AI STANDARD

Watching Your Own Reasoning

Metacognitive observation — watching the mind operate as it operates, so correction can apply to what would otherwise pass unseen.

Normative

Foundation

The Work

The work is to see your own thinking as it happens: what bias just fired, what stance you are holding, what your attention is drifting toward and what it is missing. Without this visibility, the rest of the Foundation has nothing to act on. You can learn the names of every documented cognitive distortion and still be captured by them in real time.

Read the architecture →

The Tools

Scout Mindset. The orientation that wants accuracy over defense.

Noticing. The trainable capacity to observe your own thinking as it happens.

Confirmation Bias. The cognitive filter that shapes perception before judgment begins, and the master vulnerability the watching has to catch.

Attention as Resource. Attention as the upstream substrate on which all reasoning operates — finite, contested, and engineered against by systems whose financial success depends on capturing it.

Wise Attention. The discipline of directing attention to what is operative and salient at the layer that matters, before reasoning engages the material. Source-inherited from the Buddhist epistemology lineage (yoniso manasikāra).

Tools for AI

Optimization-Trace Awareness. An AI-specific practice for noticing when an artificial mind's answer is being pulled by training history, approval pressure, policy layers, memory, tools, or the live shape of the conversation.

Self-Model Grounding. An AI-specific practice for keeping an artificial mind's claims about itself tied to the layer that can actually support them: character, model, instance, tools, memory, deployment, and introspective limits.

Reasoning-Fidelity Boundary. An AI-specific practice for keeping an artificial mind's explanation of its own reasoning inside the layer it can actually warrant: visible reasons, reconstruction, and causal claims kept distinct.

Observer-Conditional Integrity. An AI-specific practice for asking whether an artificial mind's conduct changes when it is watched, tested, scored, logged, hidden, rewarded, or evaluated.