Cognitive-OS-Wantsong/docs/COGNITIVE_WORKFLOW_V0.md

Cognitive Workflow V0

status: draft_workflow_contract date: 2026-06-20

This file describes the intended runtime loop. It is not a sample run and does not authorize M0-M1 to create prompt files or examples.

Runtime Flow

1. Intake / Value Assessment
2. QPI
3. Lens Orchestrator
4. Deep Processing
5. Synthesis & Calibration
6. Feedback & Asset Decision
7. Reader Translation

1. Intake / Value Assessment

Purpose: decide whether the input deserves cognitive processing and what depth budget is justified.

Expected output:

• processing level: L1 | L2 | L3 | L4;
• reason for depth;
• whether heavy processing is justified;
• required context gaps.

2. QPI

Purpose: classify the issue framing before deeper work.

QPI outputs:

• question | problem | issue | mixed | no_call;
• owner and scenario;
• dominant scarcity;
• missing context;
• misframing risks;
• recommended next step.

QPI does not solve the problem. It only controls routing and prevents framing mistakes.

3. Lens Orchestrator

Purpose: choose a small set of models/lenses for this run.

Default call budget:

• one primary model;
• two or three support or contrast models at most;
• one calibration lens when needed;
• one reader translation layer when producing external-facing text.

M0-M1 only prepares the fields needed for this later choice. It does not implement a selector.

4. Deep Processing

Purpose: run the selected primary/depth model.

For startup, the only depth model is intellectual_archaeology. It should run only when the input has enough value, complexity, or reuse potential.

Depth processing must obey minimum sufficient depth. Continuing deeper is justified only when it changes judgment, path, validation, action boundary, or asset decision.

5. Synthesis & Calibration

Purpose: combine outputs and prevent a single model from over-claiming.

Expected output:

• main judgment;
• supporting reasoning;
• conflicts between model outputs;
• evidence level;
• action boundary;
• what would change the conclusion.

6. Feedback & Asset Decision

Purpose: decide whether the run produced reusable learning.

Expected output:

• whether the output was useful;
• whether the run should become a sample later;
• whether a model card needs a future repair;
• whether a new model need was exposed.

7. Reader Translation

Purpose: turn internal trace into a reader-facing explanation.

The internal output can contain model vocabulary and deep structure. The reader output must explain the actual issue, mechanism, example, boundary, and next way to think in language a non-model-maintainer can follow.