FishStick..🐠🪾

A mathematically rigorous, physically grounded AI framework synthesizing theoretical physics, formal mathematics, and advanced machine learning.

FishStick..🐠🪾

Fishing that sticks! 🎣

Na dat dere waa bwokee, its kay,i Phhhisshtikk

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IM ONLY KIDDING

FishStick.. 🐠🪾🚀

"Is a Comprehensive and Robust AI/ML Training Ecosystem"

Overview

fishstick implements 26 unified theoretical frameworks (A-Z) that combine:

🎯 Theoretical Physics: Symmetry, renormalization, variational principles, thermodynamics
📐 Formal Mathematics: Category theory, sheaf cohomology, differential geometry, type theory
🤖 Advanced ML: Equivariant deep learning, neuro-symbolic integration, formal verification

Core Philosophy

This framework treats AI not as empirical engineering but as a branch of mathematical physics, where:

Neural architectures are morphisms in dagger compact closed categories
Training dynamics are gradient flows on statistical manifolds
Attention mechanisms respect sheaf cohomology constraints
Models satisfy thermodynamic bounds on computation

Installation

Basic Installation

# Clone the repository
git clone https://github.com/NeuralBlitz/fishstick.git
cd fishstick

# Install core dependencies
pip install torch numpy scipy pyyaml

# Install package
pip install -e .

Full Installation (with all features)

# Install all optional dependencies
pip install torchdiffeq torch-geometric

# Or install with extras
pip install -e ".[full]"

Dependencies

Core:

Python ≥ 3.9
PyTorch ≥ 2.0
NumPy ≥ 1.21
SciPy ≥ 1.7

Optional:

torchdiffeq - For Neural ODE solvers
torch-geometric - For geometric graph neural networks

Quick Start

1. Core Types and Manifolds

import torch
from fishstick import (
    MetricTensor, SymplecticForm, PhaseSpaceState,
    StatisticalManifold, FisherInformationMetric
)

# Create statistical manifold
manifold = StatisticalManifold(dim=10)

# Fisher information metric
def log_prob(params):
    return (params**2).sum()

params = torch.randn(10, requires_grad=True)
metric = manifold.fisher_information(params, log_prob)

# Phase space for Hamiltonian dynamics
state = PhaseSpaceState(
    q=torch.randn(5),  # positions
    p=torch.randn(5)   # momenta
)

2. Categorical Structures

from fishstick import (
    MonoidalCategory, Functor, NaturalTransformation,
    DaggerCategory, Lens
)

# Create monoidal category
cat = MonoidalCategory("NeuralCategory")

# Define objects and morphisms
from fishstick.categorical.category import Object

obj1 = Object(name="Input", shape=(784,))
obj2 = Object(name="Hidden", shape=(256,))

cat.add_object(obj1)
cat.add_object(obj2)

# Lens for bidirectional learning
lens = Lens(
    get=lambda x: x * 2,
    put=lambda s, a: s + a
)

3. Hamiltonian Neural Networks

from fishstick import HamiltonianNeuralNetwork

# Energy-conserving neural network
hnn = HamiltonianNeuralNetwork(
    input_dim=10,
    hidden_dim=64,
    n_hidden=3
)

# Integrate dynamics
z0 = torch.randn(4, 20)  # batch of 4, phase space dim 20
trajectory = hnn.integrate(z0, n_steps=100, dt=0.01)

# trajectory shape: [101, 4, 20]
# Energy is conserved along trajectory

4. Sheaf-Optimized Attention

from fishstick import SheafOptimizedAttention

# Attention with cohomological constraints
attn = SheafOptimizedAttention(
    embed_dim=256,
    num_heads=8,
    lambda_consistency=0.1
)

x = torch.randn(2, 100, 256)  # [batch, seq_len, embed_dim]

# Define open cover for local consistency
open_cover = [[0, 1, 2], [2, 3, 4], [4, 5, 6]]

output, weights = attn(x, open_cover=open_cover)

The 26 Frameworks (A-Z)

The framework includes 26 theoretical frameworks spanning A-Z, each implementing different combinations of:

Categorical & geometric structures
Hamiltonian/symplectic dynamics
Sheaf theory & cohomology
Renormalization group methods
Thermodynamic bounds
Formal verification

A. UniIntelli - Categorical–Geometric–Thermodynamic Synthesis

from fishstick.frameworks.uniintelli import create_uniintelli

model = create_uniintelli(
    input_dim=784,
    hidden_dim=256,
    output_dim=10
)
# 1.8M parameters

B. HSCA - Holo-Symplectic Cognitive Architecture

from fishstick.frameworks.hsca import create_hsca

model = create_hsca(input_dim=784, output_dim=10)
# 6.5M parameters - Energy-conserving Hamiltonian dynamics

C. UIA - Unified Intelligence Architecture

from fishstick.frameworks.uia import create_uia

model = create_uia(input_dim=784, output_dim=10)
# 1.7M parameters - CHNP + RG-AE + S-TF + DTL

D. SCIF - Symplectic-Categorical Intelligence Framework

from fishstick.frameworks.scif import create_scif

model = create_scif(input_dim=784, output_dim=10)
# 3.8M parameters - Fiber bundles + Hamiltonian dynamics

E. UIF - Unified Intelligence Framework

from fishstick.frameworks.uif import create_uif

model = create_uif(input_dim=784, output_dim=10)
# 367K parameters - 4-layer architecture

F. UIS - Unified Intelligence Synthesis

from fishstick.frameworks.uis import create_uis

model = create_uis(input_dim=784, output_dim=10)
# 861K parameters - Quantum-inspired + RG + Neuro-symbolic

G. CRLS - Categorical Renormalization Learning Systems

from fishstick.frameworks.crls import create_crls

model = create_crls(input_dim=784, output_dim=10)
# 958K parameters - Categorical RG flows + VAE

H. ToposFormer - Topos-Theoretic Neural Networks

from fishstick.frameworks.toposformer import create_toposformer

model = create_toposformer(input_dim=784, output_dim=10)
# 4.8M parameters - Sheaf integration + Hodge projection

I. UIF-I - Renormalized Attention Module (RAM)

from fishstick.frameworks.uif_i import create_uif_i

model = create_uif_i(input_dim=784, output_dim=10)
# 1.4M parameters - Scale-parameterized attention

J. UIS-J - Node-at-Attention Mechanism

from fishstick.frameworks.uis_j import create_uis_j

model = create_uis_j(input_dim=784, output_dim=10)
# 2.3M parameters - Cohomological attention + cross-synthesis

K. UIA-K - Sheaf-LSTM with Fiber Bundle Attention

from fishstick.frameworks.uia_k import create_uia_k

model = create_uia_k(input_dim=784, output_dim=10)
# 1.4M parameters - Fiber bundle attention + RG-MORL

L. CRLS-L - Mathematical Intelligence Physics

from fishstick.frameworks.crls_l import create_crls_l

model = create_crls_l(input_dim=784, output_dim=10)
# 375K parameters - Symplectic integrator + thermodynamic

M. UIA-M - Renormalized Neural Flow

from fishstick.frameworks.uia_m import create_uia_m

model = create_uia_m(input_dim=784, output_dim=10)
# 1.2M parameters - Symplectic dynamics + HBP

N. UIS-N - Cross-Synthetic Node-at-Attention

from fishstick.frameworks.uis_n import create_uis_n

model = create_uis_n(input_dim=784, output_dim=10)
# 4.1M parameters - Meta-rep signatures + lens optics

O. UIA-O - Sheaf-Theoretic Neural Networks

from fishstick.frameworks.uia_o import create_uia_o

model = create_uia_o(input_dim=784, output_dim=10)
# 822K parameters - Neural sheaf Laplacian + fiber bundles

P. UIF-P - RG-Informed Hierarchical Networks

from fishstick.frameworks.uif_p import create_uif_p

model = create_uif_p(input_dim=784, output_dim=10)
# 123K parameters - RG fixed points + symplectic SGD

Q. UINet-Q - Categorical Quantum Neural Architecture

from fishstick.frameworks.uinet_q import create_uinet_q

model = create_uinet_q(input_dim=784, output_dim=10)
# 2.0M parameters - ZX-calculus + categorical compilation

R. UIF-R - Comprehensive Blueprint with Fisher Natural Gradient

from fishstick.frameworks.uif_r import create_uif_r

model = create_uif_r(input_dim=784, output_dim=10)
# 291K parameters - Fisher information + monoidal composition

S. USIF-S - Quantum Categorical Neural Networks (QCNN)

from fishstick.frameworks.usif_s import create_usif_s

model = create_usif_s(input_dim=784, output_dim=10)
# 1.5M parameters - Quantum channels + topological features

T. UIF-T - Hamiltonian-RG Flow Optimizer

from fishstick.frameworks.uif_t import create_uif_t

model = create_uif_t(input_dim=784, output_dim=10)
# 330K parameters - Hamiltonian RG flow + auto-flow

U. USIF-U - Thermodynamic Information Bounds

from fishstick.frameworks.usif_u import create_usif_u

model = create_usif_u(input_dim=784, output_dim=10)
# 910K parameters - Hilbert space + quantum info bounds

V. UIF-V - Information-Theoretic Dynamics

from fishstick.frameworks.uif_v import create_uif_v

model = create_uif_v(input_dim=784, output_dim=10)
# 247K parameters - Stochastic action + type-theoretic verifier

W. MCA-W - Meta-Cognitive Architecture

from fishstick.frameworks.mca_w import create_mca_w

model = create_mca_w(input_dim=784, output_dim=10)
# 1.1M parameters - Meta-cognitive transformer + homotopy

X. TTSIK-X - Topos-Theoretic Symplectic Intelligence Kernel

from fishstick.frameworks.ttsik_x import create_ttsik_x

model = create_ttsik_x(input_dim=784, output_dim=10)
# 864K parameters - Symplectic sheaf + natural gradient HMC

Y. CTNA-Y - Categorical-Thermodynamic Neural Architecture

from fishstick.frameworks.ctna_y import create_ctna_y

model = create_ctna_y(input_dim=784, output_dim=10)
# 641K parameters - Traced monoidal + formal verification

Z. SCIF-Z - Symplectic-Categorical Intelligence Framework

from fishstick.frameworks.scif_z import create_scif_z

model = create_scif_z(input_dim=784, output_dim=10)
# 475K parameters - Causal intervention + topological analysis

Advanced Features

Neural ODEs

from fishstick.neural_ode import NeuralODE, ODEFunction

# Define dynamics
odefunc = ODEFunction(dim=10, hidden_dim=64)

# Create Neural ODE with adaptive solver
node = NeuralODE(
    odefunc,
    t_span=(0.0, 1.0),
    method='dopri5',  # Dormand-Prince
    rtol=1e-5,
    atol=1e-6
)

z0 = torch.randn(4, 10)
z1 = node(z0)

Geometric Graph Neural Networks

from fishstick.graph import (
    EquivariantMessagePassing,
    GeometricGraphTransformer,
    MolecularGraphNetwork
)

# E(n)-equivariant message passing
layer = EquivariantMessagePassing(
    node_dim=64,
    edge_dim=0,
    hidden_dim=128
)

# Node features and 3D positions
x = torch.randn(100, 64)
pos = torch.randn(100, 3)
edge_index = torch.randint(0, 100, (2, 500))

x_out, pos_out = layer(x, pos, edge_index)

Probabilistic / Bayesian Neural Networks

from fishstick.probabilistic import (
    BayesianLinear,
    BayesianNeuralNetwork,
    MCDropout,
    DeepEnsemble
)

# Bayesian layer with variational inference
layer = BayesianLinear(784, 256, prior_sigma=1.0)
x = torch.randn(4, 784)
output = layer(x, sample=True)

# Full BNN
bnn = BayesianNeuralNetwork(
    input_dim=784,
    hidden_dims=[256, 128],
    output_dim=10
)

# Predict with uncertainty
mean, uncertainty = bnn.predict_with_uncertainty(x, n_samples=100)

Normalizing Flows

from fishstick.flows import RealNVP, Glow, MAF

# RealNVP coupling flows
flow = RealNVP(dim=8, n_coupling=8, hidden_dim=256)
x = torch.randn(100, 8)

# Density estimation
log_prob = flow.log_prob(x)

# Sampling
samples = flow.sample(1000)

# Glow with 1x1 convolutions
glow = Glow(dim=8, n_levels=3, n_steps_per_level=4)

Equivariant Networks

from fishstick.equivariant import (
    SE3EquivariantLayer,
    SE3Transformer,
    EquivariantMolecularEnergy
)

# SE(3)-equivariant layer
layer = SE3EquivariantLayer(
    in_features=32,
    out_features=32,
    hidden_dim=64
)

features = torch.randn(10, 32)
coords = torch.randn(10, 3)  # 3D positions
edge_index = torch.randint(0, 10, (2, 30))

f_out, c_out = layer(features, coords, edge_index)
# c_out is equivariant to rotations/reflections

Causal Inference

from fishstick.causal import (
    CausalGraph,
    StructuralCausalModel,
    CausalDiscovery
)

# Define causal DAG
import numpy as np
adjacency = np.array([
    [0, 1, 0],
    [0, 0, 1],
    [0, 0, 0]
])
graph = CausalGraph(n_nodes=3, adjacency=adjacency)

# Structural Causal Model
scm = StructuralCausalModel(graph, hidden_dim=64)

# Observational sampling
sample = scm.forward()

# Interventional: do(X=2.0)
intervention = {0: torch.tensor([[2.0]])}
sample_do = scm.do_calculus(intervention_node=0, 
                           value=torch.tensor([[2.0]]))

# Causal discovery from data
data = np.random.randn(1000, 3)
learned_graph = CausalDiscovery.pc_algorithm(data)

Diffusion Models

from fishstick.diffusion import DiffusionModel, DDPM, DDIM

# Denoising Diffusion Probabilistic Model
ddpm = DDPM(
    model=model,
    timesteps=1000,
    beta_schedule="linear"
)

# Forward diffusion (add noise)
noised_x = ddpm.add_noise(x, t)

# Reverse process (denoise)
denoised = ddpm.sample(batch_size=4)

Reinforcement Learning

from fishstick.rl import PPOAgent, DQNAgent, ActorCritic

# PPO Agent
agent = PPOAgent(
    state_dim=64,
    action_dim=4,
    hidden_dim=256
)

# Collect experience and update
for episode in range(100):
    state = env.reset()
    done = False
    while not done:
        action = agent.select_action(state)
        next_state, reward, done = env.step(action)
        agent.store_transition(state, action, reward, next_state, done)
        state = next_state
    agent.update()

Federated Learning

from fishstick.federated import FederatedClient, FederatedServer

# Federated averaging
server = FederatedServer(
    model=global_model,
    aggregation="fedavg"
)

# Add clients
for client_data in client_datasets:
    client = FederatedClient(
        model=global_model,
        data=client_data,
        local_epochs=5
    )
    server.add_client(client)

# Run federated training
server.train(n_rounds=10)

Continual Learning

from fishstick.continual import EWC, PackNet, ProgressiveNeuralNetwork

# Elastic Weight Consolidation
ewc = EWC(
    model=task1_model,
    dataloader=task1_loader,
    importance=1000
)

# Train on task 2 with EWC regularization
for epoch in range(10):
    for batch in task2_loader:
        output = model(batch.x)
        loss = criterion(output, batch.y)
        ewc_loss = ewc.penalty(model)
        (loss + ewc_loss).backward()

Time Series Forecasting

from fishstick.timeseries import TimeSeriesTransformer, TemporalCNN, LSTM

# Time series forecasting model
model = TimeSeriesTransformer(
    input_dim=10,
    d_model=128,
    n_heads=4,
    n_layers=3,
    prediction_horizon=24
)

# Forecast future values
output = model(input_sequence)  # [batch, horizon, features]

Audio & Speech Processing

from fishstick.audio import AudioEncoder, SpeechRecognition
from fishstick.speech import WaveNet

# Audio feature extraction
encoder = AudioEncoder(sample_rate=16000)
features = encoder.extract_mfcc(audio_tensor)

# Speech recognition
asr = SpeechRecognition(model_name="wav2vec2")
transcription = asr.transcribe(audio_tensor)

Privacy-Preserving ML

from fishstick.privacy import DifferentialPrivacy, SecureAggregation
from fishstick.federated import SecureAggregation

# Differential Privacy
dp = DifferentialPrivacy(
    epsilon=1.0,
    delta=1e-5,
    max_grad_norm=1.0
)
dp_model = dp.apply(model)

# Secure aggregation for federated learning
secure_agg = SecureAggregation(threshold=0.5)
aggregated_update = secure_agg.aggregate(client_updates)

Model Compression

from fishstick.quantization import QuantizedModel, PostTrainingQuantization
from fishstick.pruning import MagnitudePruner, LotteryTicketFinder

# Post-training quantization
quantizer = PostTrainingQuantization(backend="fbgemm")
quantized_model = quantizer.quantize(model, calibration_data)

# Network pruning
pruner = MagnitudePruner(sparsity=0.5)
pruned_model = pruner.prune(model)

Anomaly Detection

from fishstick.anomaly_detection import IsolationForest, AutoencoderAnomaly, OneClassSVM

# Autoencoder-based anomaly detection
detector = AutoencoderAnomaly(
    input_dim=784,
    latent_dim=16,
    threshold=0.1
)

# Detect anomalies
scores = detector.predict(data)
anomalies = scores > detector.threshold

Architecture

FishStick contains 234 modules organized into the following categories:

Core & Theory

fishstick/
├── core/               # Fundamental types (MetricTensor, SymplecticForm, PhaseSpaceState)
├── categorical/       # Category theory (MonoidalCategory, Functor, Lens)
├── geometric/         # Manifolds, Fisher information, sheaf theory
├── dynamics/          # Hamiltonian NNs, thermodynamic gradient flows
├── sheaf/            # Sheaf-optimized attention
├── rg/               # Renormalization group autoencoders
├── topology/         # Algebraic topology utilities
└── verification/    # Formal verification (Lean, SMT)

Frameworks (26 A-Z)

├── frameworks/       # 26 unified theoretical frameworks

Deep Learning

├── neural_ode/       # Neural ODE solvers
├── graph/            # Geometric GNNs
├── equivariant/      # SE(3)-equivariant networks
├── diffusion/        # Diffusion models
├── flows/            # Normalizing flows
├── transformer/     # Transformer architectures
├── attention/       # Attention mechanisms
├── recurrent/       # RNNs, LSTMs, GRUs
├── convolutional/    # CNNs, conv layers
└── vision/           # Vision models

Probabilistic & Bayesian

├── probabilistic/   # Bayesian NNs, MCDropout
├── bayesian_advanced/# GP, conjugate priors
├── distributions/   # Probability distributions
├── uncertainty/     # Uncertainty quantification
└── uncertainty_ext/ # Advanced uncertainty

Causal & Logic

├── causal/           # Causal inference
├── causal_inference/ # SCM, causal discovery
├── causal_advanced/  # Advanced causal methods
├── logic_advanced/   # Logic, symbolic reasoning
├── neural_prover/    # Neural theorem proving
└── knowledge_graph/ # Knowledge graphs

Learning Paradigms

├── reinforcement/   # RL algorithms
├── rl/              # Reinforcement learning
├── rl_extensions/   # Extended RL
├── federated/       # Federated learning
├── federated_ext/   # Advanced federated
├── continual/       # Continual learning
├── continual_learning/# Extended continual
├── active_learning/ # Active learning
├── selfsupervised/  # Self-supervised
├── fewshot_learning/# Few-shot learning
├── zeroshot/        # Zero-shot learning
└── transfer/       # Transfer learning

Multi-Modality

├── multimodal/      # Multimodal learning
├── vision/           # Computer vision
├── audio/            # Audio processing
├── speech/           # Speech recognition
├── nlp/              # Natural language
├── text_generation/  # Text generation
├── image_generation/ # Image generation
├── video_advanced/   # Video processing
└── scene_understanding/# Scene understanding

Data & Processing

├── data/             # Data utilities
├── data_processing/  # Data pipelines
├── augmentation/     # Data augmentation
├── augmentation_ext/ # Extended augmentation
├── feature_selection/# Feature selection
├── clustering/       # Clustering algorithms
├── timeseries/       # Time series models
├── timeseries_forecast/# Forecasting
└── preprocessing/   # Preprocessing

Optimization & Training

├── advanced_optim/   # Advanced optimizers
├── training/         # Training utilities
├── training_utils/   # Training helpers
├── distributed/     # Distributed training
├── gradients/       # Gradient handling
├── loss/            # Loss functions
├── schedulers/       # Learning rate schedulers
└── regularization/  # Regularization

Model Compression

├── quantization/    # Model quantization
├── pruning/         # Network pruning
├── distillation/    # Knowledge distillation
├── compression/     # Model compression
└── model_compression/# Compression utilities

Domain-Specific

├── bioinformatics/   # Bioinformatics
├── bio/             # Biological data
├── finance/         # Financial models
├── finance_advanced/# Advanced finance
├── climate/          # Climate modeling
├── climate_weather/ # Weather forecasting
├── healthcare/      # Healthcare ML
├── robotics/        # Robotics
├── recommendation/ # Recommender systems
└── anomaly_detection/# Anomaly detection

Security & Privacy

├── privacy/         # Privacy-preserving ML
├── privacy_advanced/# Advanced privacy
├── adversarial/     # Adversarial robustness
├── robustness/      # Model robustness
├── fairness/        # Fairness in ML
└── security/        # Security utilities

Interpretability & Explainability

├── explainability/  # Model explainability
├── explain/        # Interpretability
├── visualization/  # Visualization tools
└── attribution/    # Attribution methods

Infrastructure

├── experiments/     # Experiment tracking
├── logging/        # Logging utilities
├── monitoring/     # Model monitoring
├── profiling/      # Performance profiling
├── serving/        # Model serving
├── deployment/     # Deployment utilities
├── api/            # API utilities
├── database/       # Database integration
├── cache/          # Caching
└── workflow/       # Workflow orchestration

Testing

Run the test suite:

# Core framework tests
python test_all.py

# Advanced features tests
python test_advanced.py

# Run all tests
python -m pytest test_all.py test_advanced.py -v

Test Coverage

Core Framework: 13/13 tests passed
Advanced Features: 6/6 tests passed
Total: 19/19 tests passed ✅

Performance

All frameworks tested on synthetic data:

Framework	Parameters	Forward Pass	Framework	Parameters	Forward Pass
UniIntelli	1.8M	✓	UIF-I	1.4M	✓
HSCA	6.5M	✓	UIS-J	2.3M	✓
UIA	1.7M	✓	UIA-K	1.4M	✓
SCIF	3.8M	✓	CRLS-L	375K	✓
UIF	367K	✓	UIA-M	1.2M	✓
UIS	861K	✓	UIS-N	4.1M	✓
CRLS	958K	✓	UIA-O	822K	✓
ToposFormer	4.8M	✓	UIF-P	123K	✓
UINet-Q	2.0M	✓	USIF-S	1.5M	✓
UIF-R	291K	✓	UIF-T	330K	✓
USIF-U	910K	✓	UIF-V	247K	✓
MCA-W	1.1M	✓	TTSIK-X	864K	✓
CTNA-Y	641K	✓	SCIF-Z	475K	✓

Total: 26 frameworks, ~27M parameters across all base configurations

Documentation

For detailed mathematical documentation, see:

A.md - UniIntelli: Categorical–Geometric–Thermodynamic Synthesis
B.md - HSCA: Holo-Symplectic Cognitive Architecture
C.md - UIA: Unified Intelligence Architecture
D.md - SCIF: Symplectic-Categorical Intelligence Framework
E.md - UIF: Unified Intelligence Framework
F.md - UIS: Unified Intelligence Synthesis
G.md - CRLS: Categorical Renormalization Learning Systems
H.md - ToposFormer: Topos-Theoretic Neural Networks
I.md - UIF-I: Renormalized Attention Module
J.md - UIS-J: Node-at-Attention Mechanism
K.md - UIA-K: Sheaf-LSTM with Fiber Bundle Attention
L.md - CRLS-L: Mathematical Intelligence Physics
M.md - UIA-M: Renormalized Neural Flow
N.md - UIS-N: Cross-Synthetic Node-at-Attention
O.md - UIA-O: Sheaf-Theoretic Neural Networks
P.md - UIF-P: RG-Informed Hierarchical Networks
Q.md - UINet-Q: Categorical Quantum Neural Architecture
R.md - UIF-R: Comprehensive Blueprint with Fisher Natural Gradient
S.md - USIF-S: Quantum Categorical Neural Networks
T.md - UIF-T: Hamiltonian-RG Flow Optimizer
U.md - USIF-U: Thermodynamic Information Bounds
V.md - UIF-V: Information-Theoretic Dynamics
W.md - MCA-W: Meta-Cognitive Architecture
X.md - TTSIK-X: Topos-Theoretic Symplectic Intelligence Kernel
Y.md - CTNA-Y: Categorical-Thermodynamic Neural Architecture
Z.md - SCIF-Z: Symplectic-Categorical Intelligence Framework

Mathematical Foundations

Key Theorems Implemented

Natural Gradient = Geodesic Flow: Theorem 2.1 - Natural gradient descent is the time-η flow of the gradient vector field w.r.t. Levi-Civita connection
SOA Conserves Sheaf Cohomology: Theorem 4.1 - Sheaf-Optimized Attention preserves δ¹(s) = 0 up to O(η²)
TGF Convergence: Theorem 5.1 - Thermodynamic Gradient Flow converges under non-equilibrium fluctuations
No-Cloning in Learn: Lemma 2.1 - No morphism Δ: A → A ⊗ A exists in the category of learning processes

Contributing

We welcome contributions! Areas of interest:

Implementing higher sheaf cohomology (H²) for mode connectivity
Quantum-categorical neural networks
Real-time formal verification with neural theorem proving
Unified scaling laws from RG fixed points

Please read CONTRIBUTING.md for guidelines.

Citation

If you use this framework in your research, please cite:

@software{fishstick_2026,
  title={fishstick: A Mathematically Rigorous AI Framework},
  author={[Your Name]},
  url={https://github.com/NeuralBlitz/fishstick},
  year={2026}
}

License

This project is licensed under the MIT License - see LICENSE file.

Acknowledgments

This framework synthesizes insights from:

Statistical mechanics and thermodynamics
Differential geometry and information geometry
Category theory and type theory
Symplectic geometry and Hamiltonian mechanics
Sheaf theory and algebraic topology

The era of black-box AI is ending. The era of principled intelligence begins now.

Name		Name	Last commit message	Last commit date
Latest commit History 70 Commits
.opencode/skills		.opencode/skills
demo_experiments		demo_experiments
docs		docs
examples		examples
fishstick.egg-info		fishstick.egg-info
fishstick		fishstick
test_output		test_output
.replit		.replit
INSTALLED_MODELS.md		INSTALLED_MODELS.md
LLM_GUIDE.md		LLM_GUIDE.md
README.md		README.md
SECURITY.md		SECURITY.md
SHOWCASE.md		SHOWCASE.md
TODO_AUGMENTATION_EXT.md		TODO_AUGMENTATION_EXT.md
TODO_NLP_EXTENSIONS.md		TODO_NLP_EXTENSIONS.md
TODO_QA_IMPLEMENTATION.md		TODO_QA_IMPLEMENTATION.md
TODO_QUESTION_ANSWERING.md		TODO_QUESTION_ANSWERING.md
TODO_SCENE_UNDERSTANDING.md		TODO_SCENE_UNDERSTANDING.md
TODO_VOICE_CONVERSION.md		TODO_VOICE_CONVERSION.md
TODO_audio_separation.md		TODO_audio_separation.md
TODO_data_processing.md		TODO_data_processing.md
TODO_neural_operators.md		TODO_neural_operators.md
UPGRADE_SUMMARY.md		UPGRADE_SUMMARY.md
_3D_VISION.md		_3D_VISION.md
_AUDIO_SEPARATION.md		_AUDIO_SEPARATION.md
_BAYESIAN_DL.md		_BAYESIAN_DL.md
_COMPRESSION_TOOLS.md		_COMPRESSION_TOOLS.md
_DEPLOYMENT_EXT.md		_DEPLOYMENT_EXT.md
_FEDERATED_EXT.md		_FEDERATED_EXT.md
_GAMETHEORY.md		_GAMETHEORY.md
_GEOMETRIC_DL.md		_GEOMETRIC_DL.md
_HARDWARE_ACCEL.md		_HARDWARE_ACCEL.md
_HYPEROPT_EXT.md		_HYPEROPT_EXT.md
_INVERSE_PROBLEMS.md		_INVERSE_PROBLEMS.md
_KNOWLEDGE_GRAPH.md		_KNOWLEDGE_GRAPH.md
_LOGIC.md		_LOGIC.md
_METRIC_LEARNING.md		_METRIC_LEARNING.md
_MULTIMODAL_EXT.md		_MULTIMODAL_EXT.md
_NN_COMPONENTS.md		_NN_COMPONENTS.md
_OBJECT_DETECTION.md		_OBJECT_DETECTION.md
_PRIVACY.md		_PRIVACY.md
_SCENE_UNDERSTANDING_V2.md		_SCENE_UNDERSTANDING_V2.md
_SSL_EXTENSIONS.md		_SSL_EXTENSIONS.md
_TIMESERIES_FORECAST.md		_TIMESERIES_FORECAST.md
_UNCERTAINTY_EXT.md		_UNCERTAINTY_EXT.md
_XAI.md		_XAI.md
_fewshot_learning.md		_fewshot_learning.md
a.out		a.out
bun.lock		bun.lock
download_models.py		download_models.py
image.png		image.png
index.js		index.js
llm_demo.py		llm_demo.py
model_browser.py		model_browser.py
package-lock.json		package-lock.json
package.json		package.json
replit.md		replit.md
requirements.txt		requirements.txt
setup.py		setup.py
stop		stop
test_advanced.py		test_advanced.py
test_all.py		test_all.py
typescript		typescript

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FishStick..🐠🪾

IM ONLY KIDDING

FishStick.. 🐠🪾🚀

Overview

Core Philosophy

Installation

Basic Installation

Full Installation (with all features)

Dependencies

Quick Start

1. Core Types and Manifolds

2. Categorical Structures

3. Hamiltonian Neural Networks

4. Sheaf-Optimized Attention

The 26 Frameworks (A-Z)

A. UniIntelli - Categorical–Geometric–Thermodynamic Synthesis

B. HSCA - Holo-Symplectic Cognitive Architecture

C. UIA - Unified Intelligence Architecture

D. SCIF - Symplectic-Categorical Intelligence Framework

E. UIF - Unified Intelligence Framework

F. UIS - Unified Intelligence Synthesis

G. CRLS - Categorical Renormalization Learning Systems

H. ToposFormer - Topos-Theoretic Neural Networks

I. UIF-I - Renormalized Attention Module (RAM)

J. UIS-J - Node-at-Attention Mechanism

K. UIA-K - Sheaf-LSTM with Fiber Bundle Attention

L. CRLS-L - Mathematical Intelligence Physics

M. UIA-M - Renormalized Neural Flow

N. UIS-N - Cross-Synthetic Node-at-Attention

O. UIA-O - Sheaf-Theoretic Neural Networks

P. UIF-P - RG-Informed Hierarchical Networks

Q. UINet-Q - Categorical Quantum Neural Architecture

R. UIF-R - Comprehensive Blueprint with Fisher Natural Gradient

S. USIF-S - Quantum Categorical Neural Networks (QCNN)

T. UIF-T - Hamiltonian-RG Flow Optimizer

U. USIF-U - Thermodynamic Information Bounds

V. UIF-V - Information-Theoretic Dynamics

W. MCA-W - Meta-Cognitive Architecture

X. TTSIK-X - Topos-Theoretic Symplectic Intelligence Kernel

Y. CTNA-Y - Categorical-Thermodynamic Neural Architecture

Z. SCIF-Z - Symplectic-Categorical Intelligence Framework

Advanced Features

Neural ODEs

Geometric Graph Neural Networks

Probabilistic / Bayesian Neural Networks

Normalizing Flows

Equivariant Networks

Causal Inference

Diffusion Models

Reinforcement Learning

Federated Learning

Continual Learning

Time Series Forecasting

Audio & Speech Processing

Privacy-Preserving ML

Model Compression

Anomaly Detection

Architecture

Core & Theory

Frameworks (26 A-Z)

Deep Learning

Probabilistic & Bayesian

Causal & Logic

Learning Paradigms

Multi-Modality

Data & Processing

Optimization & Training

Model Compression

Domain-Specific

Security & Privacy

Interpretability & Explainability

Infrastructure

Testing

Test Coverage

Performance

Documentation

Packages