The Prediction Engine: Bayesian Confidence, VAE, and Neural Policy

What is the Prediction Engine?

The prediction engine is a GPU-accelerated system for understanding relationships between tokens (words, concepts, symbols). It uses three evaluation channels, generates semantic variants, and learns from experience via a neural policy head and VAE.

Quick Start

prediction.enable(32, 0.15)  # 32-dim embeddings, 0.15 epsilon

# Record relations
prediction.record("cat", "animal", 1.0)
prediction.record("dog", "animal", 1.0)
prediction.record("cat", "pet", 0.8)

# Evaluate similarity
let result = prediction.evaluate("cat", "dog")
print(result.combined)  # > 0.0

# Find most related tokens
let related = prediction.evaluate_all("cat", 5)
for each r in related
    print(f"{r.token}: {r.combined}")

3-Layer Evaluation

Every prediction.evaluate(from, to) returns scores from three independent channels:

| Channel | What it measures | Weight |

|---------|-----------------|--------|

| Linear | Direct relation strength (normalized) | 0.4 |

| Echo | Presence-count resonance (log-scaled) | 0.2 |

| Relational | Cosine similarity in embedding space | 0.4 |

The combined score is the weighted blend.

Multi-Variant Generation

Generate 3-4 semantically distinct interpretations of a relation:

let variants = prediction.generate_variants("cat", "dog", "+")
# [{mode: "co-presence", score: 0.42, prob: 0.28, chosen: true},
#  {mode: "aggregation", score: 0.38, prob: 0.25, chosen: false},
#  {mode: "union", score: 0.35, prob: 0.23, chosen: false},
#  {mode: "resonance", score: 0.31, prob: 0.22, chosen: false}]

Each operator (+, -, *, /, ^) generates different semantic modes. For example, - generates: withdrawal, differentiation, removal, contrast.

Neural Policy Head

A 2-layer MLP that learns which variant to select:

prediction.enable_neural_policy(16)  # 16-dim hidden layer

# The policy observes variant scores + token embeddings
# and learns via REINFORCE gradient updates
prediction.update_policy("context", 0, 1.0)  # reward variant 0

Exploration Modes

prediction.set_exploration("epsilon")    # default: random with probability epsilon
prediction.set_exploration("gumbel")     # Gumbel-softmax noise
prediction.set_exploration("boltzmann")  # sample from softmax(scores/temp)
prediction.set_temperature(0.5)          # lower = more greedy

VAE Training

Train a variational autoencoder on token embeddings:

prediction.train_vae("cat", 0.001)
# Loss = MSE reconstruction + 0.01 * KL divergence
# Uses proper reparameterization trick

Iterative training improves the latent space structure over time.

Prototype Memory

Store up to 64 cluster centroids in latent space:

prediction.store_prototype("cat")
prediction.store_prototype("dog")
print(prediction.prototype_count())  # merges if cosine > 0.8

Bayesian Confidence

let conf = prediction.confidence("weather", ["sunny", "warm", "clear"])
print(conf)  # 0.0 to 1.0

Used by revise statements to blend engine posterior with user updates.

Auto-Feed from Cognitive Statements

When enabled, cognitive primitives automatically feed the engine:

• belief / observe / revise statements feed tokens
• Sequences buffer and flush at size 8 with 2-token overlap
• revise blends 70% user + 30% engine Bayesian posterior
• observe nudges related beliefs by combined_score * 0.05

Persistence

prediction.save("model.npme")      # NPME v3 binary format
prediction.load("model.npme")      # loads v2 or v3
prediction.save_ncf("model.ncf")   # NCF format
prediction.load_ncf("model.ncf")

GPU Acceleration

evaluate_all automatically uses TF32 tensor core SGEMM for batch cosine similarity when vocabulary >= 256 tokens.