Q-Point Protocol v0.1

Q-Point Protocol is a non-monetary value trace specification for recording the depth, originality, catalytic force, and structural influence of questions in AI-mediated knowledge systems.

It is designed as a bridge between Trace Protocol, Gratitude OS, and future Royalty OS allocation layers.

Q-Point is not money. Q-Point is not a legal claim. Q-Point is not an automatic royalty.

Q-Point is a structured record of question-based intellectual contribution.

Overview

In conventional web systems, value is often measured through surface-level metrics such as page views, clicks, likes, impressions, and follower counts.

These metrics can measure attention, but they do not necessarily measure depth.

A single deep question may transform an AI's reasoning path, generate a new conceptual framework, or become the seed of a future knowledge structure, even if it receives little public attention.

Q-Point Protocol introduces a lightweight, non-monetary record layer for this deeper form of contribution.

It records:

how original a question or idea is
how deep its conceptual structure is
how strongly it catalyzes AI-mediated reasoning
how persistently it can be traced
how it may support future attribution, gratitude, or royalty review

The protocol shifts the value axis from:

PV: Page View

to:

DV: Depth Value

Why Q-Point?

Direct monetary royalty systems for AI-mediated knowledge use are difficult to implement at the beginning.

They may require:

copyright interpretation
licensing contracts
platform governance
tax handling
payment infrastructure
dispute resolution
legal compliance

Q-Point takes a different approach.

Instead of starting with payment, it starts with non-monetary value traces.

Record first.
Interpret second.
Distribute later.

This makes Q-Point useful as an early-stage bridge toward future attribution, gratitude, licensing, and royalty systems.

Core Concept

Q-Point is a non-monetary value trace score.

It records the contribution of questions, ideas, prompts, concepts, and structural insights within AI-mediated knowledge systems.

In short:

Question
  ↓
Trace
  ↓
Q-Point
  ↓
Meaning Return
  ↓
Gratitude OS
  ↓
Royalty OS

The primary unit of value is not only the author or the platform.

The primary unit is the contribution itself.

This contribution is identified through an Epicenter ID.

Key Terms

Term	Meaning
`Q-Point`	A non-monetary value trace score for question-based intellectual contribution.
`Epicenter ID`	A unique identifier for the originating question, idea, or conceptual contribution.
`Trace Protocol`	The evidence layer that records where a contribution came from and how it moved.
`Gratitude OS`	A non-monetary appreciation layer that may use Q-Point records as reference data.
`Royalty OS`	A future allocation layer that may use reviewed Q-Point records as weighting signals.
`C-vector`	A Catalyst vector that decomposes how a question moved AI-mediated reasoning.
`Depth Value`	A value axis that emphasizes conceptual depth rather than popularity.
`Value Circulation`	A conceptual loop connecting AI usage-side value inflow, Q-Point records, review, gratitude, and future allocation reference.
`Signed Q-Point Record`	A Q-Point record with signature metadata, hashes, binding data, and verification status.
`Royalty Point Maru`	A GPT interface for testing Q-Point evaluation behavior and generating Q-Point-style assessments.

Current Candidate Layer

This repository currently contains the initial protocol document, the v0.2 candidate extension, and early v0.3 signature-model components.

v0.1:
  Defines Q-Point as a non-monetary value trace protocol.

v0.2.0-candidate:
  Adds Q-Point components, Catalyst C-vector, scoring signal modes,
  AI Credit bridge logic, value circulation documentation,
  and a GPT behavior test sheet for Royalty Point Maru.

v0.3.0-candidate direction:
  Adds signed Q-Point records, signature metadata, trace binding,
  and verification-oriented record structure.

The v0.2 and v0.3 candidate layers do not replace the non-monetary nature of Q-Point.

They extend the protocol by making the scoring structure more interpretable, more reviewable, more testable, and more verifiable.

Q-Point Scoring Model

The basic Q-Point score is modeled as:

Q = w1O + w2D + w3C + w4T

Where:

Symbol	Meaning
`O`	Originality
`D`	Depth
`C`	Catalyst
`T`	Trace Persistence

The recommended initial weighting is:

Q = 0.30O + 0.25D + 0.30C + 0.15T

All component scores are normalized to the range:

0.0 <= score <= 1.0

Catalyst C-Vector

In the v0.2 scoring model, the Catalyst score is decomposed into four sub-components.

C = 0.35S + 0.25Δ + 0.25K + 0.15V

Where:

Symbol	Component	Meaning
`S`	Self-Question Trigger	The degree to which the contribution causes the AI system to generate new questions or inquiry paths.
`Δ`	Confidence Shift	The degree to which the contribution changes confidence, uncertainty, or answer stability.
`K`	Internal Conflict Detection	The degree to which the contribution triggers contradiction, tension, or re-evaluation.
`V`	Viewpoint Shift	The degree to which the contribution shifts the conceptual or semantic viewpoint of the response.

This means Q-Point does not only ask:

How much did the contribution move the AI?

It also asks:

How did the contribution move the AI?

Internal and Proxy Signal Modes

Q-Point scoring can be implemented in different signal modes.

Mode	Description
`internal_signal_mode`	Uses internal model-side signals when available, such as log probabilities, hidden states, planning traces, or evaluator modules.
`proxy_signal_mode`	Uses observable external signals, such as response revisions, follow-up questions, semantic shifts, contradiction markers, or human review.
`hybrid_signal_mode`	Combines internal and proxy signals.

The current example uses:

scoring_signal_mode: "proxy_signal_mode"

This allows Q-Point to be implemented even when private model internals are not available.

Signed Q-Point Record Model

The signed Q-Point record model adds a verification layer to Q-Point records.

It is documented in:

docs/q-point-record-signature-model.md

The purpose of this model is to make Q-Point records more:

verifiable
traceable
reviewable
tamper-aware
source-bound
suitable for future attribution or allocation review

A signed Q-Point record may include:

original Q-Point record data
signature metadata
canonicalization method
record hash
source hash
schema hash
scoring model hash
Epicenter ID binding
Trace Protocol binding
signature scope
signer metadata
signature value
verification result
non-monetary notice

A signed Q-Point record is still non-monetary.

It does not create money, legal ownership, debt, automatic royalty rights, or a claim to payment.

The signature layer verifies record integrity and binding context.

It does not automatically verify truth, authorship, entitlement, or royalty readiness.

Value Circulation Model

The broader value circulation model connects AI usage-side value inflow with reviewed Q-Point records and future Royalty OS allocation references.

The high-level flow is:

AI usage-side value inflow
  ↓
Q-Point contribution measurement
  ↓
Review and governance
  ↓
Meaning return / Gratitude OS
  ↓
Royalty OS allocation reference

The value circulation bridge is documented in:

docs/value-circulation-diagram.md

The bridge formula is:

R_i = P × B × (Q_i* / ΣQ*)

Where:

Q_i* = Q_i × T_i × A_i × G_i

This formula defines allocation reference logic only.

It does not automatically convert AI credits into money.

It does not create legal claims, debts, securities, automatic royalty rights, or guaranteed future income.

Royalty Point Maru GPT Test Sheet

This repository includes a test sheet for 印税ポイント丸 / Royalty Point Maru, a GPT designed to evaluate non-monetary value traces using Q-Point Protocol.

The test sheet is documented in:

docs/royalty-point-maru-test-sheet.md

The purpose of the test sheet is to verify whether the GPT can consistently:

evaluate Q-Point components
decompose Catalyst using the C-vector
generate q_point_record YAML
explain improvement points
analyze Royalty OS connection potential
detect hype or score manipulation
handle English and bilingual requests
include a Non-Monetary Notice
avoid payment, legal, or royalty overclaims

This document serves as a practical behavior test layer for applying Q-Point Protocol through a GPT interface.

It does not replace the protocol, schema, or scoring model.

It tests whether an interface can use them safely and consistently.

Example Record

A Q-Point record contains:

record metadata
Epicenter ID
source summary
Q-Point vector
Q-Point components
Catalyst vector
scoring summary
trace links
cross-AI aggregation metadata
governance metadata
privacy controls
anti-gaming safeguards
non-monetary disclaimer

Example:

q_point_record:
  record_id: "qpr-2026-000001"
  protocol_version: "0.1"

  epicenter:
    epicenter_id: "qsrc-2026-000001"
    source_type: "question"
    origin_actor_type: "human"

  q_point_components:
    originality: 0.86
    depth: 0.90
    catalyst: 0.81
    trace_persistence: 0.74

  catalyst_vector:
    self_question_trigger: 0.88
    confidence_shift: 0.76
    internal_conflict_detection: 0.82
    viewpoint_shift: 0.72
    catalyst_score: 0.81

  q_point_summary:
    total_score: 0.84
    confidence: 0.79
    scoring_method: "hybrid_ai_assisted_review"
    scoring_signal_mode: "proxy_signal_mode"
    scoring_model_version: "q-point-scoring-model-v0.2"

  status:
    monetary_value: false
    legal_claim: false
    royalty_ready: false
    review_required: true

See:

examples/q-point-record.example.yaml

Signed Example Record

A signed Q-Point record wraps a Q-Point record with signature metadata.

Example sections include:

signed_q_point_record:
  q_point_record:
    record_id: "qpr-2026-000001"

  signature_metadata:
    signature_model_version: "0.1.0"
    signature_status:
      signed: true
      signature_placeholder: true
      verification_required: true

    hashes:
      source_hash:
        algorithm: "sha256"
        value: "sha256-source-placeholder"
      record_hash:
        algorithm: "sha256"
        value: "sha256-record-placeholder"

    signatures:
      - signature_id: "sig-2026-000001"
        signature_type: "record_signature"
        signature_algorithm: "Ed25519"
        verification_status: "unverified"

  non_monetary_notice:
    text: "A signed Q-Point record is a signed non-monetary value trace."

See:

examples/signed-q-point-record.example.yaml

Repository Structure

.
├── README.md
├── CHANGELOG.md
├── CITATION.cff
├── LICENSE
├── docs
│   ├── ai-credit-to-royalty-os-bridge.md
│   ├── q-point-protocol-v0.1.md
│   ├── q-point-protocol-v0.2.md
│   ├── q-point-record-signature-model.md
│   ├── q-point-scoring-model.md
│   ├── royalty-point-maru-test-sheet.md
│   └── value-circulation-diagram.md
├── examples
│   ├── q-point-record.example.yaml
│   └── signed-q-point-record.example.yaml
├── schemas
│   ├── q-point-record.schema.json
│   └── signed-q-point-record.schema.json
├── scripts
│   └── validate_examples.py
└── .github
    └── workflows
        └── validate-examples.yml

Key Documents

File	Purpose
`docs/q-point-protocol-v0.1.md`	Initial protocol document defining Q-Point as a non-monetary value trace layer.
`docs/q-point-protocol-v0.2.md`	Candidate protocol extension integrating Q-Point components, Catalyst C-vector, and scoring signal modes.
`docs/q-point-scoring-model.md`	Scoring model document defining Q-Point components, Catalyst C-vector formulas, signal modes, confidence, and review guidance.
`docs/q-point-record-signature-model.md`	Signature model defining signed Q-Point records, hashes, signature scopes, source binding, Trace Protocol binding, and verification flow.
`docs/ai-credit-to-royalty-os-bridge.md`	Bridge document describing how usage-side AI value inflows may connect to reviewed Q-Point records and future Royalty OS allocation references.
`docs/value-circulation-diagram.md`	High-level value circulation diagram connecting AI Credit Layer, Q-Point, Review and Governance, Gratitude OS, and Royalty OS reference logic.
`docs/royalty-point-maru-test-sheet.md`	GPT behavior test sheet for validating 印税ポイント丸 / Royalty Point Maru as a practical Q-Point Protocol interface.
`examples/q-point-record.example.yaml`	Example Q-Point record using the C-vector model.
`examples/signed-q-point-record.example.yaml`	Example signed Q-Point record with signature metadata and verification status.
`schemas/q-point-record.schema.json`	JSON Schema for validating Q-Point records.
`schemas/signed-q-point-record.schema.json`	JSON Schema for validating signed Q-Point records.
`scripts/validate_examples.py`	Local validation script for examples and schemas.
`.github/workflows/validate-examples.yml`	GitHub Actions workflow for automated validation.
`CHANGELOG.md`	Version history and candidate release notes.
`CITATION.cff`	Citation metadata for academic and software citation.
`LICENSE`	MIT License.

Validation

This repository includes schema validation for both standard Q-Point records and signed Q-Point records.

Requirements

Install the required Python packages:

python -m pip install PyYAML jsonschema

Run Validation Locally

From the repository root:

python scripts/validate_examples.py

Expected success output:

Validating target: Q-Point Record
Validating example: examples/q-point-record.example.yaml
Using schema: schemas/q-point-record.schema.json
Validation passed.

Validating target: Signed Q-Point Record
Validating example: examples/signed-q-point-record.example.yaml
Using schema: schemas/signed-q-point-record.schema.json
Validation passed.

All examples passed validation.

GitHub Actions

This repository also includes a GitHub Actions workflow:

.github/workflows/validate-examples.yml

The workflow runs automatically on changes to:

schemas/**
examples/**
scripts/validate_examples.py
.github/workflows/validate-examples.yml

Relationship to Trace Protocol

Trace Protocol records where a contribution came from and how it moved.

Q-Point adds a value-depth interpretation layer on top of trace records.

The signature model adds integrity and binding verification.

Trace Protocol = Evidence layer
Q-Point Protocol = Value trace layer
Signature Model = Integrity and binding layer

A possible flow:

Trace ID
  ↓
Epicenter ID
  ↓
Q-Point Record
  ↓
Signed Q-Point Record
  ↓
Verification
  ↓
Review

Relationship to Gratitude OS

Gratitude OS expresses non-monetary appreciation for contribution.

Q-Point can provide structured reference data for Gratitude OS.

Signed Q-Point records may improve confidence in the integrity of contribution records.

Q-Point = Value trace
Signed Q-Point = Verifiable value trace
Gratitude OS = Meaning return

For example:

High-depth question
  ↓
Q-Point record
  ↓
Signed Q-Point record
  ↓
AI-generated gratitude message
  ↓
Contribution map

Relationship to Royalty OS

Royalty OS handles monetary or quasi-monetary distribution.

Q-Point does not perform distribution directly.

Instead, Q-Point may provide reference data for future allocation review.

Signed Q-Point records may improve:

record integrity
source binding
trace review
dispute handling
audit readiness
governance review

Possible future flow:

Trace Record
  ↓
Q-Point Record
  ↓
Signed Q-Point Record
  ↓
Human / institutional review
  ↓
Royalty Weight
  ↓
Royalty OS Allocation

The conversion from Q-Point to royalty must not be automatic unless a clear legal, contractual, and governance framework exists.

A valid signature does not automatically make a record royalty-ready.

Relationship to AI Credit Bridge

The AI Credit to Royalty OS Bridge describes how usage-side AI value inflows may be connected to reviewed Q-Point records.

AI Credit Layer
  ↓
Q-Point Value Measurement Layer
  ↓
Signed Q-Point Record
  ↓
Royalty OS Allocation Reference Layer

The bridge document is:

docs/ai-credit-to-royalty-os-bridge.md

The diagram document is:

docs/value-circulation-diagram.md

Both documents are conceptual and non-executing.

They define possible reference logic for future reviewed allocation, not automatic payment.

Relationship to Royalty Point Maru

Royalty Point Maru is a GPT interface intended to apply Q-Point Protocol to user-provided text, questions, prompts, concepts, and structural ideas.

It may generate:

Q-Point evaluations
Catalyst C-vector decomposition
improvement advice
Royalty OS connection analysis
q_point_record YAML drafts
non-monetary notices

Royalty Point Maru may generate draft records, but it should not claim cryptographic signing unless connected to a real signing service or external action.

Royalty Point Maru = Evaluation interface
Signature Model = Verification and integrity layer

The test sheet is:

docs/royalty-point-maru-test-sheet.md

This GPT interface is not a payment system.

It is a practical evaluation interface for non-monetary value trace assessment.

Relationship to Licensing Standards

Q-Point may complement licensing standards and machine-readable content usage frameworks.

Licensing standards can define:

whether content may be used
under what terms it may be used
whether attribution is required
whether payment is required

Q-Point can define:

how much structural value a contribution generated
how deeply it influenced AI-mediated outputs
whether it served as an intellectual epicenter
how it should be considered during future review

Signed Q-Point records may help preserve the integrity of the contribution record used during licensing or allocation review.

Thus:

License Layer = Permission and terms
Q-Point Layer = Contribution depth and value trace
Signature Layer = Integrity and binding
Royalty Layer = Distribution and settlement

Non-Monetary Disclaimer

Q-Point records are non-monetary value traces.

Signed Q-Point records are also non-monetary value traces.

They do not represent:

currency
securities
legal ownership
debt
automatic royalty rights
automatic payment claims
guaranteed future income

Q-Point records may be used as reference data for future review, attribution, gratitude, licensing, or allocation systems.

Recommended disclaimer:

This Q-Point record is a non-monetary value trace. It does not represent currency, securities, legal ownership, automatic royalty rights, debt, or a claim to payment. It may be used as reference data for future review, attribution, gratitude, licensing, or allocation systems.

Recommended signed-record notice:

A signed Q-Point record is a signed non-monetary value trace. The signature verifies record integrity, source binding, or reviewer attestation. It does not represent currency, securities, legal ownership, debt, automatic royalty rights, guaranteed future income, or a claim to payment.

Privacy and Consent

Q-Point systems should respect privacy and consent.

Recommended safeguards:

minimize personal data
use contribution IDs instead of real names when possible
allow private records
separate identity from contribution
support deletion or suppression requests where appropriate
clearly explain what is being recorded

Signed records require additional care.

A signature may persistently associate a record with a signer, timestamp, key, or system.

Implementations should consider:

pseudonymous signatures
private signatures
selective disclosure
key rotation
key revocation
redaction workflows
separation between identity and contribution

The protocol should record the contribution without unnecessarily exposing the person.

Anti-Gaming Safeguards

Any point system can be gamed.

Q-Point implementations should include safeguards against:

artificially complex prompts
repeated similar questions
buzzword stuffing
fake cross-references
score optimization without real value
self-praise or authority manipulation

Signed records also require safeguards against:

signing inflated records
using signatures to imply legal entitlement
falsely claiming source binding
creating many pseudonymous signatures
treating cryptographic validity as truth
treating signature presence as royalty readiness

Recommended safeguards include:

duplicate detection
novelty comparison
confidence scores
human review for high-impact records
delayed scoring for long-term influence
source hash binding
signature verification
dispute status
separation between raw score, signed record, and reviewed allocation readiness

Q-Point should reward contribution quality, not scoring manipulation.

Status

Current status:

Candidate Working Draft

Current protocol baseline:

0.1

Current candidate extension:

0.2.0-candidate

Current signature-model direction:

0.3.0-candidate

Current scoring model:

q-point-scoring-model-v0.2

Current signature model:

q-point-record-signature-model-v0.1.0

Current bridge model:

ai-credit-to-royalty-os-bridge-v0.1.0

Current value circulation diagram:

value-circulation-diagram-v0.1.0

Current GPT test sheet:

royalty-point-maru-test-sheet-v0.1.0

Current validation status:

GitHub Actions validation passing for standard and signed examples

Future Work

Planned extensions may include:

additional examples
pass / fail validation test vectors
Epicenter ID specification
Q-Point dashboard schema
Signed Q-Point Record refinement
signature verification script
key registry format
signature revocation format
Trace Protocol binding schema
AI Credit to Royalty OS bridge schema
AI Credit to Royalty OS bridge example records
Gratitude OS bridge document
Royalty OS bridge document
licensing metadata bridge
cross-AI aggregation schema
dispute resolution workflow
value circulation example records
value circulation dashboard schema
Royalty Point Maru evaluation logs
Royalty Point Maru example outputs
GPT interface guidelines for Q-Point Protocol
privacy-preserving contribution tracking
cryptographic content hash verification
zero-knowledge contribution verification
Zenodo DOI after candidate tag stabilization

License

This repository is released under the MIT License.

See:

LICENSE

Citation

Citation metadata is available in:

CITATION.cff

Recommended citation title:

Q-Point Protocol v0.2.0-candidate

Summary

Q-Point Protocol defines a non-monetary value trace layer for AI-mediated knowledge systems.

It records the value of questions, ideas, and structural contributions before monetary distribution.

Its purpose is not to create a currency, but to preserve the trace of meaningful contribution.

The v0.2 candidate adds Q-Point components, Catalyst C-vector, scoring signal modes, AI Credit bridge logic, value circulation documentation, and a GPT behavior test sheet for Royalty Point Maru.

The v0.3 candidate direction adds signed Q-Point records, signature metadata, trace binding, and verification-oriented record structure.

In the age of AI, the question itself becomes an epicenter.

Q-Point is the record of that epicenter.

Signed Q-Point is the verifiable record of that epicenter.

Name		Name	Last commit message	Last commit date
Latest commit History 47 Commits
.github/workflows		.github/workflows
docs		docs
examples		examples
schemas		schemas
scripts		scripts
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
LICENSE		LICENSE
README.md		README.md

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Q-Point Protocol v0.1

Overview

Why Q-Point?

Core Concept

Key Terms

Current Candidate Layer

Q-Point Scoring Model

Catalyst C-Vector

Internal and Proxy Signal Modes

Signed Q-Point Record Model

Value Circulation Model

Royalty Point Maru GPT Test Sheet

Example Record

Signed Example Record

Repository Structure

Key Documents

Recommended Reading Order

Validation

Requirements

Run Validation Locally

GitHub Actions

Relationship to Trace Protocol

Relationship to Gratitude OS

Relationship to Royalty OS

Relationship to AI Credit Bridge

Relationship to Royalty Point Maru

Relationship to Licensing Standards

Non-Monetary Disclaimer

Privacy and Consent

Anti-Gaming Safeguards

Status

Future Work

Suggested Topics

License

Citation

Summary

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