Technical notes, evaluation methodology, and research briefs from SILENTPATTERN. All claims are bounded by explicit evidence and reproducible protocols.
Research into minimal representations that preserve intervention-relevant causal structure. Focus on identifiability, intervention accuracy, and stability under distribution shift.
Methods for prediction under distribution shift. Emphasis on regime detection, calibration, and uncertainty quantification rather than point estimates.
Infrastructure for automated experiments with full audit trails. Every result is traceable from claim to evidence.
Research into agent systems with explicit constraints, audit trails, and fail-closed behaviors. Governance is a feature, not a limitation.
Define datasets, splits, baselines, and metrics before running experiments. If the protocol is weak, results do not count.
Public language remains bounded: "concept," "prototype," "validated" with explicit assumptions and limitations.
Calibration, confidence intervals, and failure modes are included in all reports. No claims without replicated benchmarks.
Developing standardized benchmarks for causal abstraction quality across synthetic and real-world datasets.
Building tools for automatic regime identification and model switching under distribution shift.
Defining permission hierarchies and audit requirements for autonomous research agents.
Research access is curated. Contact us for collaboration opportunities or evaluation partnerships.
Curated access for research and evaluation