Design the mechanism. Test the claim.
Research OS is Arcence's environment for specifying, testing, and understanding neural architectures and internal mechanisms. We do not start with the assumption that a new architecture will beat a strong baseline—we start with the discipline required to find out.
Spec → Proof → Compile → Trials → Circuit → Guard → Evidence
Formal Architecture & Problem Specification
State the computational hypothesis, state-update rules, memory/compute complexity targets, and domain constraints explicitly before writing training loops.
Invariants, Bounds & Pre-Registered Falsifiers
Define the mathematical stability bounds and the exact empirical outcomes that would falsify the hypothesis before any model is trained.
Reproducible Execution Graph
Lower the specification into a deterministic training and evaluation pipeline with locked dataset versions, split hashes, and seed tracking.
Matched-Baseline Empirical Trials
Run simple baselines first at matched parameters and compute across multiple random seeds, reporting negative results in the same table as positive ones.
Causal Mechanism & Circuit Inspection
Investigate which internal representations and pathways actually drive model outputs using controlled interventions rather than relying on surface benchmark scores.
Targeted Intervention & Abstention Calibration
Test whether brittle shortcut pathways can be suppressed without degrading general performance, and calibrate uncertainty bounds so the system knows when to abstain.
Reproducible Evidence Bundle
Package the specification, baseline comparison, circuit findings, and falsifier outcomes into an auditable record that connects directly into the Assurance Plane.