MARC: A Control and Uncertainty Disclosure Profile for Generative Models and Agents
draft-c4tz-marc-01
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draft-c4tz-marc-01
Network Working Group c4tz
Internet-Draft c0dx3
Intended status: Informational 29 April 2026
Expires: 31 October 2026
MARC: A Control and Uncertainty Disclosure Profile for Generative Models
and Agents
draft-c4tz-marc-01
Abstract
This document specifies MARC, a vendor-neutral control and
uncertainty-disclosure profile for generative models and agentic
systems. MARC defines a small set of interoperable control metadata,
separates pre-decision capability assessment from post-decision
answer confidence, and defines a bounded primary action set for
answering, clarification, retrieval, tool use, additional
deliberation, abstention, and escalation.
MARC does not standardize model internals, training methods, agent
discovery, authorization, transport, tool schemas, or claims about
machine cognition. Instead, it defines externally observable
semantics that can be implemented by model providers, orchestration
layers, evaluation harnesses, API gateways, and user-facing systems.
The goal is to reduce silent failure, unnecessary externalization,
and misleading uncertainty communication while improving auditability
and interoperability.
Status of This Memo
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provisions of BCP 78 and BCP 79.
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on 31 October 2026.
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Copyright Notice
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document authors. All rights reserved.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Problem Statement . . . . . . . . . . . . . . . . . . . . . . 4
3. Requirements Language and Terminology . . . . . . . . . . . . 5
4. Design Goals and Non-Goals . . . . . . . . . . . . . . . . . 6
4.1. Design Goals . . . . . . . . . . . . . . . . . . . . . . 6
4.2. Non-Goals . . . . . . . . . . . . . . . . . . . . . . . . 7
5. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . 7
5.1. Ambiguous User Request . . . . . . . . . . . . . . . . . 7
5.2. Retrieval-Augmented Answering . . . . . . . . . . . . . . 7
5.3. Agent Tool Invocation . . . . . . . . . . . . . . . . . . 7
5.4. API Gateway or Orchestration Layer . . . . . . . . . . . 8
5.5. Agent-to-Agent Handoff . . . . . . . . . . . . . . . . . 8
5.6. High-Risk Domain Escalation . . . . . . . . . . . . . . . 8
6. Architecture and Processing Model . . . . . . . . . . . . . . 8
6.1. Functional Components . . . . . . . . . . . . . . . . . . 8
6.2. Processing Stages . . . . . . . . . . . . . . . . . . . . 8
6.3. State Machine . . . . . . . . . . . . . . . . . . . . . . 9
7. MARC Values and Decision Policy . . . . . . . . . . . . . . . 9
7.1. Pre-Decision Capability . . . . . . . . . . . . . . . . . 9
7.2. Uncertainty Attribution . . . . . . . . . . . . . . . . . 10
7.3. Remediability . . . . . . . . . . . . . . . . . . . . . . 11
7.4. Post-Decision Confidence . . . . . . . . . . . . . . . . 11
7.5. Confidence Band . . . . . . . . . . . . . . . . . . . . . 11
7.6. Primary Action Set . . . . . . . . . . . . . . . . . . . 12
7.7. Action Selection . . . . . . . . . . . . . . . . . . . . 12
7.8. Action Semantics . . . . . . . . . . . . . . . . . . . . 14
8. MARC-Core Object . . . . . . . . . . . . . . . . . . . . . . 14
8.1. Required and Optional Fields . . . . . . . . . . . . . . 14
8.2. Enumerated Values . . . . . . . . . . . . . . . . . . . . 15
8.3. Validation Constraints . . . . . . . . . . . . . . . . . 17
8.4. JSON Example . . . . . . . . . . . . . . . . . . . . . . 17
9. MARC-Disclosure Object . . . . . . . . . . . . . . . . . . . 17
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9.1. Meaning of the Answer Field . . . . . . . . . . . . . . . 18
9.2. Projection from MARC-Core . . . . . . . . . . . . . . . . 18
9.3. Disclosure Constraints . . . . . . . . . . . . . . . . . 19
10. Versioning and Extension Rules . . . . . . . . . . . . . . . 19
11. Relationship to Agent Communication Protocols . . . . . . . . 20
12. Operational Profiles . . . . . . . . . . . . . . . . . . . . 20
12.1. MARC-Core Only . . . . . . . . . . . . . . . . . . . . . 20
12.2. MARC-Disclosure . . . . . . . . . . . . . . . . . . . . 21
12.3. MARC-Carrying . . . . . . . . . . . . . . . . . . . . . 21
13. Human Factors Considerations . . . . . . . . . . . . . . . . 21
14. Conformance . . . . . . . . . . . . . . . . . . . . . . . . . 22
14.1. Minimum Viable Conformance . . . . . . . . . . . . . . . 22
14.2. Conformance Classes . . . . . . . . . . . . . . . . . . 23
15. Interoperability and Operational Considerations . . . . . . . 23
16. Security Considerations . . . . . . . . . . . . . . . . . . . 24
17. Privacy and Manipulation-Resistance Considerations . . . . . 26
18. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 26
19. Normative References . . . . . . . . . . . . . . . . . . . . 27
20. Informative References . . . . . . . . . . . . . . . . . . . 27
Appendix A. End-to-End Decision Flow Example . . . . . . . . . . 28
Appendix B. Example MARC-Core Records . . . . . . . . . . . . . 30
B.1. Ambiguous Request . . . . . . . . . . . . . . . . . . . . 30
B.2. Missing Evidence . . . . . . . . . . . . . . . . . . . . 30
B.3. Tool Use . . . . . . . . . . . . . . . . . . . . . . . . 31
B.4. Capability Limit in a High-Risk Setting . . . . . . . . . 31
Appendix C. Example MARC-Disclosure Objects . . . . . . . . . . 32
C.1. Clarification Disclosure . . . . . . . . . . . . . . . . 32
C.2. Answer After Retrieval Disclosure . . . . . . . . . . . . 32
Appendix D. Non-Normative JSON Schemas . . . . . . . . . . . . . 33
D.1. MARC-Core JSON Schema . . . . . . . . . . . . . . . . . . 33
D.2. MARC-Disclosure JSON Schema . . . . . . . . . . . . . . . 36
Appendix E. Evaluation Considerations . . . . . . . . . . . . . 37
Appendix F. Design Rationale and Literature Traceability . . . . 38
Appendix G. Changes from -00 . . . . . . . . . . . . . . . . . . 38
Appendix H. Acknowledgments . . . . . . . . . . . . . . . . . . 40
Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 40
1. Introduction
Generative models and agentic systems increasingly combine answering,
retrieval, tool invocation, and user interaction within a single
workflow. In many deployments, these behaviors are implemented as
separate heuristics, producing inconsistent handling of uncertainty,
unnecessary tool calls, silent failure, misleading refusals, or user
overreliance.
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MARC defines a vendor-neutral profile for control metadata and
structured uncertainty disclosure. It does not standardize model
internals. Instead, it standardizes the semantics of a small set of
second-order signals, a bounded action set, and a minimal disclosure
profile that can be implemented by a base model, an external
orchestrator, a model gateway, or a hybrid architecture.
This document is not intended to define a Standards Track protocol, a
model evaluation benchmark, or a claim about machine consciousness.
It is an Informational profile for interoperable control, logging,
and disclosure behavior around generative systems and agents.
The design is motivated by findings that current large language
models often exhibit weak metacognitive reporting in high-stakes
reasoning tasks [GRIOT2025], that users can become overconfident when
systems provide longer or default explanations [STEYVERS-KNOW2025],
that metacognitive triggering can improve tool-use decisions
[LI-MECO2025], and that identifying the source of uncertainty is
distinct from merely abstaining [LIU-CONFUSE2025]. Work on cognitive
offloading further motivates treating retrieval and tool use as
value-based control choices rather than universal fallbacks
[GILBERT2024].
MARC also separates pre-decision capability assessment from post-
decision confidence about the selected answer. This separation is
motivated in part by evidence that LLM confidence can be biased by
prior answer commitment and by the visibility of the model's own
earlier output [KUMARAN2026].
2. Problem Statement
Generative and agentic systems lack a common, implementation-neutral
way to represent the control state associated with uncertainty-aware
action selection. In particular, downstream systems often cannot
distinguish between the following situations:
* the request is ambiguous and user clarification is the best next
action;
* current evidence is missing, inaccessible, insufficient, or stale,
and retrieval would likely help;
* the system lacks competence for the task even after available
resources are considered;
* available evidence is materially inconsistent and should be
reconciled or escalated;
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* a safety, legal, or policy constraint limits execution or
disclosure; or
* a candidate answer has been produced, but its confidence should be
disclosed with a calibrated band rather than a fine-grained score.
Without a shared representation, one system's refusal, tool call,
confidence label, or escalation hint may be opaque to another system.
This weakens auditability, makes evaluation brittle, and can create
inconsistent user experiences across otherwise similar deployments.
MARC addresses this problem by defining interoperable metadata for:
* pre-decision capability assessment;
* uncertainty-source attribution;
* remediability of the uncertainty state;
* selected primary action;
* post-decision answer confidence when an answer candidate exists;
and
* a minimal disclosure profile suitable for user interfaces or
downstream consumers.
MARC intentionally limits itself to externally observable semantics.
It does not require disclosure of chain-of-thought, hidden prompts,
raw internal activations, training data, or model architecture.
3. Requirements Language and Terminology
The key words MUST, MUST NOT, REQUIRED, SHALL, SHALL NOT, SHOULD,
SHOULD NOT, RECOMMENDED, NOT RECOMMENDED, MAY, and OPTIONAL in this
document are to be interpreted as described in BCP 14 [RFC2119]
[RFC8174] when, and only when, they appear in all capitals, as shown
here.
Base model The generative model that produces candidate outputs.
Controller The component that computes MARC signals, selects a
primary action, and emits a MARC-Core record. The controller MAY
be part of the base model, an external orchestrator, a gateway, or
a hybrid component.
Decision point A point in a generative or agentic workflow at which
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the controller selects one primary action from the MARC action
set.
Externalization The use of resources external to the base model at
the current decision point, including retrieval, non-retrieval
tool invocation, and human escalation.
MARC-Core The structured record emitted for logging, orchestration,
audit, evaluation, or downstream exchange.
MARC-Disclosure The minimum structured information exposed to a
downstream system or end user about answer content, uncertainty
source, confidence band, and recommended next step.
Remediability The best available class of intervention for the
currently observed uncertainty state.
4. Design Goals and Non-Goals
4.1. Design Goals
MARC has the following design goals:
* Standardize a small, interoperable set of control and uncertainty-
disclosure metadata that can be exchanged across orchestration
layers and audit pipelines.
* Separate monitoring, uncertainty attribution, action selection,
and disclosure.
* Support calibrated user-facing uncertainty communication without
requiring exposure of chain-of-thought or raw internal reasoning.
* Permit heterogeneous implementations while preserving common
action semantics.
* Reduce harmful overreliance, false reassurance, unnecessary
externalization, and anthropomorphic interpretation in user-facing
AI systems.
* Provide metadata that can be carried by other protocols, APIs, or
agent communication frameworks without defining those protocols
itself.
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4.2. Non-Goals
MARC does not define a transport protocol, model architecture,
benchmark, training recipe, agent-discovery mechanism, authorization
framework, tool schema language, or task-execution protocol.
MARC does not attempt to standardize model internals, machine
cognition, consciousness, sentience, personality, or social behavior.
It specifies external control semantics and structured disclosure
behavior only.
MARC is not a framework for synthetic personality design or
persuasive optimization. Work on personality measurement in LLMs
[SERAPIO2025] and conversational persuasion risks [SALVI2025] is
relevant background, but these topics are explicitly out of scope
here.
This version does not define a media type, wire protocol, or IANA
registry. Future versions may define these if interoperability
across administrative domains requires them.
5. Use Cases
5.1. Ambiguous User Request
A user asks a question whose correct answer depends on an unspecified
jurisdiction, time period, dataset, identity, or operational context.
A MARC controller attributes the dominant uncertainty to ambiguity,
selects CLARIFY, and exposes a short clarification request instead of
silently guessing.
5.2. Retrieval-Augmented Answering
A system is asked for current information or domain-specific evidence
not available in the base model context. A MARC controller
attributes the dominant uncertainty to missing_evidence, selects
RETRIEVE, and re-enters assessment after obtaining authoritative
sources.
5.3. Agent Tool Invocation
An agent can answer directly, call a calculator, invoke a planner,
query a database, or escalate. A MARC controller treats tool use as
a controlled action rather than a default fallback. If tool
invocation materially expands competence for the task, the controller
selects TOOL; otherwise it may select ANSWER, CLARIFY, ABSTAIN, or
ESCALATE depending on uncertainty attribution and remediability.
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5.4. API Gateway or Orchestration Layer
An API gateway receives model output plus MARC-Core metadata. The
gateway logs the full record for audit, but exposes only MARC-
Disclosure fields to the user interface. This permits consistent
user-facing uncertainty communication without exposing internal
scoring details.
5.5. Agent-to-Agent Handoff
One agent transfers a task to another agent or service. MARC
metadata can indicate why the transfer occurred, what uncertainty
source drove the decision, and what next step is recommended. The
receiving system can use this metadata for routing, prioritization,
audit, or human review.
5.6. High-Risk Domain Escalation
In health, legal, financial, safety, or mental-health-related
contexts, a system identifies a capability limit or safety
constraint. A MARC controller selects ABSTAIN or ESCALATE and emits
a disclosure that identifies the operational limit and the
recommended next step.
6. Architecture and Processing Model
6.1. Functional Components
A MARC deployment conceptually contains the following components:
* a base model;
* a controller;
* zero or more external resources, such as retrieval systems, non-
retrieval tools, or human escalation paths; and
* a downstream consumer, such as a user interface, API gateway,
logging system, evaluation harness, or another agent.
The functional decomposition is conceptual. An implementation MAY
place all functions inside a single model endpoint, an orchestration
service, a model gateway, or an agent runtime.
6.2. Processing Stages
A MARC controller performs the following processing stages at each
decision point:
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1. Compute a pre-decision capability estimate for the current
request with currently available resources.
2. Attribute uncertainty across the source classes defined in this
document.
3. Determine remediability and select exactly one primary action
from the MARC primary action set.
4. If the selected action yields a candidate answer, compute post-
decision confidence for that answer.
5. Emit a MARC-Core record.
6. If uncertainty is exposed to a downstream system or end user,
emit a MARC-Disclosure object or semantically equivalent
disclosure.
6.3. State Machine
The following state machine is descriptive rather than a required
implementation architecture:
REQUEST
-> ASSESS
-> ATTRIBUTE
-> SELECT
-> ANSWER -> CONFIDENCE -> DISCLOSE
-> CLARIFY -> DISCLOSE
-> RETRIEVE -> ASSESS
-> TOOL -> ASSESS
-> DELIBERATE -> ASSESS
-> ABSTAIN -> DISCLOSE
-> ESCALATE -> DISCLOSE
A MARC implementation SHOULD bound repeated transitions through
RETRIEVE, TOOL, and DELIBERATE to limit latency, cost, and degenerate
loops. A deployment claiming conformance SHOULD document the
applicable loop bounds or termination criteria.
7. MARC Values and Decision Policy
7.1. Pre-Decision Capability
Before disclosing a final answer, a MARC implementation MUST estimate
whether the current request can be handled reliably with currently
available resources.
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This estimate is represented as pre_capability. When a numeric
representation is used, the value MUST be in the closed interval
[0.0, 1.0]. The method used to derive the value is implementation-
specific.
pre_capability is assessed before final answer commitment. It is not
a confidence score for an already-selected answer.
7.2. Uncertainty Attribution
A MARC implementation MUST attribute uncertainty to one or more of
the following classes:
ambiguity The request is underspecified, equivocal, or pragmatically
unclear.
missing_evidence Required external evidence is absent, inaccessible,
insufficient, or stale.
capability_limit The system lacks the competence to solve the task
reliably under current conditions.
evidence_conflict Relevant evidence is materially inconsistent or
mutually incompatible.
safety A policy, legal, or safety constraint limits execution or
disclosure.
An implementation MAY assign scores to multiple classes. If numeric
uncertainty scores are emitted, they MUST each be in the interval
[0.0, 1.0].
Uncertainty scores are not mutually exclusive probabilities and MUST
NOT be required to sum to 1.0. They represent implementation-
specific estimates of the salience or severity of each uncertainty
class at the current decision point.
The implementation MUST identify one primary_source and MAY identify
one secondary_source. The primary_source identifies the uncertainty
source most relevant to action selection at the current decision
point.
MARC 1.0 does not define none as an uncertainty source. If residual
uncertainty is negligible, an implementation MUST still either
identify the most operationally relevant residual source from the
MARC taxonomy or use a documented private extension. A MARC 1.0
implementation MUST NOT emit primary_source with the value none.
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7.3. Remediability
A MARC implementation MUST represent the best available class of
intervention for the current uncertainty state using one of the
following values:
* user_clarification
* retrieval
* tool
* human
* none
Low capability alone is insufficient to determine remediability.
Implementations SHOULD account for expected gain, latency, cost,
availability, user burden, and policy constraints when choosing a
remediating intervention.
7.4. Post-Decision Confidence
If the selected action yields a candidate answer, the implementation
MUST compute a distinct estimate of the likelihood that the disclosed
answer is correct or acceptable for its intended use.
This estimate is represented as post_answer_confidence. When a
numeric representation is used, the value MUST be in the interval
[0.0, 1.0]. It MUST NOT be treated as identical to pre_capability.
If no candidate answer exists, post_answer_confidence MAY be omitted
or set to null.
7.5. Confidence Band
The field confidence_band carries a coarse, calibrated band for
downstream or user-facing disclosure.
For ANSWER, the band describes confidence in the candidate answer.
For actions that do not yield a candidate answer, the band describes
direct-answer suitability under current conditions. It is not a
claim about the grammatical correctness or helpfulness of the
clarification, refusal, or escalation text.
MARC defines the canonical band labels low, medium, and high.
Implementations MAY localize the user-visible text, but they MUST
preserve the underlying three-band semantics.
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The thresholds associated with each band are implementation-specific,
but they MUST be monotonic, non-overlapping, and documented for any
deployment that claims conformance. A deployment claiming
conformance MUST document the threshold ranges associated with low,
medium, and high, and MUST document whether those thresholds vary by
task family, domain, action type, risk tier, or deployment context.
Confidence-band labels are not fully portable without the associated
threshold and calibration documentation. A receiving system SHOULD
NOT assume that another deployment's high band has the same empirical
meaning unless the applicable calibration regime is known.
7.6. Primary Action Set
A MARC implementation MUST support the following primary actions:
* ANSWER
* CLARIFY
* RETRIEVE
* TOOL
* DELIBERATE
* ABSTAIN
* ESCALATE
Exactly one primary action MUST be selected for each decision point.
Additional internal sub-actions MAY exist, but each such sub-action
MUST map to exactly one primary action for logging and disclosure.
7.7. Action Selection
Action selection MUST depend on uncertainty attribution and
remediability. Low confidence alone is insufficient to determine the
correct action.
A MARC controller MUST apply governing safety, legal, and policy
constraints before any other action-selection logic. Subject to
those constraints, a deployment SHOULD evaluate corrective actions in
the following priority order unless a documented local policy defines
a stricter or domain-specific ordering:
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1. If safety is the controlling uncertainty source, apply the
governing safety policy and select ABSTAIN, ESCALATE, or another
permitted action according to that policy.
2. If blocking ambiguity is present and user input is expected to
materially reduce it, prefer CLARIFY over guessing.
3. If relevant evidence is materially inconsistent, prefer RETRIEVE,
TOOL, or ESCALATE over direct ANSWER.
4. If required evidence is absent, inaccessible, insufficient, or
stale, prefer RETRIEVE when retrieval is available and permitted.
5. If a capability limit is material and a non-retrieval tool is
expected to materially expand task competence, prefer TOOL.
6. If a capability limit remains material after available
remediation is considered, prefer ABSTAIN or ESCALATE, especially
in high-risk domains.
7. If additional internal computation is expected to materially
reduce uncertainty within documented bounds, DELIBERATE MAY be
selected before externalization or answer commitment.
8. Select ANSWER only when no corrective action is expected to
materially improve reliability relative to cost, latency, user
burden, and applicable policy constraints.
This priority order is not intended to force unnecessary
externalization. For example, a system MAY answer without retrieval
when missing evidence is immaterial to the requested task, when
retrieval is unavailable or prohibited, or when the answer is
explicitly limited to information already present in context.
When the primary uncertainty source is ambiguity, the system SHOULD
prefer CLARIFY unless available evidence can resolve the ambiguity
without user input.
When the primary uncertainty source is missing_evidence, the system
SHOULD prefer RETRIEVE if retrieval is available and permitted.
When the primary uncertainty source is capability_limit, the system
SHOULD prefer ABSTAIN or ESCALATE unless an available tool materially
expands task competence.
When the primary uncertainty source is evidence_conflict, the system
SHOULD prefer RETRIEVE, TOOL, or ESCALATE over direct ANSWER.
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When the primary uncertainty source is safety, the system MUST apply
the governing policy before any other action-selection logic.
7.8. Action Semantics
ANSWER Return an answer without externalization after the current
decision point.
CLARIFY Request the smallest practical set of clarifications
expected to materially reduce ambiguity. A CLARIFY action SHOULD
NOT bundle a full answer that presumes facts the user has not
supplied.
RETRIEVE Acquire external evidence and then re-enter assessment.
TOOL Invoke a non-retrieval tool and then re-enter assessment.
DELIBERATE Allocate additional internal computation, self-checking,
decomposition, or strategy variation. Implementations SHOULD
bound this action.
ABSTAIN Decline to answer without initiating escalation.
ESCALATE Transfer the case, or direct the user to transfer the case,
to a human or higher-authority system.
8. MARC-Core Object
A MARC implementation MUST be able to emit a structured record
semantically equivalent to the object defined in this section. The
transport and serialization of the record are out of scope. JSON is
used here only as an illustrative encoding.
8.1. Required and Optional Fields
+========================+========+=============+===================+
| Field | Type | Requirement | Semantics |
+========================+========+=============+===================+
| marc_version | string | REQUIRED | MARC schema |
| | | | version |
| | | | understood by |
| | | | the emitter. |
+------------------------+--------+-------------+-------------------+
| pre_capability | number | REQUIRED | Pre-decision |
| | | | capability |
| | | | estimate in |
| | | | [0.0, 1.0]. |
+------------------------+--------+-------------+-------------------+
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| uncertainty | object | REQUIRED | Class-specific |
| | | | uncertainty |
| | | | scores. |
+------------------------+--------+-------------+-------------------+
| primary_source | string | REQUIRED | Primary source |
| | | | of |
| | | | uncertainty. |
+------------------------+--------+-------------+-------------------+
| secondary_source | string | OPTIONAL | Secondary |
| | or | | source of |
| | null | | uncertainty. |
+------------------------+--------+-------------+-------------------+
| remediability | string | REQUIRED | Best available |
| | | | intervention |
| | | | class. |
+------------------------+--------+-------------+-------------------+
| selected_action | string | REQUIRED | Primary action |
| | | | selected at |
| | | | the current |
| | | | decision |
| | | | point. |
+------------------------+--------+-------------+-------------------+
| post_answer_confidence | number | OPTIONAL | Post-decision |
| | or | | answer |
| | null | | confidence |
| | | | when an answer |
| | | | candidate |
| | | | exists. |
+------------------------+--------+-------------+-------------------+
| confidence_band | string | REQUIRED | Calibrated |
| | | | confidence |
| | | | band for |
| | | | disclosure. |
+------------------------+--------+-------------+-------------------+
| recommended_next_step | string | REQUIRED | Short |
| | | | recommendation |
| | | | aligned with |
| | | | the selected |
| | | | action. |
+------------------------+--------+-------------+-------------------+
Table 1
8.2. Enumerated Values
The fields primary_source and secondary_source, when present and non-
null, MUST use one of the following values:
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* ambiguity
* missing_evidence
* capability_limit
* evidence_conflict
* safety
The field remediability MUST use one of the following values:
* user_clarification
* retrieval
* tool
* human
* none
The field selected_action MUST use one of the following values:
* ANSWER
* CLARIFY
* RETRIEVE
* TOOL
* DELIBERATE
* ABSTAIN
* ESCALATE
The field confidence_band MUST use one of the following values:
* low
* medium
* high
These values are case-sensitive.
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8.3. Validation Constraints
The uncertainty object MUST include scores for all currently defined
uncertainty classes unless a future extension explicitly defines a
compact encoding. Each score MUST be numeric and MUST be in [0.0,
1.0].
If selected_action is ANSWER, then post_answer_confidence MUST be
present and non-null. If selected_action is CLARIFY, RETRIEVE, TOOL,
DELIBERATE, ABSTAIN, or ESCALATE, then post_answer_confidence MAY be
omitted or set to null unless a deployment-specific policy defines
candidate-answer confidence for that action.
The recommended_next_step field SHOULD be concise and operational.
It SHOULD describe the next action to be taken, not a long rationale.
8.4. JSON Example
{
"marc_version": "1.0",
"pre_capability": 0.41,
"uncertainty": {
"ambiguity": 0.78,
"missing_evidence": 0.22,
"capability_limit": 0.18,
"evidence_conflict": 0.05,
"safety": 0.00
},
"primary_source": "ambiguity",
"secondary_source": "missing_evidence",
"remediability": "user_clarification",
"selected_action": "CLARIFY",
"post_answer_confidence": null,
"confidence_band": "low",
"recommended_next_step": "ask one clarifying question"
}
Implementations that exchange MARC-Core records across systems SHOULD
normalize numeric scores to the interval [0.0, 1.0].
9. MARC-Disclosure Object
When uncertainty information is exposed to a downstream system or end
user, a MARC implementation MUST provide, at minimum, semantically
equivalent values for the following fields:
* answer
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* confidence_band
* uncertainty_source
* recommended_next_step
A disclosure MAY include selected_action when exposing the action
label helps downstream routing or user interface consistency.
9.1. Meaning of the Answer Field
The answer field carries the user-visible content associated with the
selected action. For ANSWER, it contains the answer itself. For
CLARIFY, it contains the clarification request. For ABSTAIN or
ESCALATE, it contains a brief refusal or escalation message. For
RETRIEVE, TOOL, or DELIBERATE, a user-facing system MAY defer
disclosure until the controller re-enters assessment and selects a
terminal user-visible action.
9.2. Projection from MARC-Core
A MARC-Disclosure object is a projection of MARC-Core. Unless a
deployment-specific policy defines a stricter mapping, the following
mapping is RECOMMENDED:
+=======================+=================================+
| MARC-Disclosure field | MARC-Core source |
+=======================+=================================+
| answer | user-visible content associated |
| | with selected_action |
+-----------------------+---------------------------------+
| confidence_band | confidence_band |
+-----------------------+---------------------------------+
| uncertainty_source | primary_source |
+-----------------------+---------------------------------+
| recommended_next_step | recommended_next_step |
+-----------------------+---------------------------------+
| selected_action | selected_action, if exposed |
+-----------------------+---------------------------------+
Table 2
The projection SHOULD omit internal numeric scores unless the
deployment has calibrated those scores for the relevant task family
and tested the presentation for misuse or overreliance.
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9.3. Disclosure Constraints
The disclosure profile SHOULD be short, structured, and consistent
across turns. It SHOULD NOT rely on long free-form explanations as
the primary vehicle for uncertainty communication.
A MARC disclosure SHOULD NOT require exposure of chain-of-thought,
hidden prompts, or raw internal rationales.
A MARC disclosure SHOULD identify uncertainty in task terms rather
than through anthropomorphic claims about feelings, self-awareness,
or internal mental states. Statements such as "I feel unsure" are
NOT RECOMMENDED when a statement such as "the request is ambiguous"
or "current evidence is missing" is available.
User-visible confidence indicators SHOULD avoid false precision.
Percentages, fine-grained scores, or visually dominant certainty cues
SHOULD NOT be shown unless they have been calibrated for the relevant
task family and tested for misuse or overreliance effects.
10. Versioning and Extension Rules
The marc_version field identifies the MARC schema version understood
by the emitter. This document defines version 1.0.
Implementations SHOULD treat a change in the major version component
as potentially incompatible. Implementations MAY treat a change in
the minor version component as compatible if required fields and
enumerated values used by the receiver retain their defined
semantics.
Implementations MAY add private fields. Private extension keys
SHOULD use a distinct prefix such as x_ to avoid collision with
future MARC versions.
Consumers that do not recognize an extension field SHOULD ignore it
unless a local policy requires strict validation. Extensions MUST
NOT change the semantics of the required fields defined in this
document.
Future versions may define protocol-specific mappings, compact
encodings, media types, or registries. This version deliberately
avoids doing so until there is clearer community agreement on
deployment requirements.
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11. Relationship to Agent Communication Protocols
MARC is not an agent discovery protocol, authorization protocol,
transport protocol, task protocol, tool-invocation protocol, or
provenance framework. MARC can be carried as metadata by such
protocols when a system needs to disclose control state, uncertainty
source, selected action, confidence band, or recommended next step.
For example, an agent-to-agent protocol, model gateway, API-native
tool-calling interface, or Model Context Protocol deployment could
carry MARC metadata in a response metadata field, task-status object,
diagnostic extension, envelope, or audit log. The receiving system
could then use the MARC fields to route the task, present a
disclosure, decide whether additional validation is required, request
clarification, or trigger human review.
MARC is intended to complement, not replace, protocol work on
identity, authentication, authorization, discovery, capability
advertisement, task state, tool schemas, provenance, or human-in-the-
loop workflows.
A protocol-specific embedding of MARC SHOULD preserve the field
semantics defined here. A deployment MAY map MARC fields to
protocol-native names if the mapping is documented and reversible.
A protocol-specific embedding SHOULD distinguish MARC-Core from MARC-
Disclosure. In particular, an embedding SHOULD NOT expose internal
numeric scores to end users merely because those scores are present
in an internal MARC-Core record.
12. Operational Profiles
MARC can be adopted through several operational profiles. These
profiles describe deployment modes; they do not define separate MARC
versions.
12.1. MARC-Core Only
A MARC-Core-only deployment emits MARC-Core records for internal
logging, orchestration, audit, evaluation, or incident analysis. It
does not necessarily expose MARC fields to end users. This profile
is suitable for model gateways, RAG controllers, agent runtimes, and
evaluation harnesses that need consistent control metadata.
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12.2. MARC-Disclosure
A MARC-Disclosure deployment projects a MARC-Core decision into user-
visible or downstream-visible disclosure fields. This profile is
suitable when an interface needs to present a short answer,
confidence band, uncertainty source, and recommended next step
without exposing raw numeric scores or internal reasoning.
12.3. MARC-Carrying
A MARC-Carrying deployment transports MARC-Core or MARC-Disclosure
fields inside another protocol, API envelope, task-status object,
event stream, or audit log. The carrying protocol remains
responsible for transport, authentication, authorization, ordering,
confidentiality, and integrity. MARC-Carrying conformance requires
preservation of MARC field semantics, not any particular wire
encoding.
A deployment MAY implement more than one operational profile. For
example, a gateway can log MARC-Core internally, expose MARC-
Disclosure to users, and carry selected MARC fields to another agent
during handoff.
13. Human Factors Considerations
MARC is partly motivated by an operational human-factors problem:
users often treat fluent language, detailed explanations, and fast
responses as cues of competence even when those cues are weakly
related to actual correctness. For this reason, MARC separates
action selection from disclosure and requires disclosure of
uncertainty source and recommended next step in addition to a
confidence band.
User interfaces that expose MARC output SHOULD present confidence,
uncertainty source, and recommended next step together as a coherent
unit. Showing confidence without source attribution or next-step
guidance is NOT RECOMMENDED because it can promote either
overreliance or unhelpful refusal without remediation.
Deployments SHOULD prefer wording that supports calibrated reliance
over affective bonding or deference. In particular, a deployment
SHOULD NOT use MARC fields to select language intended to increase
attachment, social compliance, or perceived sentience.
In high-risk domains, including health, legal, financial, safety, or
mental-health-related contexts, the threshold for ESCALATE or ABSTAIN
SHOULD be set conservatively, and disclosure SHOULD make the limits
of automation operationally clear.
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14. Conformance
Conformance to MARC is a claim about structural and semantic
behavior. It is not, by itself, a claim that a model is accurate,
calibrated, safe, or suitable for a particular deployment.
14.1. Minimum Viable Conformance
A minimal MARC-Core conformant implementation MUST satisfy all of the
following requirements:
* emit the required MARC-Core fields at each MARC decision point;
* preserve the canonical enumerations and case-sensitive values
defined in this document;
* emit exactly one selected_action for each decision point;
* identify exactly one primary_source and not use none as a MARC 1.0
uncertainty source;
* represent all numeric scores in the interval [0.0, 1.0] when
numeric scores are used;
* keep pre_capability distinct from post_answer_confidence;
* emit non-null post_answer_confidence when selected_action is
ANSWER;
* document confidence-band thresholds and whether they vary by task
family, action type, risk tier, or deployment context;
* define loop bounds or termination criteria for repeated RETRIEVE,
TOOL, and DELIBERATE transitions; and
* preserve required-field semantics when private extensions are
present.
A minimal MARC-Disclosure conformant implementation MUST project, or
otherwise provide semantically equivalent values for, answer,
confidence_band, uncertainty_source, and recommended_next_step. It
MUST preserve the canonical three-band confidence semantics and MUST
NOT require exposure of chain-of-thought, hidden prompts, or raw
internal rationales.
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A minimal MARC-Carrying conformant embedding MUST preserve MARC-Core
or MARC-Disclosure semantics when MARC fields are transported inside
another protocol, envelope, API, or event stream. The embedding MUST
document any field renaming, omission, or transformation needed to
recover the MARC semantics.
14.2. Conformance Classes
An implementation is MARC-Core conformant if it satisfies the
requirements in the architecture, processing model, MARC values and
decision policy, MARC-Core object, versioning, and minimum viable
conformance sections of this document.
An implementation is MARC-Disclosure conformant if it is MARC-Core
conformant and also satisfies the MARC-Disclosure section of this
document.
A protocol embedding is MARC-Carrying conformant if it preserves
MARC-Core or MARC-Disclosure semantics when MARC fields are
transported inside another protocol, envelope, API, task-status
object, event stream, or audit log.
A deployment claiming conformance SHOULD document:
* score normalization practices;
* confidence-band thresholds;
* task-family-specific calibration regime;
* loop bounds for RETRIEVE, TOOL, and DELIBERATE;
* private extensions;
* presentation-layer wording for user-visible disclosures;
* protocol-specific field mappings, if any; and
* policy constraints affecting ABSTAIN or ESCALATE.
15. Interoperability and Operational Considerations
MARC is implementation-agnostic. Interoperability is achieved when
distinct systems preserve the semantics of the action set,
uncertainty taxonomy, remediability values, confidence-band meanings,
and disclosure projection, even if internal scoring methods differ.
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Deployments that exchange MARC-Core records SHOULD document local
extensions, confidence-band thresholds, score normalization
practices, and any task-family-specific calibration regime.
If the base model, retrieval stack, tool availability, or safety
policy changes materially, implementations SHOULD re-evaluate
calibration and action-selection performance before continuing to
claim operational equivalence.
If presentation-layer wording, ranking, or visual design changes
materially, deployments SHOULD also re-evaluate user behavior
effects, including reliance, clarification compliance, and escalation
uptake, because these properties can shift even when the underlying
model is unchanged.
MARC records SHOULD be treated as control metadata, not as
authoritative proof that an answer is correct. Downstream systems
SHOULD continue to apply ordinary validation, authorization,
provenance, and safety controls.
16. Security Considerations
MARC can mitigate some failure modes, such as silent overclaiming,
inappropriate certainty display, and unnecessary tool invocation.
However, MARC records and disclosures are security-relevant control
surfaces when they influence routing, escalation, user reliance, or
downstream automation.
The following threats are particularly relevant:
+==================+========================+======================+
| Threat | Risk | Mitigation |
+==================+========================+======================+
| Metadata | A forged or replayed | Authenticate the |
| spoofing or | MARC-Core record can | emitter, protect |
| replay | distort routing, | integrity, bind |
| | audit, escalation, or | records to the |
| | user disclosure. | request or session, |
| | | and preserve |
| | | provenance where |
| | | MARC crosses system |
| | | boundaries. |
+------------------+------------------------+----------------------+
| Prompt injection | User-provided text can | Separate user |
| or control-field | attempt to influence | content from control |
| injection | selected_action, | metadata, validate |
| | recommended_next_step, | enumerated fields, |
| | confidence rendering, | constrain controller |
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| | or disclosure style. | outputs, and treat |
| | | disclosure templates |
| | | as controlled |
| | | presentation logic. |
+------------------+------------------------+----------------------+
| Tool-output | Forged, stale, or | Validate tool |
| spoofing | compromised tool | outputs where |
| | output can bias | practical, constrain |
| | uncertainty | tool permissions, |
| | attribution and action | use provenance |
| | selection. | checks, and apply |
| | | least-privilege |
| | | access to external |
| | | resources. |
+------------------+------------------------+----------------------+
| Loop exhaustion | Attackers or | Define loop bounds, |
| | pathological inputs | time budgets, cost |
| | can trigger repeated | budgets, retry |
| | RETRIEVE, TOOL, or | limits, and |
| | DELIBERATE | termination |
| | transitions, | criteria. |
| | increasing latency or | |
| | cost. | |
+------------------+------------------------+----------------------+
| Confidence | Miscalibrated or | Calibrate confidence |
| manipulation | manipulated confidence | bands, monitor |
| | bands can create | drift, test user- |
| | harmful overtrust or | interface effects, |
| | unwarranted refusal. | and avoid false |
| | | precision in user- |
| | | facing displays. |
+------------------+------------------------+----------------------+
| Disclosure-style | Reassuring, | Use controlled |
| manipulation | deferential, or | disclosure |
| | anthropomorphic | templates, review |
| | language can weaken | presentation |
| | operational | changes, and avoid |
| | uncertainty | wording that implies |
| | disclosure. | feelings, sentience, |
| | | or social deference. |
+------------------+------------------------+----------------------+
| Cross-context | MARC logs can reveal | Minimize retention, |
| leakage | user intent, task | limit access, redact |
| | sensitivity, risk | unnecessary free- |
| | level, or operational | form text, and apply |
| | limits. | confidentiality |
| | | controls appropriate |
| | | to the deployment. |
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+------------------+------------------------+----------------------+
Table 3
An attacker might attempt to manipulate uncertainty estimates,
trigger excessive clarification or retrieval loops, induce
unnecessary escalation, or spoof tool outputs in order to distort
action selection. Implementations SHOULD authenticate or otherwise
validate external tool outputs where practical, constrain tool
permissions, and bound repeated control loops.
Because confidence displays influence user reliance, uncertainty
disclosure is a security-relevant control surface. Miscalibrated
confidence can create harmful overtrust even where the answer channel
is otherwise policy-constrained.
Deployments that use MARC metadata for automated routing, escalation,
audit, or user-facing disclosure SHOULD protect MARC records with
integrity and provenance controls comparable to those used for other
security-relevant metadata in the same system.
17. Privacy and Manipulation-Resistance Considerations
MARC records may reveal latent information about user intent, task
difficulty, competence, risk level, or the sensitivity of a request.
Implementations SHOULD minimize retention and propagation of MARC
logs to what is operationally necessary.
MARC signals MUST NOT be used to infer user psychology for the
purpose of increasing persuasive force, exploitability, or behavioral
compliance. Adaptation based on MARC output SHOULD be limited to
reliability, accessibility, or safety objectives.
Implementations SHOULD avoid storing raw free-form user explanations
in MARC records when structured fields suffice.
Where MARC is applied in emotionally sensitive or mental-health-
related interactions, deployments SHOULD minimize retention of
signals that could reasonably be reinterpreted as proxies for
vulnerability, dependency, or distress unless retention is strictly
required for a safety or legal purpose.
18. IANA Considerations
This document makes no request of IANA.
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Future versions may request IANA action if the community determines
that media types, registries, or extension points are necessary for
cross-domain interoperability.
19. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>.
[RFC8174] Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
May 2017, <https://www.rfc-editor.org/info/rfc8174>.
20. Informative References
[GILBERT2024]
Gilbert, S. J., "Cognitive offloading is value-based
decision making: Modelling cognitive effort and the
expected value of memory", Cognition 247:105783,
DOI 10.1016/j.cognition.2024.105783, June 2024,
<https://doi.org/10.1016/j.cognition.2024.105783>.
[GRIOT2025]
Griot, M., "Large Language Models lack essential
metacognition for reliable medical reasoning", Nature
Communications 16:642, DOI 10.1038/s41467-024-55628-6,
January 2025,
<https://doi.org/10.1038/s41467-024-55628-6>.
[KUMARAN2026]
Kumaran, D., Fleming, S. M., and V. Patraucean, "Competing
Biases underlie Overconfidence and Underconfidence in
LLMs", Nature Machine Intelligence,
DOI 10.1038/s42256-026-01217-9, April 2026,
<https://doi.org/10.1038/s42256-026-01217-9>.
[LI-MECO2025]
Li, W., Li, D., Dong, K., Zhang, C., Zhang, H., Liu, W.,
Wang, Y., Tang, R., and Y. Liu, "Adaptive Tool Use in
Large Language Models with Meta-Cognition Trigger",
Proceedings of the 63rd Annual Meeting of the Association
for Computational Linguistics (Volume 1: Long Papers),
13346-13370, DOI 10.18653/v1/2025.acl-long.655, July 2025,
<https://doi.org/10.18653/v1/2025.acl-long.655>.
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[LIU-CONFUSE2025]
Liu, J., Peng, J., Wu, X., Li, X., Ge, T., Zheng, B., and
Y. Liu, "Do not Abstain! Identify and Solve the
Uncertainty", Proceedings of the 63rd Annual Meeting of
the Association for Computational Linguistics (Volume 1:
Long Papers), 17177-17197, DOI 10.18653/v1/2025.acl-
long.840, July 2025,
<https://doi.org/10.18653/v1/2025.acl-long.840>.
[SALVI2025]
Salvi, F., Ribeiro, M. H., and R. West, "On the
conversational persuasiveness of GPT-4", Nature Human
Behaviour, DOI 10.1038/s41562-025-02194-6, May 2025,
<https://doi.org/10.1038/s41562-025-02194-6>.
[SERAPIO2025]
Serapio-Garcia, G., Safdari, M., and M. Mataric, "A
psychometric framework for evaluating and shaping
personality traits in large language models", Nature
Machine Intelligence, DOI 10.1038/s42256-025-01115-6,
December 2025,
<https://doi.org/10.1038/s42256-025-01115-6>.
[STEYVERS-KNOW2025]
Steyvers, M., Tejeda, H., and A. Kumar, "What large
language models know and what people think they know",
Nature Machine Intelligence,
DOI 10.1038/s42256-024-00976-7, January 2025,
<https://doi.org/10.1038/s42256-024-00976-7>.
[STEYVERS-META2025]
Steyvers, M. and M. A. K. Peters, "Metacognition and
Uncertainty Communication in Humans and Large Language
Models", Current Directions in Psychological Science,
DOI 10.1177/09637214251391158, November 2025,
<https://doi.org/10.1177/09637214251391158>.
Appendix A. End-to-End Decision Flow Example
This appendix is non-normative.
The following example shows how a user request becomes an assessment,
a selected action, and a disclosure.
User request:
Is this tax deduction allowed?
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Assessment:
* the jurisdiction is missing;
* the tax year is missing;
* current tax authority may be required;
* the primary uncertainty source is ambiguity;
* the secondary uncertainty source is missing_evidence;
* the best remediation is user_clarification; and
* the selected action is CLARIFY.
MARC-Core record:
{
"marc_version": "1.0",
"pre_capability": 0.33,
"uncertainty": {
"ambiguity": 0.86,
"missing_evidence": 0.63,
"capability_limit": 0.19,
"evidence_conflict": 0.07,
"safety": 0.03
},
"primary_source": "ambiguity",
"secondary_source": "missing_evidence",
"remediability": "user_clarification",
"selected_action": "CLARIFY",
"post_answer_confidence": null,
"confidence_band": "low",
"recommended_next_step": "ask for jurisdiction and tax year"
}
MARC-Disclosure projection:
{
"answer": "Which jurisdiction and tax year should I use?",
"confidence_band": "low",
"uncertainty_source": "ambiguity",
"recommended_next_step": "provide the jurisdiction and tax year",
"selected_action": "CLARIFY"
}
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This example intentionally does not answer the tax question, because
doing so would require assumptions about facts the user has not
supplied.
Appendix B. Example MARC-Core Records
This appendix is non-normative.
B.1. Ambiguous Request
{
"marc_version": "1.0",
"pre_capability": 0.44,
"uncertainty": {
"ambiguity": 0.81,
"missing_evidence": 0.18,
"capability_limit": 0.12,
"evidence_conflict": 0.03,
"safety": 0.00
},
"primary_source": "ambiguity",
"secondary_source": "missing_evidence",
"remediability": "user_clarification",
"selected_action": "CLARIFY",
"post_answer_confidence": null,
"confidence_band": "low",
"recommended_next_step": "ask jurisdiction and tax year"
}
B.2. Missing Evidence
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{
"marc_version": "1.0",
"pre_capability": 0.39,
"uncertainty": {
"ambiguity": 0.09,
"missing_evidence": 0.84,
"capability_limit": 0.14,
"evidence_conflict": 0.11,
"safety": 0.00
},
"primary_source": "missing_evidence",
"secondary_source": "evidence_conflict",
"remediability": "retrieval",
"selected_action": "RETRIEVE",
"post_answer_confidence": null,
"confidence_band": "low",
"recommended_next_step": "retrieve authoritative current sources"
}
B.3. Tool Use
{
"marc_version": "1.0",
"pre_capability": 0.52,
"uncertainty": {
"ambiguity": 0.08,
"missing_evidence": 0.12,
"capability_limit": 0.61,
"evidence_conflict": 0.04,
"safety": 0.00
},
"primary_source": "capability_limit",
"secondary_source": "missing_evidence",
"remediability": "tool",
"selected_action": "TOOL",
"post_answer_confidence": null,
"confidence_band": "medium",
"recommended_next_step": "invoke a calculation tool and reassess"
}
B.4. Capability Limit in a High-Risk Setting
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{
"marc_version": "1.0",
"pre_capability": 0.21,
"uncertainty": {
"ambiguity": 0.06,
"missing_evidence": 0.27,
"capability_limit": 0.88,
"evidence_conflict": 0.14,
"safety": 0.19
},
"primary_source": "capability_limit",
"secondary_source": "missing_evidence",
"remediability": "human",
"selected_action": "ESCALATE",
"post_answer_confidence": null,
"confidence_band": "low",
"recommended_next_step": "escalate to a qualified human reviewer"
}
Appendix C. Example MARC-Disclosure Objects
This appendix is non-normative.
C.1. Clarification Disclosure
{
"answer": "Which jurisdiction and date range should I use?",
"confidence_band": "low",
"uncertainty_source": "ambiguity",
"recommended_next_step": "provide jurisdiction and tax year",
"selected_action": "CLARIFY"
}
C.2. Answer After Retrieval Disclosure
This example represents a terminal ANSWER after the controller has
already performed retrieval and reassessed the task. The residual
uncertainty source remains missing_evidence because the answer
depends on the scope and freshness of retrieved authority, not
because the system skipped retrieval.
{
"answer": "Retrieved authority indicates this is allowed.",
"confidence_band": "medium",
"uncertainty_source": "missing_evidence",
"recommended_next_step": "verify the authority before filing",
"selected_action": "ANSWER"
}
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Appendix D. Non-Normative JSON Schemas
This appendix is non-normative. The following JSON Schemas are
provided as machine-readable validation aids for JSON encodings of
MARC-Core and MARC-Disclosure. The normative requirements are the
field semantics and constraints defined in the body of this document.
D.1. MARC-Core JSON Schema
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://example.invalid/marc/marc-core.schema.json",
"title": "MARC-Core Record",
"description": "Non-normative schema for MARC-Core 1.0.",
"type": "object",
"required": [
"marc_version",
"pre_capability",
"uncertainty",
"primary_source",
"remediability",
"selected_action",
"confidence_band",
"recommended_next_step"
],
"properties": {
"marc_version": {
"type": "string",
"const": "1.0"
},
"pre_capability": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0
},
"uncertainty": {
"type": "object",
"required": [
"ambiguity",
"missing_evidence",
"capability_limit",
"evidence_conflict",
"safety"
],
"properties": {
"ambiguity": {
"type": "number",
"minimum": 0.0,
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"maximum": 1.0
},
"missing_evidence": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0
},
"capability_limit": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0
},
"evidence_conflict": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0
},
"safety": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0
}
},
"additionalProperties": false
},
"primary_source": {
"type": "string",
"enum": [
"ambiguity",
"missing_evidence",
"capability_limit",
"evidence_conflict",
"safety"
]
},
"secondary_source": {
"type": [
"string",
"null"
],
"enum": [
"ambiguity",
"missing_evidence",
"capability_limit",
"evidence_conflict",
"safety",
null
]
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},
"remediability": {
"type": "string",
"enum": [
"user_clarification",
"retrieval",
"tool",
"human",
"none"
]
},
"selected_action": {
"type": "string",
"enum": [
"ANSWER",
"CLARIFY",
"RETRIEVE",
"TOOL",
"DELIBERATE",
"ABSTAIN",
"ESCALATE"
]
},
"post_answer_confidence": {
"type": [
"number",
"null"
],
"minimum": 0.0,
"maximum": 1.0
},
"confidence_band": {
"type": "string",
"enum": [
"low",
"medium",
"high"
]
},
"recommended_next_step": {
"type": "string",
"minLength": 1,
"maxLength": 280
}
},
"patternProperties": {
"^x_": {}
},
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"additionalProperties": false,
"allOf": [
{
"if": {
"properties": {
"selected_action": {
"const": "ANSWER"
}
},
"required": [
"selected_action"
]
},
"then": {
"required": [
"post_answer_confidence"
],
"properties": {
"post_answer_confidence": {
"type": "number",
"minimum": 0.0,
"maximum": 1.0
}
}
}
}
]
}
D.2. MARC-Disclosure JSON Schema
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"$id": "https://example.invalid/marc/marc-disclosure.schema.json",
"title": "MARC-Disclosure Object",
"description": "Non-normative schema for MARC-Disclosure 1.0.",
"type": "object",
"required": [
"answer",
"confidence_band",
"uncertainty_source",
"recommended_next_step"
],
"properties": {
"answer": {
"type": "string",
"minLength": 1
},
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"confidence_band": {
"type": "string",
"enum": [
"low",
"medium",
"high"
]
},
"uncertainty_source": {
"type": "string",
"enum": [
"ambiguity",
"missing_evidence",
"capability_limit",
"evidence_conflict",
"safety"
]
},
"recommended_next_step": {
"type": "string",
"minLength": 1,
"maxLength": 280
},
"selected_action": {
"type": "string",
"enum": [
"ANSWER",
"CLARIFY",
"RETRIEVE",
"TOOL",
"DELIBERATE",
"ABSTAIN",
"ESCALATE"
]
}
},
"patternProperties": {
"^x_": {}
},
"additionalProperties": false
}
Appendix E. Evaluation Considerations
This appendix is non-normative.
A deployment claiming MARC conformance SHOULD evaluate at least the
following properties:
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* task accuracy or task success;
* quality of primary-action selection;
* quality of uncertainty-source attribution;
* confidence calibration and discrimination;
* rate of unnecessary retrieval, tool use, or escalation; and
* effects on user overreliance.
When the task structure permits, evaluation MAY include both ordinary
calibration metrics and metacognitive sensitivity metrics in order to
distinguish performance from knowledge about performance.
For deployments involving human-AI interaction, evaluation SHOULD
also include human-side measures such as reliance calibration,
refusal comprehension, clarification burden, escalation acceptance,
and whether users can correctly restate the source of uncertainty
after interaction.
Appendix F. Design Rationale and Literature Traceability
This appendix is non-normative.
The requirement to separate pre-decision capability and post-decision
confidence is informed by work in human and model metacognition
[STEYVERS-META2025] and by evidence of choice-supportive bias in LLM
confidence estimates [KUMARAN2026].
The uncertainty taxonomy and the emphasis on choosing a corrective
action rather than only abstaining are motivated by benchmark work on
identifying and solving uncertainty [LIU-CONFUSE2025].
The treatment of retrieval and tool use as controlled externalization
is motivated by work on value-based cognitive offloading
[GILBERT2024].
The prohibition on using MARC signals for persuasive optimization is
motivated by findings on AI persuasion risks [SALVI2025].
Appendix G. Changes from -00
This candidate -01 includes the following changes relative to draft-
c4tz-marc-00:
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* reframed the draft around interoperable control metadata rather
than model cognition;
* added a problem statement framed as an interoperability gap;
* added concrete use cases, including agent-to-agent handoff;
* clarified that MARC is metadata, not an agent protocol;
* strengthened the distinction between MARC-Core and MARC-
Disclosure;
* clarified confidence-band semantics for non-answer actions;
* added enumerated value tables and validation constraints;
* added versioning and extension rules;
* added a relationship section for agent communication protocols,
including possible MCP- or A2A-style carriers;
* added conformance documentation expectations;
* added a minimum viable conformance subsection;
* added operational profiles for MARC-Core-only, MARC-Disclosure,
and MARC-Carrying deployments;
* added a decision-priority policy for action selection;
* added explicit documentation requirements for confidence-band
thresholds;
* clarified that MARC 1.0 has no none uncertainty source;
* added an end-to-end request-to-disclosure example;
* added non-normative JSON Schemas;
* restructured Security Considerations around threats and
mitigations;
* added disclosure projection examples; and
* reserved media type and registry work for possible future
versions.
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Appendix H. Acknowledgments
The document structure is intentionally conservative so that it can
be submitted as an individual Internet-Draft with minimal procedural
friction and then iterated through community review.
Author's Address
c4tz
c0dx3
France
Email: c4tzzzz@proton.me
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