boltzmind.ai

Engineering Persistent Intelligence

Boltzmind.ai is a deep-tech research initiative building stateful cognitive architectures for next-generation AI systems.

We move beyond stateless prediction — toward structured physics-based world modeling, memory integration, object-centric reasoning and reliability systems aligned with international AI safety and interpretability requirements.

What We Do

We design foundational architectures that integrate perception, hierarchical memory, spatial and relational reasoning, and meta-cognition into unified systems.

Our research explores how modern neural networks evolve into persistent world models capable of reasoning across time and knowing when their own understanding is becoming unreliable. We build products, infrastructure and communities to help align our AI goals with the society it is meant to serve.

Our Projects

CORE

CORE

Cognitive Objects Representation Engine

Initial launch of CORE in version 0.1 as a LLM/VLM augmentation middleware to achieve world model capabilities. The 20mb checkpoint gives vision models object grounding, spatial understanding and object relationship prediction in a stateful scene

CORE version 0.2 introduces a physics-based meta-cognition layer built on top of the object memory layer of version 0.1. Inspired by Boltzmann decay equations and other physics principles, v0.2 gives physical AI models the ability to know when their perception is becoming unreliable, the ability to predict a scene wide cascade risk, and the decision to trust more of its own memory than the current perception. Ultimately, an interpretable reliability layer for physical AI inline with safety regulations.

Visit CORE →

Vision

To advance AI toward structured, interpretable and persistent world models that understand their own limits and work in conjuction with a human in the loop. We envision a future where AI is super-intelligent, fair, auditable and fundamentally aligned with safety and governance.

Mission

To architect foundational systems that unify memory, perception, reasoning, and meta-cognition, bridging deep learning with structured cognition.