Reasoning does not have to live inside the model.
AI models are powerful knowledge engines, but they are opaque, expensive to scale and hard to correct. GitMir moves the reasoning architecture outside model weights into a structured, mutable object graph — the model supplies knowledge, the graph supplies the structure of thought.
Four layers, cleanly separated.
Most AI systems mix knowledge, meaning and reasoning inside one set of weights. GitMir keeps them separate — so each layer can be inspected, corrected and reused on its own.
Knowledge
AI models, databases, documents, code and external systems. Interchangeable sources — none of them owns the reasoning.
Meaning
Objects, relationships, states, rules, processes and constraints — an explicit representation of what the domain actually is.
Reasoning
A dynamic graph that determines how information is interpreted, which rules apply and what happens next.
Runtime
Interprets the reasoning graph into executable behavior and writes outcomes back — so the graph improves with use.
Meaning as an engineering object.
Every object in the graph has a stable identity, explicit relationships, defined states and the rules that govern it. Nothing is implicit: what the system believes can be read, questioned and changed — without retraining anything.
SCALE — · SHEET 1/1
Identity
Every object carries a stable id. Knowledge attaches to the object, not to a prompt.
Relationships
Dependencies are explicit edges — impact and reach can be computed, not guessed.
States
Lifecycles are first-class: what an object is allowed to do depends on where it stands.
Rules
Constraints live outside the model, in reviewable form — corrections take a minute, not a training run.
How a task actually runs.
For each task the runtime traverses the graph and assembles only the slice of meaning the task needs. The model reasons over that compact context, acts, and the outcome is written back into the graph — reasoning improves without retraining.
THE FEEDBACK EDGE IS THE POINT: THE REASONING STRUCTURE IS MUTABLE AND IMPROVES WITH EVERY COMPLETED TASK.
Intelligence per token, not tokens per answer.
Interpret once
The meaning of a system is reconstructed once and kept synchronized — instead of being re-derived from raw sources on every request.
Task-scoped context
Only the relevant slice of the graph reaches the model. Smaller context, same decision quality.
Reuse across consumers
People, agents and analysis tools reason over the same representation — the interpretation cost is paid once, not per consumer.
WE PUBLISH MEASUREMENTS, NOT MULTIPLIERS — SEE THE BENCHMARK PROGRAM ON RESEARCH.
Inspectable
What the system believes is readable — object by object, rule by rule.
Correctable
A wrong belief is edited in the graph, not fine-tuned into weights.
Composable
Models of different systems connect — meaning is a network, not a silo.
Model-agnostic
Knowledge engines are interchangeable; the reasoning layer is yours.