You can deploy this model and start earning money today!

Kateira1123/
fuga-2.0

Name: fuga-2.0
Availability: InStock

Views

No views yet

Fuga 2.0 — VSA Hierarchical Predictive Memory & Code Generation

Non-transformer AGI core built on Vector Symbolic Architectures (VSA), Hierarchical JEPA, Temporal Memory, and locality-sensitive binding. Replaces backpropagation with local Delta Rule updates and generates Rust code via pure VSA/TM autoregression — no LLM dependency.

Training Pipeline

1. Index source files into phase graph

bash

# Index a directory — reads .rs files, creates PhaseNodes with SDR encoding
cargo run --release -- mirror-index src/ai

# Load existing mirror and index another directory
cargo run --release -- mirror-index src/core

Creates fuga_mirror_nodes.bin (phase nodes), fuga_mirror_tm.bin (TM), fuga_mirror_jepa.bin (HJEPA).

2. Train the predictor (HJEPA + TM)

bash

# Train on existing mirror nodes (5 epochs, chunk=1)
cargo run --release -- train-predictor 5

# With larger chunks for sequence patterns
cargo run --release -- train-predictor 10 --chunk 3

3. Train token vocabulary (embedded in generation)

bash

# Builds top-4000 char-level token vocab from indexed .rs files
# then trains TM on 20000+ token bigram steps
# then generates tokens
cargo run --release -- generate-code "fn new" --tokens

The token trainer:

Char-level tokenizer: splits identifiers from operators, recognizes -> :: => != == >= <= += -= && ||
Syntactic pattern injection: 14 hardcoded Rust patterns × 5 repeats
WTA (Winner-Take-All) prediction with Inhibition of Return
Anti-repetition window (16 tokens)

Generation

Token-level (syntactic)

cargo run --release -- generate-code "fn new" --tokens

Outputs real Rust tokens: ( ) { } [ ] , :: . ' -> \ + identifiers, numbers

PhaseNode-level (semantic)

bash

# Beam search over PhaseNode graph
cargo run --release -- generate-code "struct Foo"

# Autoregressive mode (generates full snippets)
cargo run --release -- generate-code "fn new" --gen

# With beam width and temperature
cargo run --release -- generate-code "async fn" --beam 3 --temp 1.2

Query & Evaluation

bash

# Self-query — find matching phase nodes
cargo run --release -- self-query "async fn handle"

# Evaluate mirror quality
cargo run --release -- eval

# Inspect text or file
cargo run --release -- inspect "fn new() -> Self"
cargo run --release -- inspect src/main.rs

Tests

bash

# All library tests
cargo test --lib

# Anomaly detection (Inhibition of Return, overshoot)
cargo test --test test_anomaly_detection -- --nocapture

# JEPA / TM / MoE tests
cargo test --test jepa_test
cargo test --test hierarchical_jepa_test
cargo test --test moe_routing_test

Architecture

Component	Description
Hypervector	8192-bit, ~2% density (~164 active bits), XOR bind / sum bundle / permute
Hierarchical JEPA	L0 (static), L1 (macro), L2 (metacognition) with ls_bind phase-shift
Temporal Memory	Cells with DendriteSegments, learn_segment / reinforce / prune / predict_next
SDR (Sparse Distributed Representation)	`encode_text()` → deterministic hash-based sparse binary vector
Tokenizer	Char-level: splits identifiers from operators, multi-char operator recognition
WTA	Winner-Take-All with Inhibition of Return (fatigue = wins × 10, decay every 10 steps)
AnomalyEvent	Detects phase overload — `pred_count > 100` or `power_mw > 500` triggers overshoot

License

Apache-2.0

Kateira1123/fuga-2.0

Fuga 2.0 — VSA Hierarchical Predictive Memory & Code Generation

Training Pipeline

1. Index source files into phase graph

2. Train the predictor (HJEPA + TM)

3. Train token vocabulary (embedded in generation)

Generation

Token-level (syntactic)

PhaseNode-level (semantic)

Query & Evaluation

Tests

Architecture

License

Kateira1123/
fuga-2.0