LATENT
REASONING
A new approach to language models. NARE-1 achieves state-of-the-art reasoning with unprecedented efficiency — delivering GPT-4 class performance at a fraction of the cost and latency.
Not just another model. A new class of intelligence.
The NARE-1 Series.
Scalable by design.
The NARE-1 Series represents an optimized inference architecture. While legacy models rely on generating lengthy text traces, our models shift this computational load to internal hidden states, maintaining high intelligence density at scale.
Latent Reasoning Pathways
The NARE-1 architecture shifts reasoning computation to internal hidden states, eliminating the need for explicit chain-of-thought generation.
Fixed-Step Latency
Reasoning overhead is controlled via a fixed budget of latent cycles, providing predictable, high-speed latency for production-grade inference.
High-Consistency Inference
By utilizing latent dissonance feedback loops, common reasoning errors are mitigated within the internal state before token emission.
Minimal Integration
Our architecture supports lightweight adaptation for existing models, enabling latent reasoning capabilities without the overhead of massive full-scale retraining.
Performance Benchmarks
The NARE-1 Series represents an optimized inference architecture that maintains high intelligence density. Below are the audited performance metrics for NARE-1-Mini.
| Benchmark | Result | Advantage |
|---|---|---|
| GSM8K (Zero-Shot) | 68.3% (Amortized) | +30.1% Abs |
| MATH (Logic) | 58.4% (Amortized) | +3.2% Abs |
| HumanEval (Code) | 65.2% (Amortized) | +3.6% Abs |
| MBPP (Python) | 68.5% (Amortized) | +5.3% Abs |
| GPQA (Science) | 32.5% (Amortized) | +2.7% Abs |
| Est. Cost per Logic Task • | $0.0006 (Amortized) | 20.0× Lower |
| Chain-of-Thought Overhead | Non-verbalized | Internalized |
Edge Intelligence Comparison
Efficient computation. Minimized bloat.
Latent planning without the output overhead. While conventional models often rely on extensive chain-of-thought prompt generation, NARE-1-Mini explores higher zero-shot accuracy through fixed-step latent guidance. Our goal is to push the boundaries of architectural efficiency without relying purely on model scale.
| Model | Parameters | GSM8K (Zero-Shot) | GSM8K (With CoT) |
|---|---|---|---|
| NARE-1-Mini (Ours) | 1.7B | 68.3% | N/A (Amortized) |
| NARE-1-Pro | 32B | Target 90%+ | In Training |
| NARE-1-Ultra | 100B+ | TBD | In R&D |
| GPT-3.5 Turbo | Cloud | 57.1% | Required |
| Llama 3 8B | 8.0B | 52.8% | 79.6% (8-shot) |
| Mistral 7B v0.3 | 7.3B | 39.4% | Required |
| Qwen 2.5 1.5B | 1.5B | 38.2% | 73.2% (8-shot) |
We built the architecture the industry hasn't considered.
Built For
Autonomous Agents
Deploy agents that execute complex logical chains with high efficiency. Built for latency-sensitive environments like robotics and autonomous systems.
Research Infrastructure
Seamlessly integrate our proprietary reasoning capabilities into your existing pipelines. Designed for absolute compatibility with modern ML infrastructure.
Enterprise Reasoning
Run frontier-level logical inference entirely on premise. Achieve unprecedented intelligence density without relying on massive cloud compute clusters.
Technical Specifications
A breakdown of the core metrics and capabilities of the NARE-Field experimental architecture.
Caches successful logical pathways into continuous trace memory, bypassing redundant calculations.
Dynamically injects logic back into the LLM based on geometric manifold optimization.
Routes representations through a proprietary continuous space for consistent reasoning.
Implementation Standard
Deploy the NARE-1-Mini foundational model locally with zero external dependencies.
Interactive Latent Playground
Experience zero-latency logical inference firsthand. Input a complex reasoning task and observe the instantaneous output generated by Nare-1-Mini. (Requires valid Research Beta key).
Input
Your prompt and credentialsOutput
Model responseThe future of AI is not generating more tokens.
Consortium Access
NARE-Field is currently operating in closed research preview. API access is granted selectively to academic institutions and enterprise partners deploying autonomous systems.
Grant Provisioning
Approved applicants receive a dedicated research key granting full access to the NARE-Field inference endpoints, standard rate limits, and technical integration support.