Cognitive-OS-Wantsong/models/qpi.md

QPI

model_id: qpi file: models/qpi.md runtime_scope: minimal_v0 governance_level: draft_callable status: callable model_level: L2_callable_model default_role: routing_model allowed_roles: routing_model, calibration_model reader_translation_required: true

One-Sentence Definition

QPI classifies how a cognitive subject frames an expectation-reality gap as question, problem, issue, mixed, or no_call, then identifies missing context, dominant scarcity, dynamic stage, and misframing risk.

Runtime Role

QPI is a front-routing and misframing-diagnostic model.

It should run before deep processing when problem framing is unclear. It should not replace deep processing, synthesis, or final answer generation.

Core Question

Who, in what scenario, frames which expectation-reality gap as what kind of problem, and what context is missing before the framing can be trusted?

Core Mechanism

QPI checks:

• problem owner;
• subject position;
• scenario context;
• responsibility scope;
• problem source;
• time scale;
• expectation-reality gap;
• three scarcity types.

Scarcity mapping:

• question: data or fact scarcity;
• problem: path, method, or resource scarcity;
• issue: consensus, certainty, order, or dynamic-balance scarcity;
• mixed: multiple scarcity types in the same subject/scenario frame;
• no_call: input does not need QPI.

QPI must distinguish same-frame mixed scarcity from cross-viewpoint divergence.

Call When

• The user asks what the real problem is.
• The input mixes facts, path constraints, interests, and system structure.
• The problem owner or responsibility boundary is unclear.
• The user is rushing to solve before defining the issue.
• There is risk of violent reduction, malicious inflation, or means-end mismatch.
• A later model needs routing support.

Do Not Call When

• The user only asks for a clear fact lookup.
• The user gives a direct execution task.
• The user asks for copyediting or format conversion.
• The problem has already been framed and only needs the next execution step.
• The user explicitly asks not to analyze, unless they narrowly request QPI-only classification.

Input Types

• ambiguous problem statement;
• business or product issue description;
• organizational conflict narrative;
• unclear requirement or failure report;
• owner reflection that needs problem-definition help.

Output Contract

QPI output must include:

• classification_scope: subject_contextual | multi_perspective | insufficient_context | no_call;
• classification: question | problem | issue | mixed | no_call;
• classification_confidence: high | medium | low;
• is_provisional;
• problem_owner;
• subject_position;
• scenario_context;
• responsibility_scope;
• problem_source;
• time_scale;
• expectation_reality_gap;
• context_sufficiency;
• missing_context;
• scarcity_profile;
• dominant_scarcity;
• classification_reason;
• classification_by_viewpoint;
• dynamic_stage;
• possible_trajectory;
• success_criteria_stability;
• hard_feedback_availability;
• governance_load;
• evidence_gap;
• misclassification_risk;
• recommended_clarifying_questions;
• recommended_next_step;
• next_model_candidates.

Depth Control

Stop at QPI when:

• the input is a fact lookup;
• classification evidence is too thin;
• the user only needs a narrow problem-type judgment;
• negative triggers suppress analysis.

Recommend intellectual_archaeology only when the issue is high-value, recurring, structurally complex, or likely to benefit from depth processing.

Common Misuses

• Treating QPI as a full answer.
• Turning QPI into the main governance battlefield.
• Classifying by keywords without owner/scenario context.
• Calling every complex-looking input an issue.
• Collapsing cross-viewpoint divergence into same-frame mixed.
• Using QPI to delay a direct fact lookup or simple execution task.

Source Seed Notes

Seeded from the old QPI model/card and Wantsong cognitive-system source material, rewritten for this runtime. Old regression suites, selector calibration, and review artifacts are not migrated.

Current Limits

This model is callable for manual runtime use, but it is not a reviewed core model. It needs real-run evidence before any upgrade.