🏛️ QU-SSM-60M-MoE: Continuous Quasi-Unitary State Space Model with Sparse Mixture-of-Experts

📄 Official Research Paper (PDF) & Open Verification

Title: "Gated Quasi-Unitary Lie-Algebra Recurrent State Space Models"
Author: Prannessh K.V.A. (@prannesshkva)

📥 Download Full Research Paper (PDF) | 🏛️ Zenodo DOI: 10.5281/zenodo.22283431 | 🎮 Live Interactive Space

QU-SSM-60M-MoE is the mid-tier foundation model of the QU-SSM family designed and invented by Prannessh K.V.A. (Sole Architect & Inventor). It combines continuous Lie-group unitary recurrence on SO(2) ≅ U(1) with 4 SwiGLU Mixture-of-Experts (MoE) and Top-2 routing (44.64M active parameters per token).

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🧬 Base Model Lineage & Technical Notes

• Core Architecture: Continuous Quasi-Unitary State Space Model (SO(2) phase rotations) coupled with 4 SwiGLU Mixture-of-Experts and Top-2 routing.
• Tokenizer Lineage: Standard GPT-2 Byte-Pair Encoding (BPE) vocabulary (50,257 tokens).
• Pre-training & Calibration: Pre-trained on roneneldan/TinyStories (~20M+ tokens) demonstrating sub-millisecond step latency and exact norm preservation.
• Parameter Footprint: 64.30M Total Parameters, 44.64M Active Parameters per token.
• Inference Efficiency: Constant O(1) inference state RAM (0.19 MB) regardless of sequence length.
• Official Research Contact: LinkedIn — Prannesh K. V. A.

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🔍 What is QU-SSM?

QU-SSM is a linear-time continuous sequence engine that replaces the monotonic dissipative decay of classical state space models with non-dissipative SO(2) unitary phase rotations (‖R(θ)‖₂ ≡ 1.00000), delivering strictly constant O(1) inference memory and sub-millisecond step latency.

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📊 Architecture Specifications

Specification	Value
Model Name	QU-SSM-60M-MoE
Sole Architect & Inventor	Prannessh K.V.A.
Total Parameters	64.30M
Active Parameters / Token	44.64M (Top-2 Sparse MoE)
Hidden Dimension (D)	384
Layers	6
SSM State Dimension	8
Expert Count	4 SwiGLU Experts
Vocabulary	50,257 (GPT-2 BPE)
Inference State RAM	0.19 MB (Constant O(1))

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🔒 Intellectual Property & Citation

• Sole Architect & Inventor: Prannessh K.V.A.
• Official Research DOI: 10.5281/zenodo.22283431
• License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Official Contact: LinkedIn — Prannesh K. V. A.

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🔗 Related QU-SSM Hub Repositories

• 🚀 QU-SSM-130M-MoE (134.89M Flagship Model)
• ⚡ QU-SSM-60M-MoE (64.30M Mid-Tier Model)
• 🔬 QU-SSM-15M (29.80M Foundation Model)
• ⚛️ Liquid QU-SSM-130M (Continuous-Time LNN)
• 🎮 QU-SSM Scientific Benchmark Suite