✦ Frontier Research Laboratory ✦
HOOSHA AI
Subquadratic Sequence Models • Spectral SVD • State-Space Hybrids
Eliminating quadratic bottlenecks 𝒪(N²) via exact subspace factorization, continuous differential recurrence, and curvature-aware neural compression.
236
Attention Mechanisms
107
Compression Engines
𝒪(1)
Step Latency
1.40x
Mean Compression
🔬 Theoretical Foundations & Research Disciplines
| Paradigm | Mathematical & Algorithmic Formulation | Complexity / Guarantee |
|---|---|---|
| ⚡ Subquadratic Attention | Factorized singular projections, positive feature kernel maps, and parallel associative prefix scans replacing dense Q K⊤. | 𝒪(N · r) |
| 🌀 State-Space Models (SSMs) | Continuous-time differential recurrence with input-dependent parametric selectivity: ht = At ht−1 + Bt xt. | Train: 𝒪(N) • Step: 𝒪(1) |
| 🧩 Spectral SVD Surgery |
Zero-shot rank reduction governed by cumulative singular energy preservation: (∑i=1..r σi2) / (∑j=1..d σj2) ≥ τ |
r ≪ min(din, dout) |
| ✂️ Structured Pruning | Curvature-aware empirical Fisher pruning yielding 2:4 semi-structured tensor patterns for direct hardware execution. | Tensor-Core Accelerated |
📊 Architectural Paradigm & Scientific Quality Gate
1. Quadratic vs Subquadratic Paradigm
Standard Attention: Softmax(QKᵀ)
𝒪(N²) Memory Wall • Unbounded KV-Cache Growth
𝒪(N²) Memory Wall • Unbounded KV-Cache Growth
Hoosha AI Subquadratic Paradigm
SVD + SSMs + Feature Kernels • 𝒪(N) Training • 𝒪(1) Step Latency
SVD + SSMs + Feature Kernels • 𝒪(N) Training • 𝒪(1) Step Latency
2. NeuralOps Quality Gate
Statistical Verification Criteria
• Student's t-test (p < 0.05) • Accuracy ≥ 56.00% • Cosine Sim ≥ 0.98
• Student's t-test (p < 0.05) • Accuracy ≥ 56.00% • Cosine Sim ≥ 0.98
PASS → Hub Release
FAIL → Auto Purge
🚀 Quick Start Model Verification
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load certified subquadratic model from Hoosha AI
repo_id = "Hooshaai/svd-linear-attention-roberta-dobi-svd"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
inputs = tokenizer("Subquadratic sequence modeling with spectral guarantees.", return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
print("Inference verified successfully. Logits shape:", logits.shape)