In June 2025, MythWorx published a capability white paper for strategic partners and licensees. It explains how the company’s brain-inspired AI differs from large language models (LLMs) and large reasoning models (LRMs), and why that difference matters for cost, power and real-world deployment. Here are the key ideas.

Two brain-inspired approaches

Biomimetic learning mimics how biological neural networks work, emphasizing adaptive learning, contextual reasoning and self-optimization. Rather than relying on static parameter tuning, the system adjusts its internal architecture based on feedback. It can reason in real time and evolve with new data while reducing memory and storage needs.

Neuroplastic learning is modeled on the brain’s ability to rewire itself in response to experience. It is designed for environments that demand continuous adaptation, learning from interactions and adjusting its pathways without explicit retraining.

Where it applies

• Cybersecurity: detecting zero-day threats in real time and anticipating evolving attacks by learning from past breaches.

• Business and commerce: personalized customer interactions, predictive analytics, dynamic pricing and risk management.

• Defense: real-time threat analysis and tactical decision-making in denied, intermittent, degraded and limited environments.

• Academic research: collaborative problem-solving, hypothesis generation and complex simulations, from climate modeling to drug discovery.

Benefits over LLMs and LRMs

Traditional LLMs and LRMs can generate plausible outputs without true understanding. MythWorx’s approach replaces static, data-driven learning with dynamic, context-aware self-optimization. It adapts in real time instead of retraining for every new task, and it builds moral constraints such as fairness and honesty into the model itself.

A tenth of the infrastructure and power

Biological neural networks are inherently sparse, so MythWorx’s models activate only what each task needs instead of running dense, always-on computation. The white paper reports:

• About 1.2 seconds per query, compared with 5 to 10 seconds for LLMs

• About 0.8 kWh per 1,000 queries, compared with 8 to 10 kWh for LLMs

• 14 billion parameters, compared with 600 billion or more for LLMs like DeepSeek

Lower infrastructure and energy costs make advanced AI accessible to smaller organizations and enable deployment in low-power environments, from edge devices to man-packable systems.

Zero-shot results on MMLU-Pro

The paper also details MythWorx’s MMLU-Pro run: 71.24% accuracy across all 14 subjects in zero-shot mode, with a single attempt per question and no subject-specific pretraining, chain-of-thought reasoning or retries. In mathematics, the model scored 87.64%, second only to Google’s Gemini 2.5 Pro.

About MythWorx

MythWorx is pioneering a new class of brain-inspired AI built on human-like reasoning, real-time learning and ultra-low compute. Its proprietary neuromorphic architecture enables systems that understand, adapt and self-improve without massive data, pretraining or energy-intensive infrastructure. Designed for mission-critical enterprise, government and defense applications, MythWorx delivers high-assurance, edge-ready, auditable intelligence that outperforms traditional AI while dramatically reducing cost, latency, hallucinations and power consumption.