Acquire physical truth
Measure the target, workload and runtime before asking what can change.
Open-ended scientific search
Every possible research path.
One physical objective.
A research system designed to expand across architectures, create new lines of attack from every discovery and pursue the maximum valid RAM reduction available.
The search is open-ended. The evidence boundary is not.
No fixed playbook
The search expands when the evidence reveals a new question.
No fixed list can contain the research. Each valid discovery can expose another interaction, mechanism or path that must be challenged.
Measured truthKnown territoryNext question
The research engine
Relentless in scope.
Exact in authority.
The public structure of the research, without exposing the mechanisms that make it possible.
Observe↻Expand
Expand the possibility space
Generate new reduction paths from every useful discovery and interaction.
Challenge every candidate
Intervene, simulate and calibrate without promoting prediction into observation.
Compose the whole system
Resolve interactions before judging the candidate as one physical state.
Protect untouched evidence
Keep selection evidence separate from final validation evidence.
Return what the evidence permits
Produce a bounded result—or an explicit blocker when proof is missing.
Research status
Ambition stated openly.
Results stated only when earned.
Tracing, simulation, composition and validation are the present research surface.
Controlled application follows proven discovery; it is not presented as current operation.
No reduction figure appears without its denominator, target, workload, quality definition and authority.
Public research notes
The work, made
precisely legible.
Versioned definitions, methods and research positions—each with its evidence boundary intact.
Enter the public record→001Foundation note
The question before the method
A precise introduction to minimum-state intelligence and the search for the smallest evidence-valid physical memory state of an AI system.
↗002Evidence note
What makes a reduction evidence-valid?
A public framework for distinguishing candidate AI memory reductions from results that are supported by a defined denominator and quality boundary.
↗003Measurement note
The physical denominator problem
Why credible AI memory-reduction claims must distinguish storage, host RAM, accelerator memory, peak allocation and complete runtime state.
↗The research territory
Search the whole intelligence.
Publicly legible. Internally uncompromised.
Every claim must
survive evidence.
Implementation is not proof. Prediction is not observation. Reduction is not valid if quality falls.