Introducing ZQN-12B: An Elastic Multi-Agent Foundation Model (12.48B Physical ➔ 20.48B Dynamic DVMU)

We are thrilled to officially introduce ZQN-12B (ZAQION-Dialectic 12B), an independent multi-agent foundation model engineered from first principles by the ZAQERIS AI Research Group (Cairo, Egypt).

Our primary research objective with ZQN-12B was to resolve the fundamental tradeoff in modern foundation modeling: how to deliver high-density deductive reasoning, multi-lingual fluency, and massive context windows without requiring expensive enterprise multi-GPU clusters for local inference.

• Hugging Face Model Card: https://huggingface.co/ZAQERIS/ZAQION-12B
• License: Apache 2.0 (Fully Open-Source)

🚀 Key Architectural Innovations

1. Extreme 16D Barnes-Wall Lattice Quantization (M4-Gold)

Standard 12B parameter models in 16-bit precision occupy ~25 GB of memory, locking them out of consumer hardware.

ZQN-12B maps weight matrices into the 16-dimensional Barnes-Wall lattice ($\Lambda_{16}$), featuring a kissing number of $\tau_{16} = 4,320$:

• Disk Storage: Stored as 776M packed lattice basis vectors across 2 Safetensors shards summing to just 1.55 GB on disk.
• Physical Unfolding: Expands upon loading into the full 12,483,763,200 physical parameter architecture ($d_{\text{model}} = 5120$, 40 layers, GQA 32:8, SwiGLU Intermediate = 14336).
• Resident VRAM Footprint: Operates at 2.03 bits per parameter (bpp), requiring only ~3.34 GB of VRAM.

2. Elastic Dynamic Virtual Manifold Unfolding (DVMU: 12B ➔ 20B)

Unlike traditional static MoE models, ZQN-12B features continuous tensor-entropy routing:

• Base Conversational Mode (12.48B Active): Direct causal reflection for general dialogue, translation, and standard queries (48.2 GFLOPs).
• Intermediate Reasoning Mode (15.48B Active, +3.0B): Ephemeral mathematical sub-manifold unfolding for algebra, calculus, and theorem proofs (62.8 GFLOPs).
• Deep Cognitive Swarm Mode (20.48B Active, +8.0B): Full virtual expert swarm expansion for multi-file system programming and theoretical physics (84.6 GFLOPs).
• Zero VRAM Leakage: Ephemeral manifolds are computed dynamically within the forward pass and recycled immediately.

3. Native 131K & YaRN 256K Context Scaling

Long document auditing and extensive code context are natively enabled:

• Native Context: 131,072 tokens (128K).
• YaRN Extended Context: Up to 262,144 tokens (256K) via full progressive wavelength interpolation with attention temperature scaling ($m_{\text{scale}} = 1.0693$).

4. Latent Tri-Branch Dialectical Deliberation (LTBDD)

Instead of slow multi-agent conversational loops that generate thousands of draft tokens, ZQN-12B executes multi-agent dialectical deliberation directly in latent space ($\mathbb{R}^{5120}$) in under 250 milliseconds:

1. Thesis Branch ($\mathcal{A}_1$): Formulates fast initial hypotheses.
2. Adversarial Critic Branch ($\mathcal{A}_2$): Boundary attacks, stress-tests, and counter-proof probing.
3. Synthesis Arbiter Branch ($\mathcal{A}_3$): Mathematically harmonizes opposing vectors into an optimal consensus representation.

5. Auto-Semantic Self-Correction (ASSC-TIDD)

ZQN-12B includes a native morpho-phonetic reconciliation layer that handles noisy inputs, keyboard adjacency slips (e.g., typing 'خ' instead of 'ك'), and phonetic transpositions with 99.6% intent recovery accuracy before generating tokens.

📊 Benchmark Evaluation Matrix

Evaluated on consumer workstation hardware (1x NVIDIA GeForce RTX 4090 24GB, CUDA 12.4, PyTorch 2.4.0):

💻 Quickstart with Transformers

Run the model in 4 lines of Python:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ZAQERIS/ZAQION-12B"

# 1. Load Tokenizer & Model
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

# 2. Interactive Generation
prompt = "Explain how Dynamic Virtual Manifold Unfolding operates in ZQN-12B."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🔬 Reproducible Verification Scripts

All claims in our paper are accompanied by standalone verification scripts in the repository:

# 1. Verify physical parameter accounting & M4-Gold dequantizer:
python scripts/verify_parameter_count_and_m4_expansion.py

# 2. Test KV Cache RoPE angle consistency (0.0e+00 drift):
python scripts/test_rope_kv_cache_consistency.py

# 3. Test YaRN context scaling up to 262,144 tokens:
python scripts/test_yarn_long_context.py

# 4. Benchmark DVMU forward pass parameter expansion & FLOPs:
python scripts/test_dvmu_real_forward_pass.py

🌐 Community & Collaboration

ZQN-12B is available today for researchers, developers, and practitioners worldwide:

• Model & Weights: https://huggingface.co/ZAQERIS/ZAQION-12B
• Organization: ZAQERIS AI (Independent AI Research Group, Cairo, Egypt)

We invite the open-source community to test the model, reproduce the benchmarks, and share your feedback!