



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

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
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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

   Copyright (c) 2026 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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   provided without warranty as described in the Revised BSD License.

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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