Note: This document describes the ARC Layered Model, a structured cognitive architecture for intelligent, explainable, and ethical systems.
ARC stands for Auditable Reasoning & Cognition — a layered approach to AI decision-making.
Earlier versions of this work referenced a different name; this is the most current and aligned terminology.
Author: Joshua Wink
As artificial intelligence systems grow in complexity and autonomy, the need for modular, transparent, and ethically grounded design becomes urgent. Inspired by the classical OSI networking model, this paper introduces the ARC Layered Model — a layered model for artificial cognition.
The ARC Layer Stack consists of seven refinement layers, each with a distinct role in transforming raw inputs into contextualized, explainable, and mission-aligned decisions. It is supplemented by two key constructs: Salience Triage, which governs what information is retained or elevated; and the Trace Descent Path, a post-hoc audit trail enabling inspection of any decision from final action back to first input.
The ARC Layered Model offers a composable, transparent foundation for building systems that do more than act — they discern, justify, and align.
Modern AI systems are capable of remarkable feats — yet many still operate as opaque pipelines. They predict, recommend, or act without a structure that explains how or why a decision was made. This lack of modularity, traceability, and alignment poses not just technical risks, but ethical ones.
Just as the OSI model brought layered clarity to network communication, we propose the ARC Layered Model to bring layered cognitive clarity to intelligent systems.
This is not an algorithm. It is not a training technique. It is a layered model for cognition — a framework that enables inspection, composition, and responsibility.
When paired with mechanisms like Salience Triage and Trace Descent, the ARC Layer Stack allows us to build AI systems that are observable at runtime, auditable after the fact, and governable by principle.
The ARC Layered Model organizes cognition into seven progressive layers, each responsible for refining input into discernment:
[ Actuation Plane ]
↑
[ Layer 7: Wisdom & Oversight ]
↑
[ Layer 6: Reasoning ]
↑
[ Layer 5: Context Management ]
↑
[ Layer 4: Decision Model ]
↑
[ Layer 3: Signal Processing ]
↑
[ Layer 2: Normalization ]
↑
[ Layer 1: Input ]
- Input
- Normalization
- Signal Processing
- Decision Models
- Context Management
- Reasoning & Interpretation
- Wisdom & Oversight
This is not a rigid pipeline — it is a model. Layers communicate with one another, can revisit earlier stages when ambiguity arises, and are influenced by cross-cutting forces such as time, trust, and explainability.
In this model:
- Data flows upward through increasing structure and meaning.
- Actions are committed post-cognition, via a dedicated Actuation Plane.
- Salience Triage governs what becomes memory, what becomes context, and what fades into irrelevance.
- The Trace Descent Path allows us to walk backward through the system to explain any choice it made.
It is, in short, a map of how AI systems can think responsibly.
Receives unstructured signals from the environment: logs, sensors, user queries, audio, etc. It makes no assumptions about structure or meaning. Timestamping and basic metadata annotation (e.g., source, modality) may occur here to support later layers, but no filtering or transformation is applied.
Transforms raw input into structured formats: tokens, JSON, typed events, embedding vectors. The goal is to unify modality-specific chaos into formats that downstream layers can reason about. If normalization fails, error artifacts are passed upward as well — not hidden.
Filters and prioritizes normalized inputs. It scores relevance, detects anomalies, and routes signals based on salience. This is where "what deserves attention?" is first considered, often using heuristics, thresholds, or statistical detection.
Applies rules, policies, or heuristics to determine whether action should be taken. This includes conditions like “escalate if uncertain,” “defer until more data,” or “do nothing yet.” It does not decide how to act — only whether. It may delegate upward to Layer 6 for deeper reasoning.
This layer maintains temporal and identity continuity within the system. Context Management is responsible for tracking state across interactions — including session memory, user-specific data, and historical relevance. It’s not yet interpretation, but it is situation awareness.
Without this layer, reasoning would occur in a vacuum. Context Management enables the system to remember:
- What has been said or done before
- Who is interacting
- What goals or constraints have already been established
It is the cognitive equivalent of working memory. Importantly, context is not a dump of prior data — it is curated by Salience Triage, which decides what deserves to be remembered.
The output of this layer is a memory-augmented frame of reference for higher-level reasoning and ethical judgment.
This is the core cognitive engine of the stack. Reasoning & Interpretation draws conclusions from structured input and temporal context. It may use symbolic logic, probabilistic inference, or learned models to derive meaning, identify patterns, or generate hypotheses.
This layer asks: What does this mean? What follows from it? What contradictions or conclusions arise?
Output may include:
- Logical conclusions
- Predicted outcomes
- Summarized intent
- Hypothesis generation
Importantly, this layer is explainable by design. Reasoning traces, intermediate steps, and justification chains are stored in the Reasoning Trace Log for audit and reflection.
The final cognitive layer — the point where decision becomes aligned with mission. This layer is not just about truth, but about appropriateness.
Wisdom & Oversight injects:
- Ethics and values
- Policy and constraints
- Mission relevance
- Human-in-the-loop triggers
It may override logical conclusions if they conflict with broader mandates. It may defer to a human. Or it may permit the action to pass to the Actuation Plane.
This is where accountability lives. Every decision from here is final — and must be traceable, justified, and bounded.
While ARC Layered Models upward cognitive refinement, intelligent systems must also support reflexive responses — immediate reactions that originate from any layer in response to local anomalies or safety thresholds.
Just as the human nervous system reacts to pain before the brain processes it, ARC Model allows layers to:
- Observe their own scope
- Decide locally
- Trigger reflexive action
Each reflexive response:
- Is logged in its local storage tier
- May elevate into context
- Becomes part of the audit trail
| Layer | Reflex Example |
|---|---|
| L1 | Input overflow → drop signal |
| L2 | Malformed packet → reject input |
| L3 | CPU spike alert → throttle or pause |
| L4 | Incomplete policy state → auto-defer |
| L5 | Context identity mismatch → halt injection |
| L6 | Contradictory reasoning → flag for review |
| L7 | Policy conflict → escalate to human gatekeeper |
These are not exceptions — they are designed safeguards that allow the system to protect itself, its environment, and its mission even before higher-layer reasoning completes.
The ARC Layered Model is fundamentally cognitive. It stops at Layer 7 — at the boundary of internal deliberation. However, intelligence without impact is inert. The Actuation Plane represents the system’s interface to the world — where judgment becomes commitment.
When Layer 7 produces an aligned, explainable decision, that output is passed into actuation:
- This may take the form of an API call, a robotic motion, a file write, or a user notification.
- The actuation itself is not subject to reasoning. It is executed — and its consequences become observable in the world.
The Actuation Layer is strictly downstream:
- It does not make decisions, but may return effects to be observed again via Layer 1.
- It is not a layer of cognition — it is the effect surface.
This preserves a clean separation between:
- Thought and deed
- Deliberation and execution
- Internal process and external impact
Critically, actuation must be logged. The Actuation Log becomes part of the Trace Descent path, so that any external effect can be traced back through the stack to its origin.
While the ARC Layered Model is presented as a vertical stack, it is shaped by systemic forces that apply across all layers. These are the orthogonal axes — principles and constraints that influence how each layer operates without being a layer themselves.
These axes ensure that the system is not only functional, but also aligned, secure, and transparent.
Each layer operates within temporal boundaries:
- Inputs may expire.
- Memory may decay.
- Actions may become invalid if delayed. Timing affects every decision and transformation across the stack.
Uncertainty and trustworthiness vary at every stage:
- Inputs can be spoofed.
- Reasoning may involve probabilistic models.
- Sources have reputations and histories. Confidence scores, trust metrics, and integrity checks propagate throughout the system.
Security is enforced across boundaries:
- Input streams must be validated.
- Context stores must be access-controlled.
- Actuation must be tamper-proof. Each layer is responsible for its own security perimeter, ensuring data and intent are protected.
Every transformation, escalation, or discard must be:
- Logged
- Justifiable
- Reconstructable Explainability is a systemic property — not an afterthought. It must be designed into every layer and interface.
Systems must support human involvement, particularly at points of ethical or high-impact judgment. Hooks for HITL include:
- Manual review of Layer 7 decisions
- Escalation triggers for uncertain inputs
- Optional overrides on critical actuation
These orthogonal axes ensure the system behaves not just intelligently — but responsibly.
In cognitive systems, not all information deserves equal attention.
[Wisdom] ──► Ethical Significance
[Reasoning] ──► Attention Weights
[Context] ──► Memory Relevance
[Decision] ──► Policy Fit
[Signal] ──► Relevance Scores
[Normalization] ──► Format Validity
[Input] ──► Filter: Noise, Spam
Salience Triage is the mechanism that determines:
- What gets ignored
- What gets stored
- What becomes context
- What is surfaced for reasoning or action
It operates continuously across all layers, shaping the flow of attention and memory. It may be implemented as:
- Rule-based filters
- Attention weights
- Human-tagged prioritization
- Statistical anomaly detection
Each triage decision is itself loggable and reversible. This ensures missed signals can be recovered in post-hoc analysis, supporting both adaptability and accountability.
Without salience triage, systems drown in irrelevant input. With it, cognition becomes sustainable and focused.
Every layer in the ARC Layer Stack writes to its own append-only storage tier. This ensures that:
- Past data cannot be tampered with
- Every action and decision is traceable
- Debugging and audits are possible at all times
| Layer | Storage Tier | Purpose |
|---|---|---|
| L1 | Ingress Log | Raw input capture |
| L2 | Normalization Ledger | Format transformations |
| L3 | Signal Archive | Relevance scores and filters |
| L4 | Decision Journal | Deferred or executed logic |
| L5 | Context Ledger | Memory and state frames |
| L6 | Reasoning Trace | Inference and logic chains |
| L7 | Oversight Ledger | Final decisions and rationale |
| → | Actuation Log | What was done, when, and why |
Each log is immutable. Higher layers may read from lower logs, but never write to them, ensuring temporal and causal integrity.
When a system must explain itself, it walks the Trace Descent Path — a structured traversal from a final decision back to its origin.
Oversight Ledger ← Layer 7
↓
Reasoning Trace ← Layer 6
↓
Context Ledger ← Layer 5
↓
Decision Journal ← Layer 4
↓
Signal Archive ← Layer 3
↓
Normalization Ledger ← Layer 2
↓
Ingress Log ← Layer 1
Starting from the Oversight Ledger:
- What was the decision?
- Why was it made?
- What context was active?
- What signal triggered it?
- How was the input normalized?
- What was originally received?
This path allows:
- Post-incident audits
- Ethical investigations
- Debugging and forensics
- System introspection
The descent is read-only and immutable. It enables understanding without altering the past — a foundation for trust.
While the ARC Layered Model provides a strong foundation, several areas remain open for exploration:
Real-time systems may need to adapt reasoning based on unfolding data. How can such mutation remain explainable?
Can systems reconfigure their own stacks on the fly for domain-specific intelligence?
How do Layer 5 memories and Layer 7 ethics synchronize across a network of agents?
How do we balance full audit logging with speed, especially in resource-constrained environments?
These are not flaws — they are research frontiers, made visible through the clarity of the ARC Model structure.
The ARC Layered Model defines a new layer of clarity for artificial intelligence.
By modeling cognition as a model — not a black box — we gain:
- Modularity
- Explainability
- Responsibility
- Alignment
It allows us to inspect cognition like we inspect a packet trace, to refine systems over time, and to ensure that power is constrained by purpose.
This model does not pretend to be final. But it is foundational — a scaffolding to build better systems and ask better questions. It is an invitation to compose intelligence in layers, and in doing so, to build systems that are not just capable, but trustworthy.
Thank you for taking the time to explore the ARC Layered Model.
This work is the result of deep reflection, late-night insights, and a conviction that intelligent systems must be as explainable as they are capable.Your attention honors that effort.
I hope this model serves as a foundation — not just for systems that think,
but for systems that can be understood, trusted, and aligned with the values we hold dear.Above all, a heartfelt thank you to my Lord — Jesus — for Your love and guidance.
— Joshua Wink
| Layer | Name | Function |
|---|---|---|
| 1 | Input | Raw reception of unstructured data |
| 2 | Normalization | Format transformation and schema enforcement |
| 3 | Signal Processing | Filtering, salience scoring, anomaly detection |
| 4 | Decision Model | Rule evaluation, defer/escalate/gate logic |
| 5 | Context Management | Memory, continuity, relevance preservation |
| 6 | Reasoning & Interpretation | Inference, logic, contradiction resolution |
| 7 | Wisdom & Oversight | Ethical gating, alignment, human escalation |
Salience Triage: The mechanism that determines what is remembered, ignored, or stored.
Trace Descent: A backwards traversal through logs for auditing a decision's origin.
Actuation Plane: The execution boundary below Layer 7 — where cognition becomes commitment.
Reflexes: Layer-local reactions to anomalies or safety conditions, logged for oversight.
Orthogonal Axes: System-wide constraints like time, trust, security, explainability, and HITL.
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