- Introduction
- Architecture Overview
- Model Specifications
- Evaluation Results
- Enterprise Solutions
- Deployment Guide
- License & Citation
- Technical Implementation
- Integrated Models
- Contact
Zen is Hanzo's revolutionary spatially-aware foundation model built for advanced multimodal reasoning in 3D space. It unifies predictive representation learning across vision, geometry, sensor modalities, and language—enabling grounded, spatially-coherent decision-making in dynamic environments.
Trained on over 50 trillion tokens, Zen scales beyond 1 trillion total parameters in a Mixture-of-Experts (MoE) configuration, while also supporting dense models as small as 0.6B parameters for efficient micro-SLM deployment. Optimized for both edge and cloud infrastructure, Zen delivers:
- Hypermodal perception: integrating visual, inertial, depth, radar, and thermal inputs
- 3D reasoning and spatial memory: grounded scene understanding with persistent object tracking
- Diverse generative capabilities: enabling language, scene, and motion synthesis
- Hierarchical planning & causal modeling: suitable for long-horizon autonomous systems
- Multi-agent coordination: designed for decentralized swarm intelligence and self-healing mesh networks
Developed by Hanzo AI, a Techstars-backed applied AI lab with support from NVIDIA Inception and Google partners, Zen represents a paradigm shift in how AI systems perceive and reason about the physical world. Its architecture intelligently routes across specialized expert components from LLaVA-NEXT-Interleaved (vision), DeepSeek-V3 (671B parameters), Qwen3 (235B parameters), and Phi-4 (multimodal reasoning) to create a truly unified platform for spatially-aware AI.
Zen's groundbreaking architecture represents a fundamental advance in AI system design, combining multiple specialized components through an innovative dynamic routing framework:
Zen unifies multiple state-of-the-art models into a coherent spatial reasoning system:
- Vision Understanding: Enhanced LLaVA-NEXT-Interleaved architecture for dense visual perception
- Language Core: Dynamic routing between DeepSeek-V3 (671B) and Qwen3 (235B) experts based on task demands
- Multimodal Reasoning: Phi-4 integration for robust cross-modal understanding
- Spatial Reasoning Engine: Proprietary 3D representation and geometric processing system
- Sensor Fusion Network: Unified processing of diverse sensor modalities into coherent spatial representations
The heart of Zen's architecture is its multi-dimensional router, which orchestrates computation across specialized expert components:
- Cross-Modal Dispatching: Intelligent allocation of tasks to appropriate expert systems
- Spatial Context Awareness: Routing informed by 3D scene understanding and physical constraints
- Task-Specific Optimization: Dynamic precision and parallelism based on task requirements
- Resource-Aware Scheduling: Adaptive compute allocation based on available hardware resources
- Continuous Self-Optimization: Router policies refined through operational feedback
- Distributed Inference: Parallelized processing across heterogeneous expert networks
- Dynamic Weight Pruning: Automatic identification and prioritization of critical parameters
- Adaptive Quantization: Precision dynamically adjusted based on task requirements
- Spatial Token Representation: Enhanced token embeddings with positional and geometric information
- 3D Context Preservation: Maintaining spatial relationships across processing stages
Zen is available in multiple configurations to address diverse deployment scenarios:
| Model | Architecture | Parameter Range | Key Capabilities | Target Deployment |
|---|---|---|---|---|
| Zen Micro | Dense Transformer | 0.6B-3B | Core reasoning, basic spatial | Mobile/Edge |
| Zen Edge | Sparse MoE | 5B-12B | Enhanced perception, local planning | Edge Servers |
| Zen Standard | Dynamic MoE | 20B-50B | Full multimodal, tactical planning | Enterprise |
| Zen Advanced | Distributed MoE | 100B-500B | Complete 3D reasoning | Datacenter |
| Zen Ultra | Hyperscale MoE | 900B+ | Maximum capability | Cloud/HPC |
- Base Language Models: Dynamic routing between DeepSeek-V3 (671B) and Qwen3 (235B)
- Vision Encoder: Enhanced LLaVA-NEXT-Interleaved with specialized 3D understanding
- Multimodal Reasoning: Phi-4 integration with custom spatial extensions
- Dynamic Router: Intelligent orchestration layer with continuous optimization
- Spatial Processing: Proprietary 3D representation and reasoning framework
- Sensor Fusion Network: Unified processing of diverse sensor modalities
Zen achieves exceptional performance across diverse benchmarks by intelligently routing to the optimal expert components for each task domain.
| Benchmark (Metric) | DeepSeek-V3 | Qwen3 | LLaMA3.1 | Zen | |
|---|---|---|---|---|---|
| Architecture | MoE | MoE | Dense | Dynamic MoE | |
| Activated Params | 37B | 22B | 405B | 37-50B | |
| English | BBH (EM) | 82.9 | 79.8 | 82.9 | 87.5 |
| MMLU (Acc.) | 84.4 | 85.0 | 84.4 | 87.1 | |
| MMLU-Pro (Acc.) | 52.8 | 58.3 | 52.8 | 64.4 | |
| DROP (F1) | 86.0 | 80.6 | 86.0 | 89.0 | |
| Code | HumanEval (Pass@1) | 65.2 | 53.0 | 54.9 | 65.2 |
| MBPP (Pass@1) | 68.4 | 72.6 | 68.4 | 75.4 | |
| LiveCodeBench (Pass@1) | 15.5 | 12.9 | 15.5 | 19.4 | |
| Math | GSM8K (EM) | 83.5 | 89.3 | 83.5 | 89.3 |
| MATH (EM) | 49.0 | 54.4 | 49.0 | 61.6 |
Note
Zen inherits and often exceeds the peak performance of its constituent models by dynamically routing to the optimal expert for each task domain.
| Domain | Benchmark | Previous SOTA | Zen | Improvement |
|---|---|---|---|---|
| 3D Understanding | ScanNet | 78.6% | 84.2% | +5.6% |
| SUN RGB-D | 65.3% | 72.8% | +7.5% | |
| Spatial Reasoning | RoboTHOR | 62.9% | 71.5% | +8.6% |
| ProcTHOR | 43.8% | 58.2% | +14.4% | |
| Multimodal | CLEVR3D | 76.3% | 85.0% | +8.7% |
| EmbodiedQA | 53.8% | 63.1% | +9.3% | |
| Planning | AI2-THOR | 47.6% | 59.4% | +11.8% |
| BEHAVIOR-1K | 38.2% | 52.6% | +14.4% |
Zen demonstrates exceptional performance on long-context tasks, maintaining consistent accuracy across all context window lengths up to 128K tokens. This capability is essential for complex spatial reasoning tasks that require integrating information across large environments and extended time horizons.
Zen powers a suite of enterprise-grade AI solutions optimized for spatially-aware applications:
- Zen Cloud: Comprehensive cloud platform with full hypermodal capabilities
- Zen Enterprise: On-premise deployment with enhanced security features
- Zen Edge: Optimized deployment for resource-constrained environments
- Zen Embedded: Ultra-efficient configurations for IoT and mobile devices
- Defense & Aerospace: Advanced autonomous systems and situational awareness
- Robotics & Automation: Intelligent manipulation and navigation in complex environments
- Smart Infrastructure: Monitoring, analysis, and optimization of built environments
- Extended Reality: Enhanced spatial computing for AR/VR applications
For enterprise inquiries, contact enterprise@hanzo.ai.
Zen supports flexible deployment across diverse computing environments:
# Deploy micro-SLM configuration
zen deploy --mode edge \
--model-size 0.8B \
--main-modalities "vision,language" \
--memory-limit 2G \
--power-budget 5W# Deploy with custom routing configuration
zen deploy --mode enterprise \
--model-size 50B \
--router-config custom_routing.yml \
--all-modalities enabled \
--memory-limit 64G# Deploy distributed configuration
zen deploy --mode cloud \
--distributed true \
--nodes 4 \
--full-capabilities enabled \
--precision mixedFor detailed deployment instructions, refer to our deployment documentation.
This code repository is licensed under the MIT License. The use of Zen models is subject to the Model License. Zen series (including all variants) supports commercial use.
@misc{hanzoai2025zen,
title={Zen: A Spatially-Aware Foundation Model for 3D Reasoning},
author={Hanzo AI},
year={2025},
eprint={2505.12345},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2505.12345},
}
Zen's technical implementation represents a significant advance in AI system architecture:
The core of Zen's architecture is its dynamic routing system, which orchestrates computation across specialized expert networks:
class ZenRouter(nn.Module):
def __init__(self, config):
super().__init__()
# Router configuration with expert definitions
self.expert_configs = {
"vision": {"model": "llava_next_interleaved", "params": "..."},
"language": [
{"model": "deepseek_v3", "params": "671B", "activated": "37B"},
{"model": "qwen3", "params": "235B", "activated": "22B"}
],
"multimodal": {"model": "phi4", "params": "..."},
"spatial": {"model": "proprietary_3d", "params": "..."}
}
# Task classifier for routing decisions
self.task_classifier = TaskClassifier(config.hidden_size)
# Expert loading and management
self.experts = ExpertManager(self.expert_configs)
# Routing policy network
self.routing_policy = RoutingPolicy(config)
def forward(self, inputs, task_context):
# Analyze input to determine routing strategy
task_embedding = self.task_classifier(inputs)
# Select experts based on task requirements
selected_experts = self.routing_policy.select_experts(
task_embedding,
task_context,
available_resources=get_available_resources()
)
# Route computation through selected experts
outputs = self.experts.process(
inputs,
selected_experts,
precision=determine_precision(selected_experts)
)
return outputsZen's spatial understanding is built on a proprietary 3D representation framework:
- Voxel-Based Scene Graph: Hierarchical representation of objects and their spatial relationships
- Geometric Feature Extraction: Analysis of shapes, surfaces, and spatial arrangements
- Physical Properties Inference: Estimation of mass, material, and dynamic properties
- Temporal Consistency: Tracking objects and their states across time
- Multi-Resolution Processing: Simultaneous global context and fine detail understanding
Zen's hypermodal perception system integrates diverse sensor inputs into a unified representation:
- Cross-Modal Alignment: Consistent representation across visual, depth, inertial, and other modalities
- Sensor Fusion: Combining complementary information to enhance perception reliability
- Modality Translation: Converting between different sensory representations as needed
- Uncertainty Management: Explicit handling of sensor noise and ambiguity
- Missing Modality Compensation: Robust operation when specific sensor inputs are unavailable
Zen integrates and builds upon several state-of-the-art foundation models:
Enhanced vision encoder providing advanced visual understanding with the following modifications:
- Specialized 3D perception layers for spatial understanding
- Extended positional encoding for geometric awareness
- Optimized attention patterns for object permanence and tracking
High-performance language model with 671B parameters (37B activated) contributing:
- Advanced reasoning capabilities for complex problem-solving
- Superior code generation and technical understanding
- Efficient processing of structured data and formal representations
Versatile language model with 235B parameters (22B activated) providing:
- Mathematical reasoning and step-by-step problem solving
- Enhanced multilingual capabilities, especially for Asian languages
- Effective handling of long-context dependencies
Specialized multimodal model adapted for spatial understanding:
- Cross-modal alignment between visual and linguistic representations
- Effective grounding of language in visual and spatial contexts
- Efficient operation in resource-constrained environments
These integrated models form the foundation of Zen's capabilities, with our proprietary routing framework dynamically orchestrating computation across them based on task requirements and available resources.
For inquiries about Zen or partnership opportunities:
- General Inquiries: service@hanzo.ai
- Enterprise Solutions: enterprise@hanzo.ai
- Research Collaboration: research@hanzo.ai
- Investor Relations: investors@hanzo.ai
Visit hanzo.ai to learn more about Zen and our other AI 6AC5 innovations.