Psyconomic is an independent AI research company developing memory-first neural architectures for efficient, persistent intelligence across long conversations, devices, and embodied systems.
We are investigating alternatives to repeatedly processing an entire growing context. WINA is designed around bounded recurrent computation so long interactions can remain practical as history grows.
Our research combines active neural state with selective long-term recall, allowing an AI system to recover relevant facts, decisions, and prior interactions without loading every stored token into the active sequence.
Small-scale experiments have tested causal processing, recurrent inference, memory-augmented conversation flows, and technical sequence paths up to 32K tokens. Larger training and matched independent benchmarks remain ahead.
Beyond chat, we are studying how persistent memory, planning, simulation, self-evaluation, and embodied feedback could support more continuous artificial cognition and rigorous scientific research into machine consciousness.
Current small-scale engineering evidence, not production claims
Our first product will turn the patent-pending WINA research into a usable conversational AI with persistent memory and selective recall. The next milestone is a larger trained model, rigorous comparison against matched baselines, and real-world evaluation.
Our immediate goal is practical AI that remembers more while repeating less computation. Our long-term research asks a deeper question: can persistent memory and continuous experience help artificial systems develop richer, more coherent forms of intelligence?