mobiu-q 3.6.2

Soft Algebra Optimizer + O(N) Linear Attention for Long Context LLMs

Mobiu-Q v3.6.2

Soft Algebra for Optimization & Attention

---

Overview

Mobiu-Q is a framework built on Soft Algebra (nilpotent ε²=0) that provides:

1. Mobiu 🆕 - Simple API that auto-detects everything (like Adam!)
2. MobiuOptimizer - Manual configuration for advanced users
3. MobiuAttention 🧪 - O(N) linear attention for long sequences

---

Installation

pip install mobiu-q

---

Quick Start

🆕 Simple API (Recommended)

from mobiu_q import Mobiu

# Just like Adam - no configuration needed!
model = MyModel()
opt = Mobiu(model.parameters(), lr=0.001)

for batch in dataloader:
    loss = criterion(model(batch))
    opt.zero_grad()
    loss.backward()
    opt.step(loss.item())  # That's it!

Mobiu automatically:

• Detects if you're maximizing (reward) or minimizing (loss)
• Selects the best optimization strategy
• Adapts learning rate via Cloud Soft Algebra

Benchmarks with Simple API:

Benchmark	Improvement	Win Rate
Loss Minimization	+54.3%	100%
Noisy Labels	+50.8%	100%
VQE H₂	+35.8%	100%

MobiuOptimizer (Advanced)

from mobiu_q import MobiuOptimizer
import torch

# Your license key (get one at https://app.mobiu.ai)
LICENSE_KEY = "your-license-key-here"

# Wrap any PyTorch optimizer
model = MyModel()
base_opt = torch.optim.Adam(model.parameters(), lr=0.0003)
opt = MobiuOptimizer(
    base_opt,
    license_key=LICENSE_KEY,
    method="adaptive",
    use_soft_algebra=True
)

for batch in dataloader:
    loss = criterion(model(batch))
    loss.backward()
    opt.step(loss.item())  # Pass loss for Soft Algebra

opt.end()  # Important: release resources

Monitoring Training

opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive")
# ... training ...

# Track metrics
print(opt.lr_history)    # Learning rates over time
print(opt.warp_history)  # Gradient warp factors (new in v3.1.3)

MobiuAttention (🧪 Experimental)

from mobiu_q.experimental import MobiuAttention, MobiuBlock

# Drop-in replacement for nn.MultiheadAttention
# Note: MobiuAttention runs locally, no license key needed!
attn = MobiuAttention(d_model=512, num_heads=8)
out = attn(x)  # x: [batch, seq, dim]

# Or use complete block
block = MobiuBlock(d_model=512, num_heads=8)
out = block(x)

---

License Key

MobiuOptimizer requires a license key to access the cloud API:

from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# PyTorch mode (pass optimizer)
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive")

# Quantum/NumPy mode (pass params array)
opt = MobiuOptimizer(params, license_key=LICENSE_KEY, method="standard")

Get your key: https://app.mobiu.ai

Tier	API Calls	Price
Free	20/month	$0
Pro	Unlimited	$19/month

Note: MobiuAttention runs locally and does NOT require a license key.

---

MobiuOptimizer

Methods

Method	Use Case	Default LR
standard	Smooth landscapes, chemistry, physics	0.01
deep	Deep circuits, noisy hardware, complex opt	0.1
adaptive	RL, LLM fine-tuning, high-variance problems	0.0003

Benchmarks

Reinforcement Learning & Trading

Domain	Improvement	Win Rate	p-value
Crypto Trading	+56% profit	100%	<0.001
LunarLander-v3	+128%	97%	<0.001
MuJoCo InvertedPendulum	+111%	100%	<0.001

Quantum Computing

Domain	Improvement	Win Rate	p-value
VQE H₂ (FakeFez)	+52%	100%	<0.001
QAOA MaxCut	+45%	95%	<0.001

Noisy & Distributed Learning 🆕

These domains have systematic gradient bias - exactly where Soft Algebra excels:

Domain	Improvement	Win Rate	p-value	Bias Source
Federated Learning	+67%	100%	<0.001	Non-IID client data
Imbalanced Data	+52%	100%	<0.001	Majority class dominates
Sim-to-Real	+47%	100%	<0.001	Simulator ≠ reality
Noisy Labels	+40%	100%	<0.001	Systematic mislabeling

All tests: 10 seeds, same energy & gradient for both, only use_soft_algebra differs

Why Soft Algebra Works Here

In these domains, the gradient is systematically biased:

• Federated: Each client sees different data distribution
• Imbalanced: Gradient dominated by majority class
• Sim-to-Real: Simulator has wrong physics parameters
• Noisy Labels: Labels consistently confused (e.g., 3↔8)

Soft Algebra detects the gap between gradient direction and actual loss improvement, then corrects for it.

Maximize vs Minimize

By default, Mobiu-Q assumes you're minimizing (loss, energy). For RL/Trading where you maximize (reward, profit), set maximize=True:

LICENSE_KEY = "your-license-key-here"

# Loss minimization (default) - for supervised learning, VQE
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive")
opt.step(loss.item())

# Reward maximization - for RL, trading
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive", maximize=True)
opt.step(episode_return)

Use Case	maximize=	Example
Supervised Learning	False (default)	opt.step(loss.item())
VQE / QAOA	False (default)	opt.step(energy)
RL (policy gradient)	True	opt.step(episode_return)
Trading	True	opt.step(profit)

Why does this matter? Soft Algebra tracks the "direction of improvement". Using the wrong setting confuses the optimizer.

A/B Testing

LICENSE_KEY = "your-license-key-here"

# Test with Soft Algebra
opt_on = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, use_soft_algebra=True)

# Test without (baseline)
opt_off = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, use_soft_algebra=False)

---

Examples by Domain

Federated Learning 🆕

import numpy as np
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# Simulate federated aggregation with non-IID clients
class FederatedTrainer:
    def __init__(self, n_clients=10, non_iid_strength=0.5):
        self.n_clients = n_clients
        self.non_iid = non_iid_strength
        # Each client has biased local data
        self.client_biases = [np.random.randn(dim) * non_iid_strength 
                             for _ in range(n_clients)]
    
    def aggregate_gradients(self, params, sampled_clients):
        """Aggregate gradients from subset of clients (FedAvg style)"""
        grads = []
        for c in sampled_clients:
            # Each client's gradient is biased by their local data
            local_grad = compute_gradient(params) + self.client_biases[c]
            grads.append(local_grad)
        return np.mean(grads, axis=0)

# Mobiu-Q handles the systematic bias from non-IID aggregation
params = np.random.randn(100)
opt = MobiuOptimizer(
    params,
    license_key=LICENSE_KEY,
    method="standard",
    base_lr=0.01
)

for round in range(100):
    # Sample random clients (realistic FL scenario)
    clients = np.random.choice(n_clients, size=5, replace=False)
    gradient = trainer.aggregate_gradients(params, clients)
    loss = compute_global_loss(params)
    
    params = opt.step(params, gradient, loss)

opt.end()

Imbalanced Data Classification 🆕

import torch
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# Dataset with 90% class 0, 10% class 1 (fraud detection, medical diagnosis)
train_loader = create_imbalanced_loader(imbalance_ratio=0.9)

model = FraudDetector()
base_opt = torch.optim.Adam(model.parameters(), lr=0.001)
opt = MobiuOptimizer(
    base_opt,
    license_key=LICENSE_KEY,
    method="standard"
)

for batch in train_loader:
    # Gradient dominated by majority class
    loss = criterion(model(batch))
    loss.backward()
    
    # Soft Algebra corrects for class imbalance bias
    opt.step(loss.item())

opt.end()

Sim-to-Real Robotics 🆕

import torch
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# Policy trained in simulator, deployed in real world
policy = RobotPolicy()
base_opt = torch.optim.Adam(policy.parameters(), lr=0.0003)
opt = MobiuOptimizer(
    base_opt,
    license_key=LICENSE_KEY,
    method="adaptive",
    maximize=True
)

for episode in range(1000):
    # Gradient from SIMULATOR (biased - wrong friction, mass, etc.)
    sim_loss = run_simulator_episode(policy)
    sim_loss.backward()
    
    # Periodically evaluate in REAL environment
    if episode % 10 == 0:
        real_reward = run_real_episode(policy)
    
    # Soft Algebra uses real reward to correct simulator bias
    opt.step(real_reward)

opt.end()

Noisy Labels 🆕

import torch
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# Dataset with systematic label noise (crowdsourced, OCR errors)
# e.g., "3" often mislabeled as "8", "cat" confused with "dog"
train_loader = create_noisy_label_loader(noise_rate=0.3)

model = Classifier()
base_opt = torch.optim.Adam(model.parameters(), lr=0.001)
opt = MobiuOptimizer(
    base_opt,
    license_key=LICENSE_KEY,
    method="standard"
)

for batch_x, noisy_labels in train_loader:
    # Gradient points toward WRONG targets due to label noise
    loss = criterion(model(batch_x), noisy_labels)
    loss.backward()
    
    # Validate on clean held-out set
    clean_loss = evaluate_clean(model)
    
    # Soft Algebra detects mismatch and corrects
    opt.step(clean_loss)

opt.end()

Reinforcement Learning (REINFORCE)

import torch
import torch.nn.functional as F
import gymnasium as gym
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# Simple policy network
policy = torch.nn.Sequential(
    torch.nn.Linear(8, 64), torch.nn.Tanh(),
    torch.nn.Linear(64, 64), torch.nn.Tanh(),
    torch.nn.Linear(64, 4)
)

# Wrap optimizer with maximize=True for RL
base_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
opt = MobiuOptimizer(
    base_opt,
    license_key=LICENSE_KEY,
    method="adaptive",
    maximize=True,       # Important: RL maximizes reward!
    sync_interval=50,    # Sync with cloud every 50 steps
    verbose=True
)

env = gym.make("LunarLander-v3")

for episode in range(1000):
    state, _ = env.reset()
    log_probs, rewards = [], []
    
    # Collect episode
    done = False
    while not done:
        logits = policy(torch.FloatTensor(state))
        dist = torch.distributions.Categorical(logits=logits)
        action = dist.sample()
        log_probs.append(dist.log_prob(action))
        state, reward, terminated, truncated, _ = env.step(action.item())
        rewards.append(reward)
        done = terminated or truncated
    
    # REINFORCE update
    returns = []
    G = 0
    for r in reversed(rewards):
        G = r + 0.99 * G
        returns.insert(0, G)
    returns = torch.tensor(returns)
    returns = (returns - returns.mean()) / (returns.std() + 1e-8)
    
    loss = sum(-lp * G for lp, G in zip(log_probs, returns))
    
    opt.zero_grad()
    loss.backward()
    opt.step(sum(rewards))  # Pass episode return for Soft Algebra

opt.end()

Quantum Chemistry (VQE with Qiskit)

import numpy as np
from qiskit.circuit.library import EfficientSU2
from qiskit.quantum_info import SparsePauliOp
from qiskit_aer import AerSimulator
from qiskit.primitives import BackendEstimatorV2
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# H₂ Hamiltonian
hamiltonian = SparsePauliOp.from_list([
    ("II", -0.4804), ("ZZ", 0.3435), ("ZI", -0.4347),
    ("IZ", 0.5716), ("XX", 0.0910), ("YY", 0.0910)
])

# Setup
backend = AerSimulator()
estimator = BackendEstimatorV2(backend=backend)
estimator.options.default_shots = 4096

ansatz = EfficientSU2(2, reps=2, entanglement="linear")
params = np.random.uniform(-0.3, 0.3, ansatz.num_parameters)

# Optimizer (NumPy mode - auto-delegates to MobiuQCore)
opt = MobiuOptimizer(
    params,
    license_key=LICENSE_KEY,
    method="standard",
    mode="hardware",        # Use hardware mode for noisy backends
    use_soft_algebra=True
)

# VQE loop with SPSA gradient
for step in range(100):
    # SPSA gradient estimation (2 circuit evaluations)
    delta = np.random.choice([-1, 1], size=len(params))
    shift = 0.1
    
    job = estimator.run([
        (ansatz, hamiltonian, params),
        (ansatz, hamiltonian, params + shift * delta),
        (ansatz, hamiltonian, params - shift * delta)
    ])
    results = job.result()
    
    energy = float(results[0].data.evs)
    grad = (float(results[1].data.evs) - float(results[2].data.evs)) / (2 * shift) * delta
    
    # Update params via Mobiu-Q
    params = opt.step(params, grad, energy)
    
    if step % 20 == 0:
        print(f"Step {step}: energy = {energy:.4f}")

opt.end()
print(f"Final energy: {energy:.4f}")  # Should approach -1.85

Combinatorial Optimization (QAOA)

import numpy as np
from qiskit import QuantumCircuit
from qiskit_aer import AerSimulator
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# MaxCut graph
edges = [(0, 1), (1, 2), (2, 3), (3, 0), (0, 2)]
n_qubits = 4
p = 2  # QAOA layers

def qaoa_circuit(params):
    gammas, betas = params[:p], params[p:]
    qc = QuantumCircuit(n_qubits)
    qc.h(range(n_qubits))
    for layer in range(p):
        for i, j in edges:
            qc.rzz(2 * gammas[layer], i, j)
        for i in range(n_qubits):
            qc.rx(2 * betas[layer], i)
    qc.measure_all()
    return qc

def evaluate(params, shots=1024):
    qc = qaoa_circuit(params)
    counts = AerSimulator().run(qc, shots=shots).result().get_counts()
    cost = 0
    for bitstring, count in counts.items():
        for i, j in edges:
            if bitstring[-(i+1)] != bitstring[-(j+1)]:
                cost += count
    return -cost / shots  # Negative for minimization

# Optimizer
params = np.random.uniform(-np.pi, np.pi, 2 * p)
opt = MobiuOptimizer(
    params,
    license_key=LICENSE_KEY,
    method="deep",
    mode="simulation"
)

for step in range(100):
    # SPSA gradient
    delta = np.random.choice([-1, 1], size=len(params))
    shift = 0.1
    e_plus = evaluate(params + shift * delta)
    e_minus = evaluate(params - shift * delta)
    energy = evaluate(params)
    grad = (e_plus - e_minus) / (2 * shift) * delta
    
    params = opt.step(params, grad, energy)
    
    if step % 20 == 0:
        print(f"Step {step}: MaxCut = {-energy:.2f}")

opt.end()
print(f"Final MaxCut value: {-energy:.2f}")

Trading / Finance

import torch
import numpy as np
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# Simple trading policy: state → action probabilities
policy = torch.nn.Sequential(
    torch.nn.Linear(20, 64), torch.nn.ReLU(),
    torch.nn.Linear(64, 32), torch.nn.ReLU(),
    torch.nn.Linear(32, 3)  # Hold, Buy, Sell
)

base_opt = torch.optim.Adam(policy.parameters(), lr=3e-4)
opt = MobiuOptimizer(
    base_opt,
    license_key=LICENSE_KEY,
    method="adaptive",
    maximize=True,       # Maximize profit!
    sync_interval=50,
    verbose=True
)

# Training loop
for episode in range(500):
    state = get_market_state()  # Your market data
    log_probs, rewards = [], []
    
    for step in range(episode_length):
        logits = policy(torch.FloatTensor(state))
        dist = torch.distributions.Categorical(logits=logits)
        action = dist.sample()
        log_probs.append(dist.log_prob(action))
        
        state, reward = execute_trade(action.item())  # Your trading logic
        rewards.append(reward)
    
    # Policy gradient update
    returns = compute_returns(rewards, gamma=0.99)
    loss = sum(-lp * G for lp, G in zip(log_probs, returns))
    
    opt.zero_grad()
    loss.backward()
    opt.step(sum(rewards))  # Pass episode profit

opt.end()

Stable-Baselines3 (PPO, SAC, etc.)

SB3 calls optimizer.step() internally without arguments. Use set_metric() to provide the reward:

import gymnasium as gym
import numpy as np
from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import BaseCallback
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

class MobiuSB3Callback(BaseCallback):
    """Callback that integrates Mobiu-Q with SB3."""
    
    def __init__(self, method="adaptive", use_soft_algebra=True, verbose=0):
        super().__init__(verbose=verbose)
        self.method = method
        self.use_soft_algebra = use_soft_algebra
        self._mobiu = None
        self._ep_returns = []
    
    def _on_training_start(self):
        base_opt = self.model.policy.optimizer
        self._mobiu = MobiuOptimizer(
            base_opt,
            license_key=LICENSE_KEY,
            method=self.method,
            use_soft_algebra=self.use_soft_algebra,
            maximize=True,
            sync_interval=50,
            verbose=True
        )
        # Replace SB3's optimizer
        self.model.policy.optimizer = self._mobiu
    
    def _on_step(self):
        for info in self.locals.get("infos", []):
            if "episode" in info:
                ep_return = info["episode"]["r"]
                self._ep_returns.append(ep_return)
                # Update metric with rolling average
                recent = self._ep_returns[-4:]
                self._mobiu.set_metric(np.mean(recent))
        return True
    
    def _on_training_end(self):
        if self._mobiu:
            self._mobiu.end()


# Usage
env = gym.make("LunarLander-v3")
model = PPO("MlpPolicy", env, learning_rate=3e-4, verbose=0)
model.learn(total_timesteps=200_000, callback=MobiuSB3Callback())

---

Base Optimizers

Mobiu-Q enhances these base optimizers with Soft Algebra:

Optimizer	Description	Best For
Adam	Adaptive moments, most popular	Default, most cases
AdamW	Adam with decoupled weight decay	LLM, Transformers
NAdam	Adam with Nesterov momentum	Alternative to Adam
AMSGrad	Adam with max(v) for stability	Drug discovery, unstable loss
SGD	Simple gradient descent	QAOA, convex problems
Momentum	SGD with momentum	RL, LLM fine-tuning
LAMB	Layer-wise adaptive scaling	Large batch training

Choosing an Optimizer

PyTorch mode - Choose your optimizer when creating the base optimizer:

import torch
from mobiu_q import MobiuOptimizer

LICENSE_KEY = "your-license-key-here"

# Using Adam (default, recommended for most cases)
base_opt = torch.optim.Adam(model.parameters(), lr=0.0003)
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive")

# Using AdamW (recommended for LLM/Transformers)
base_opt = torch.optim.AdamW(model.parameters(), lr=0.0003, weight_decay=0.01)
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive")

# Using SGD with Momentum (recommended for RL)
base_opt = torch.optim.SGD(model.parameters(), lr=0.02, momentum=0.9)
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive", maximize=True)

# Using NAdam
base_opt = torch.optim.NAdam(model.parameters(), lr=0.0003)
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="deep")

Quantum mode - Choose your optimizer via the base_optimizer parameter:

from mobiu_q import MobiuOptimizer
import numpy as np

LICENSE_KEY = "your-license-key-here"
params = np.random.randn(10)

# Using Adam (default)
opt = MobiuOptimizer(params, license_key=LICENSE_KEY, method="standard")

# Using NAdam
opt = MobiuOptimizer(params, license_key=LICENSE_KEY, method="standard", base_optimizer="NAdam")

# Using AMSGrad
opt = MobiuOptimizer(params, license_key=LICENSE_KEY, method="deep", base_optimizer="AMSGrad")

⚠️ Important: Optimizer names are case-sensitive!

# ✅ Correct
opt = MobiuOptimizer(params, license_key=LICENSE_KEY, base_optimizer="NAdam")

# ❌ Wrong - will fall back to Adam
opt = MobiuOptimizer(params, license_key=LICENSE_KEY, base_optimizer="nadam")

---

🛠️ Troubleshooting

If optimization is not improving or diverging, try these adjustments:

1. Switch Base Optimizer

Different optimizers work better for different problems:

Problem Type	Recommended Optimizer
LoRA / LLM	Momentum or AdamW
VQE / Chemistry	Adam
QAOA	NAdam
RL / Trading	Momentum
Drug Discovery	AMSGrad
Large Batch	LAMB
Federated Learning	Adam
Imbalanced Data	Adam
Sim-to-Real	Adam + adaptive
Noisy Labels	Adam

LICENSE_KEY = "your-license-key-here"

# PyTorch: If Adam isn't working, try Momentum:
base_opt = torch.optim.SGD(model.parameters(), lr=0.02, momentum=0.9)
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive")

# Quantum: If Adam isn't working, try NAdam:
opt = MobiuOptimizer(params, license_key=LICENSE_KEY, base_optimizer="NAdam", method="adaptive")

2. Switch Method

If This Fails	Try This
standard	adaptive
adaptive	deep
deep	standard

# If standard isn't working for your problem:
opt = MobiuOptimizer(base_opt, license_key=LICENSE_KEY, method="adaptive")

3. Switch Mode (Quantum only)

If This Fails	Try This
simulation	hardware

opt = MobiuOptimizer(params, license_key=LICENSE_KEY, method="standard", mode="hardware")

4. Adjust Learning Rate

# Try lower LR if diverging
base_opt = torch.optim.Adam(model.parameters(), lr=0.0001)

# Try higher LR if stuck
base_opt = torch.optim.Adam(model.parameters(), lr=0.001)

5. Common Fixes by Domain

Domain	Common Issue	Fix
LoRA	SGD + high LR diverges	Use Momentum + LR=0.02
Drug Discovery	BCE loss unstable	Use AMSGrad + standard method
Crypto/RL	High variance	Use Momentum + adaptive method
QAOA	Local minima	Use NAdam + deep method
Federated	Non-IID variance	Use Adam + standard + LR=0.01
Imbalanced	Majority bias	Use Adam + standard + LR=0.01

---

MobiuAttention 🧪

Why?

Standard Transformer attention is O(N²) in sequence length. MobiuAttention is O(N).

Seq Length	Transformer	MobiuAttention	Speedup
2,048	21s	9s	2.3x
4,096	39s	10s	3.9x
8,192	42s	7s	6.0x
16,384	OOM 💥	5s ✅	∞

Quality (Same as Transformer)

Benchmark	Transformer	MobiuAttention
Shakespeare PPL	12.8	13.5
ListOps Accuracy	81%	82%
Needle-in-Haystack	100%	100%

Usage

from mobiu_q.experimental import MobiuBlock

# No license key needed - runs locally!
class LongContextLM(nn.Module):
    def __init__(self, vocab, d=512, h=8, layers=6):
        super().__init__()
        self.embed = nn.Embedding(vocab, d)
        self.blocks = nn.Sequential(*[MobiuBlock(d, h) for _ in range(layers)])
        self.head = nn.Linear(d, vocab)
    
    def forward(self, x):
        return self.head(self.blocks(self.embed(x)))

# Works with 16K+ tokens!
model = LongContextLM(50000)
x = torch.randint(0, 50000, (1, 16384))
out = model(x)  # No OOM!

⚠️ Experimental Status

• Functional and tested
• API may change in future versions
• Feedback welcome!

---

How It Works

Soft Algebra

Both optimizer and attention use the nilpotent property ε²=0:

SoftNumber multiplication: (a,b) × (c,d) = (ad + bc, bd)

This enables tracking both "potential" and "realized" components.

In Optimization

lr_t = base_lr × (1 + soft_component)

Soft Algebra adapts learning rate based on loss landscape curvature.

In Attention

S(t) = γ·S(t-1) + k_t ⊗ v_t  # O(N) state update

Instead of O(N²) pairwise attention, we track state with O(N) complexity.

---

Full Examples

For complete working examples with benchmarking, see the examples/ folder:

File	Domain	Description
test_lunarlander_hybrid.py	RL	LunarLander with REINFORCE
test_mujoco_maximize.py	RL	MuJoCo continuous control
ppo_mobiu_test.py	RL	PPO from scratch
crypto_trading_benchmark.py	Trading	Crypto with regime switching
test_fakefez_h2.py	VQE	H₂ molecule on FakeFez
test_fakefez_lih.py	VQE	LiH molecule
test_fakefez_qaoa.py	QAOA	MaxCut optimization
test_federated_fair.py	FL	Federated learning benchmark
test_noisy_labels_fair.py	Noisy	Noisy labels benchmark
test_sim_to_real_fair.py	Robotics	Sim-to-real benchmark
test_imbalanced_fair.py	Classification	Imbalanced data benchmark

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License

Tier	API Calls	Price	Get Started
Free	20/month	$0	Sign up
Pro	Unlimited	$19/month	Get one

Note: MobiuAttention runs locally, no API calls required.

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Links

• PyPI
• GitHub

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Citation

@software{mobiu_q,
  title={Mobiu-Q: Soft Algebra for Optimization and Attention},
  author={Mobiu Technologies},
  year={2026},
  url={https://mobiu.ai}
}