interactions between domains (e.g., physics ↔ geometry ↔ information; ethics ↔ coherence ↔ metacognition).
• The language backbone (t5-small) and the resonant embedder (RRFSAVANTMADE) act as input organs; the GNN is the reasoning core that aggregates and organizes these representations.

In short:

The icosahedral GNN is intended to act as a symbiotic reasoning nucleus inside a larger Savant/RRF system, rather than as a standalone large language model.

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3. Intended Use

3.1. Primary use cases

The model is suitable as a secondary/auxiliary model that receives embeddings from text (via t5-small + RRFSAVANTMADE) and outputs structured signals such as:

• Role activations: how much a given input engages each of the 12 roles (ethics, creativity, metacognition, etc.).
• Control / scoring signals for:
  • ranking or scoring candidate text generations,
  • evaluating coherence, entropy, or ethical alignment,
  • guiding selection of actions in an agent loop.

Typical applications:

• Meta-evaluation of language outputs (critic/judge model).
• Educational or curricular analysis:
  • Mapping texts, course descriptions, or student work into the icosahedral role space.
• Research on resonant / geometric cognition:
  • Studying how different domains (physics, ethics, information) interact in a structured graph.

3.2. Non-intended use

This model should not be used as:

• A standalone text generator (it does not generate text).
• A single source of truth for:
  • medical, legal, financial, or high-stakes decisions.
• A guarantee of ethical or value-aligned behavior:
  • the “ethics” node is a learned representation, not a normative authority.

Human oversight and domain expertise are required in any critical application.

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4. Architecture

Conceptual pipeline (high-level):

1. Text input (e.g., prompt, document, conversation snippet).
2. Embedding stage:
   • The text is encoded by:
     • t5-small (micro-AGI repo) and/or
     • RRFSAVANTMADE (resonant embedder)
   • Result: an embedding of dimension 32 (in_dim), or projected to that size.
3. Icosahedral GNN:
   • The embedding is distributed/initialized across the 12 nodes.
   • A graph neural network runs over the icosahedron:
     • message passing between neighboring roles,
     • hidden states of size hidden_dim = 64.
4. Role-level outputs:
   • Final node states can be:
     • read individually (per role activation),
     • pooled (global representation),
     • further mapped to scores, probabilities, or control signals.

Because the 12 nodes are labeled, the model offers a structured, interpretable intermediate representation of the reasoning process.

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5. Example Usage (conceptual)

Note: This is illustrative pseudo-code. Actual usage depends on the code released in the repository.

from transformers import AutoTokenizer, T5EncoderModel
from savant_icosagnn_irm import IcosahedralGNNReasoner  # hypothetical import

# 1. Load micro-AGI encoder (t5-small)
text_encoder_name = "t5-small"
tokenizer = AutoTokenizer.from_pretrained(text_encoder_name)
text_encoder = T5EncoderModel.from_pretrained(text_encoder_name)

# 2. Load Icosahedral GNN reasoner
gnn = IcosahedralGNNReasoner.from_pretrained("antonypamo/SAVANT-IcosaGNN-IRM")

text = "Explain how energy, entropy, and information are related in thermodynamics."

# Encode text
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
    enc_outputs = text_encoder(**inputs).last_hidden_state  # [batch, seq, hidden]

# (Simplify) Pool to a single embedding, then project to in_dim (32)
pooled = enc_outputs.mean(dim=1)  # [batch, hidden]
embedded = some_projection(pooled, out_dim=32)  # user-defined or from repo

# Run through the Icosahedral GNN
role_states, global_state = gnn(embedded)  # e.g. role_states: [batch, 12, 64]

# role_states can be mapped to scores per role (physics, entropy, ethics, etc.)
role_scores = heads_to_scores(role_states)  # application-dependent

You can then use role_scores to:

• Diagnose which roles are strongly engaged by the input.
• Guide downstream decisions (e.g., if “ethics” is low for a sensitive question, request human review).

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6. Bias, Risks & Limitations

• Data and training unknown:
  Without full training details (loss functions, datasets, IRM setup, etc.), one should assume:

  • possible biases inherited from both t5-small and RRFSAVANTMADE,
  • no guarantees of fairness or robustness across domains.

• Interpretation risk:
  The labeled roles (ethics, metacognition, coherence, etc.) are learned representations, not grounded philosophical or moral categories.
  Misinterpreting them as “absolute measures” of ethics or truth can be misleading.

• Small dimensionality:
  With in_dim = 32 and hidden_dim = 64, the model is designed to be lightweight, not a large-scale general reasoner.
  It is best suited for:

  • exploratory research,
  • adding structured signals on top of other models,
  • not as a single, universal decision-maker.

• No real-time knowledge:
  The model does not have access to current events or dynamic world updates. Any “knowledge” is static from the training phase.

Always combine this model with:

• Domain-specific checks.
• Human-in-the-loop review for sensitive tasks.

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7. How to Cite

If you use SAVANT-IcosaGNN-IRM in academic or technical work, you can cite it along these lines (adapt as needed):

@misc{savant_icosagnn_irm,
  title        = {SAVANT-IcosaGNN-IRM: Icosahedral Graph Neural Network Reasoner},
  author       = {Antonypamo},
  howpublished = {\url{https://huggingface.co/antonypamo/SAVANT-IcosaGNN-IRM}},
  note         = {Icosahedral GNN reasoner over 12 cognitive/semantic roles, driven by t5-small and RRFSAVANTMADE embeddings},
  year         = {2025}
}

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8. License

The precise license for this model should be checked on the Hugging Face model page.
This README does not define or override the official license.

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9. Summary

• What it is:
  A lightweight icosahedral GNN reasoner operating over 12 explicit roles, fed by t5-small and RRFSAVANTMADE embeddings.

• Why it matters:
  It introduces geometric, role-based structure in line with the Savant/RRF framework, enabling:

  • interpretable role activations,
  • structured reasoning signals on top of language models.

• How to use it:
  As a symbiotic reasoning module—a critic, controller, or analyzer—rather than a standalone text generator.