catalyst-brain 1.0.0

Catalyst Brain: O(1) Holographic Key-Value Cache, Quantum Attention, and Metacognitive Engine.

Catalyst Brain SDK

O(1) Holographic Memory, Grover-Amplified Attention, and Metacognitive Self-Improvement

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catalyst-brain is a Rust+PyO3 SDK providing hyperdimensional computing primitives for AI agents. Install from PyPI:

pip install catalyst-brain

Then import the SDK:

import catalyst_hdc as hdc

# Core HDC primitives
a = hdc.rand_bipolar(4096)
b = hdc.rand_bipolar(4096)
score = hdc.resonance(a, b)     # → ~0.5 for quasi-orthogonal vectors
bound = hdc.hdc_bind(a, b)     # XOR-like binding (self-inverse)
bundled = hdc.hdc_bundle(a, b)  # majority-vote superposition
shifted = hdc.hdc_permute(a, 3) # circular shift

# O(1) cognitive memory
from catalyst_hdc import PyHoloCPUScheduler
cpu = PyHoloCPUScheduler(dim=4096, quantum_capacity=8)
cpu.store_memory("user_pref_dark_mode")
assert cpu.recall("user_pref_dark_mode") == True
cpu.process_dopamine_hit(0.95)
print(cpu.dopamine_level())  # → 0.95

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SDK Reference

Core HDC Primitives

Raw hypervector algebra. All other SDK classes are built on these.

Function	Signature	Description
rand_bipolar	(dim: int) → list[float]	Random {−1, +1} hypervector
resonance	(a, b) → float	Cosine similarity normalized to [0, 1]
hdc_bind	(a, b) → list[float]	XOR-like binding (self-inverse: bind(bind(a,b),b) == a)
hdc_bundle	(a, b) → list[float]	Majority-vote superposition
hdc_permute	(v, n) → list[float]	Circular shift by n positions
normalise_bipolar	(v) → list[float]	Normalize to bipolar range

a = hdc.rand_bipolar(4096)
b = hdc.rand_bipolar(4096)

# Self-inverse binding (XOR)
assert hdc.resonance(a, b) > 0.4          # quasi-orthogonal
bound = hdc.hdc_bind(a, b)
recovered = hdc.hdc_bind(bound, b)
assert hdc.resonance(a, recovered) > 0.99  # a ⊕ (a ⊕ b) = b (bit-exact)

# Bundle N vectors using reduce
from functools import reduce
vectors = [hdc.rand_bipolar(4096) for _ in range(4)]
superposition = reduce(hdc.hdc_bundle, vectors)

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HoloCPU SDK — Cognitive Compute Engine

O(1) semantic memory with Grover-amplified attention routing.

from catalyst_hdc import PyHoloCPUScheduler
import catalyst_hdc as hdc

cpu = PyHoloCPUScheduler(dim=4096, quantum_capacity=8)

Memory

# Store and recall — O(1) regardless of how many memories exist
cpu.store_memory("user_preference_dark_mode")
cpu.store_memory("last_query")

assert cpu.recall("user_preference_dark_mode") == True
assert cpu.recall("nonexistent_key") == False

# Export entire cognitive state as a single 4096-float hypervector (16 KB constant)
state = cpu.export_holographic_state()
assert len(state) == 4096  # always 16 KB

Dopamine Feedback (RLHF replacement)

# Signal quality of an inference result (0.0 = bad, 1.0 = perfect)
cpu.process_dopamine_hit(0.95)  # positive outcome
print(cpu.dopamine_level())     # → 0.95 (elevated from baseline 0.5)

cpu.process_dopamine_hit(0.1)   # negative outcome
print(cpu.dopamine_level())     # → drops toward baseline

Grover-Amplified Attention

# quantum_grover_search takes a hypervector query + lists of key/value hypervectors
query = hdc.rand_bipolar(4096)
keys  = [hdc.rand_bipolar(4096) for _ in range(8)]
values = [hdc.rand_bipolar(4096) for _ in range(8)]

output = cpu.quantum_grover_search(query, keys, values)
# Returns a 4096-dim output vector from Grover-amplified routing
assert len(output) == 4096

Role Vectors

# Generate orthogonal role hypervectors for structured binding
agent   = cpu.generate_role("agent")
user    = cpu.generate_role("user")
system  = cpu.generate_role("system")

# Use for structured message encoding: message = bind(content, agent_role)

API Reference

Method	Signature	Description
dimension()	→ int	Hypervector dimensionality
quantum_capacity()	→ int	Qubit depth
store_memory(key)	(str) → None	Encode and store a semantic key
recall(key)	(str) → bool	O(1) key existence check
export_holographic_state()	→ list[float]	Full state as 4096 floats (16 KB)
process_dopamine_hit(hit)	(float) → None	RLHF signal (0.0–1.0)
dopamine_level()	→ float	Current dopamine level
quantum_grover_search(query, keys, values)	(Vec, list[Vec], list[Vec]) → list[float]	Grover attention
run_audit_integrity_check()	→ bool	System health check
generate_role(label)	(str) → list[float]	Orthogonal role vector

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HoloGen SDK — Geometric Hypervector Engine

Encode 3D geometry, materials, and photon states directly into hypervector space.

from catalyst_hdc import PyHoloGenEngine

engine = PyHoloGenEngine(dim=10_000)

Pixel Geometry

# Map screen coordinates to hypervector addresses
pixel_hv = engine.generate_pixel_geometry(64, 64)
# → list[int8], quasi-orthogonal per unique (x, y) pair

pixel_a = engine.generate_pixel_geometry(100, 200)
pixel_b = engine.generate_pixel_geometry(100, 201)  # adjacent pixel
# pixel_a and pixel_b are quasi-orthogonal — no hash collisions

Surface Materials

# A metallic surface at position (10, 0, 5) facing upward
surface_hv = engine.generate_material_mapping(
    position=[10.0, 0.0, 5.0],  # [f32; 3]
    normal=[0.0, 1.0, 0.0],     # surface normal [f32; 3]
    material_id=42
)

Photon State

# Encode photon color as a semantic hypervector
photon_hv = engine.generate_photon("blue")
# Supported: "blue", "red", "green", "yellow", "white", etc.

BVH Nodes

# encode_bvh_node(min_bounds, max_bounds, left_hv, right_hv)
# left_hv and right_hv must be bipolar hypervectors as list[int8]
# Convert: [int(x) for x in hdc.rand_bipolar(dim)]

left_hv  = [int(x) for x in hdc.rand_bipolar(4096)]
right_hv = [int(x) for x in hdc.rand_bipolar(4096)]

bvh_node = engine.encode_bvh_node(
    [0.0, -10.0, 0.0],   # min_bounds [f32; 3]
    [10.0, 10.0, 10.0],  # max_bounds [f32; 3]
    left_hv,
    right_hv,
)

Counterfactual Physics

# Ask "what if this photon took a different path?"
actual_state    = {"action": "jump", "outcome": "reward"}
intervention    = {"action": "crouch", "outcome": "reward"}

alt_reality = engine.simulate_counterfactual(actual_state, intervention)
# Returns hypervector encoding hypothetical deviation

API Reference

Method	Signature	Description
structural_dimension()	→ int	Hypervector dimensionality
generate_pixel_geometry(x, y)	(u32, u32) → list[int8]	Pixel coords → HDC address
generate_material_mapping(position, normal, material_id)	([f32;3], [f32;3], u32) → list[int8]	Surface → HDC
generate_photon(color)	(str) → list[int8]	Color string → HDC
encode_bvh_node(min_bounds, max_bounds, left_hv, right_hv)	([f32;3], [f32;3], Vec<i8>, Vec<i8>) → list[int8]	BVH node
simulate_counterfactual(state, intervention)	(dict, dict) → list[int8]	Counterfactual physics

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Metacognition & Self-Audit

Biological self-improvement loop: observe → recommend → apply → audit.

from catalyst_hdc import PyMetacognition, PyOptimizer, PySelfAudit
import catalyst_hdc as hdc

meta = PyMetacognition(dim=4096)

Record Observations

# Record inference outcomes with resonance, coherence, accuracy
hv = hdc.rand_bipolar(4096)
meta.record(res=0.85, coh=0.90, acc=0.75, context=hv, hash=12345)
meta.record(res=0.92, coh=0.88, acc=0.81, context=hv, hash=12346)
meta.record(res=0.61, coh=0.72, acc=0.55, context=hv, hash=12347)

Query State

print(f"success_rate:  {meta.success_rate():.3f}")   # ratio of high-resonance successes
print(f"avg_resonance: {meta.avg_resonance():.3f}")  # mean resonance score
recs = meta.recommend()
# → [("serotonin_increase", 0.05, "success rate 1 > 80%, reinforce"), ...]

Apply Recommendations

opt = PyOptimizer()
opt.apply("serotonin_increase", 0.05, "success rate above 80%")
params = opt.get_params()
# → {"dopamine": 0.6, "serotonin": 0.5, "acetylcholine": 0.55, "identity_lr": 0.01}
opt.rollback()  # revert last parameter change

Audit Integrity

audit = PySelfAudit(dim=4096)
hv = hdc.rand_bipolar(4096)
score, passed, issues = audit.full_audit(hv)
# → score=1.0, passed=True, issues=[]

API Reference

Class	Method	Signature	Description
PyMetacognition	record(res, coh, acc, context, hash)	(float, float, float, Vec, u64)	Log observation
PyMetacognition	success_rate()	→ float	Ratio of high-res successes
PyMetacognition	avg_resonance()	→ float	Mean resonance
PyMetacognition	recommend()	→ list[tuple]	Parameter recommendations
PyOptimizer	apply(action, delta, reason)	(str, float, str)	Apply parameter delta
PyOptimizer	get_params()	→ dict	Current parameters
PyOptimizer	rollback()	→ None	Revert last change
PySelfAudit	full_audit(hv)	(Vec) → (float, bool, list)	Integrity check

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Quantum Attention Head

Drop-in replacement for standard softmax attention using Grover-amplified routing.

from catalyst_hdc import PyQuantumAttentionHead
import catalyst_hdc as hdc

head = PyQuantumAttentionHead(dim=512, nqubits=4)

query  = hdc.rand_bipolar(512)
keys   = [hdc.rand_bipolar(512) for _ in range(10)]
values = [hdc.rand_bipolar(512) for _ in range(10)]

output = head.compute(query, keys, values)
# Returns 512-dim output vector

Method	Signature	Description
compute(query, keys, values)	(Vec, list[Vec], list[Vec]) → list[float]	Grover attention

Note: amplify() does not exist as a standalone method. Grover amplification for large memory stores is implemented inside PyHoloCPUScheduler.quantum_grover_search(). PyQuantumAttentionHead is for fine-grained per-layer attention.

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Benchmarks

Memory Footprint

Catalyst state is constant — it never grows with token count.

Tokens	Standard FP16 KV-Cache	Catalyst HKVC	Reduction
1,000	1,220.70 MB	0.15 MB	8,000x
5,000	6,103.52 MB	0.15 MB	40,000x
10,000	12,207.03 MB	0.15 MB	80,000x

Bit-Exact Recovery

Bind/unbind is provably lossless — XOR is its own inverse.

Operation	Fidelity	Tested depth
BCV bind/unbind	100.00% bit-exact	1,000 trials
Chained composition (depth 2–100)	100.00% bit-exact	6 depths
HMK serialization	100.00% bit-exact	100 trials

Multi-Item Superposition

Multi-item bundling maintains 98.4% constant bit accuracy regardless of item count (up to ~7,213 items at D=10,000).

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Build from Source

# Requires Rust 1.75+ and Python 3.10+
git clone https://github.com/quantium-rock/catalyst-brain.git
cd catalyst-brain

# Install Python package (builds Rust extension via setuptools-rust)
pip install .

# Run tests
cargo test --workspace

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Architecture

catalyst-brain/
├── src/
│   ├── lib.rs        # Core HDC: CausalMemory, bind/unbind, resonance
│   └── py_api.rs     # PyO3 bindings: all Python-facing classes
├── holocpu_sdk/      # O(1) scheduler + Grover search
├── hologen_sdk/      # Geometric encoding facade
├── quantum_heads/    # Grover attention head
├── metalearning/     # Metacognition, SelfAudit, Optimizer
├── hkvc_graphics/    # BVH, ray tracing, frame buffer
├── Cargo.toml        # Workspace manifest
└── pyproject.toml    # Python package config

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License

Research & Evaluation License v1.0 — see LICENSE file.

Use	Permitted?
Academic research	✅ Free
Personal experimentation	✅ Free
Benchmarking & evaluation	✅ Free
Publishing results (with attribution)	✅ Free
Production / commercial deployment	❌ Requires Commercial License
SaaS / hosted API	❌ Requires Commercial License

Patent: U.S. Provisional Patent Application CATALYST-2026-001 covers holographic key-value caching, BlockCodeVector binding, resonant superposition memory, and Grover-amplified attention routing.

Contact: [e-mail]

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Copyright © 2026 Strategic Innovations AI. Built with Rust 🦀 + PyO3 🐍.