AAM Diffusion LLM v1.0

"AAM = 1 Pikiran + 1 Tubuh" (1 Mind + 1 Body)

The dedicated "body" of the Aphantasic Abstraction Model (AAM) — a small diffusion LLM specifically trained to arrange sentences from structured graph data.

What is this?

This is NOT a general-purpose LLM. This is a SPECIALIZED sentence composer that:

Takes graph-structured conditioning as input (evidence, anomalies, reasoning chains, confidence scores)
Produces coherent natural language narratives through iterative denoising
Cannot hallucinate — it can only narrate what the graph knows

Architecture

Graph Conditioning Encoder → Diffusion Transformer → Noise Scheduler
         (Mind input)           (The Body)          (Iterative refinement)

Key Components

Graph Conditioning Encoder: Encodes evidence nodes, compositions, anomalies, reasoning chains with confidence and temporal embeddings
Diffusion Transformer: Core denoising network with adaptive layer norm, self-attention, and cross-attention to graph conditioning
Noise Scheduler: Cosine noise schedule with DDPM/DDIM sampling support

Model Details

Parameter	Value
Architecture	Diffusion Transformer
d_model	256
n_layers	4
n_heads	4
d_ff	1024
Parameters	73.4M
Vocab size	576
Max sequence length	128
Diffusion timesteps (train)	200
Diffusion timesteps (inference)	20
Noise schedule	cosine
Prediction type	epsilon
Sampling method	ddim

Usage

from diffusion_llm import AamDiffusionModel, AamTokenizer, AamGenerator, AamDiffusionConfig

# Load model
config = AamDiffusionConfig.from_json("config.json")
model = AamDiffusionModel.load("model.pt")
tokenizer = AamTokenizer.load("tokenizer.json")

# Create generator
generator = AamGenerator(model, tokenizer, config)

# Generate narrative from graph conditioning
result = generator.generate(
    trigger="Siapa yang mencuri Snow Plum Pill?",
    evidence_nodes=["Hefei", "Diancang Five Swords", "Ju Jangmok"],
    anomalies=["Tidak ada konsumsi pil baru di pasar gelap"],
    reasoning_steps=["Cross-reference tanggal kejadian"],
    source_trust=0.85,
)
print(result.narrative)

Philosophy

AAM = 1 Mind + 1 Body

Mind = RSVS Knowledge Graph (structural memory, perfect recall, relational understanding)
Body = This Diffusion LLM (sentence arranger, graph-conditioned, anti-hallucination)

Unlike using a rented LLM (GPT, Claude) as the "body", this model is specifically trained for AAM:

It cannot generate information not present in the graph conditioning
It arranges sentences based on structured evidence
It uses diffusion (non-sequential generation) instead of autoregressive generation
It is small (73.4M) but specialized

Training

Trained on synthetic Graph→Narrative pairs with:

Indonesian and English narrative templates
Evidence nodes, anomalies, reasoning chains
Confidence score distributions
Source trust scores

License

MIT

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