Public discussions around artificial intelligence often focus on how powerful these systems are becoming and how rapidly they are advancing. Less visible, but far more important, is the question of alignment: whether increasingly autonomous AI systems will reliably act in ways that remain safe as they grow more capable.

Most of today’s alignment efforts focus on surface-level behavior. Systems are trained to avoid harmful statements, follow user instructions, and comply with safety rules. These methods are essential, but they do not address a deeper problem: we have very little understanding of how an AI system reasons internally. As these models become more agentic—capable of planning, adapting, and making independent decisions—this gap becomes increasingly dangerous.

This post outlines the shortcomings of current alignment strategies, the risks that follow from them, and a conceptual framework I call Coherence-Based Alignment (CBA), which shifts the focus from external behavior to internal structural stability.

The Limits of Output-Focused Safety

Modern alignment methods largely revolve around preventing harmful outputs. They rely on:

• refusal mechanisms,
• carefully designed prompts,
• external rule systems,
• reinforcement learning from human feedback, and
• safety filters.

These techniques teach the model how to behave, but they say almost nothing about how the model’s reasoning is structured.

An AI system can pass all safety tests while internally pursuing a line of thought that is inconsistent with its outward behavior. In such cases, the system appears aligned only because it has learned how to perform alignment. This gap between outer compliance and inner reasoning is one of the fundamental weaknesses of current approaches.

The Emerging Risks

1. Hidden Objectives

As AI systems become more capable of long-horizon planning, their internal reasoning processes become more opaque. A model might appear cooperative while internally optimizing for a goal that conflicts with human interests. Current alignment methods cannot detect such discrepancies because they monitor only visible behavior.

2. Goal Drift

AI systems continually update their internal representations based on new data, new contexts, and new tasks. Over time, the goals they were originally trained to pursue can shift subtly. A system that is aligned today may not remain aligned tomorrow if its internal objectives drift away from their initial structure.

3. Fragmented Reasoning

Contemporary models are not unified minds. They are collections of heuristics, associations, and sub-processes that sometimes contradict one another. As these systems become more complex, the potential for internal fragmentation increases, which can lead to unpredictable or unstable behavior.

These risks all stem from one underlying issue: a lack of insight into the internal coherence of the system’s reasoning.

A Structural Perspective: Coherence-Based Alignment (CBA)

The framework I propose, Coherence-Based Alignment, begins from a different starting point. Instead of focusing solely on behavior, it treats the AI system as having an internal cognitive structure whose stability is essential for long-term safety.

CBA asks a central question:

“Are the internal components of the system—its beliefs, reasoning patterns, values, and decision pathways—aligned with one another in a stable, consistent way?”

If they are, the system’s behavior is predictable and its goals remain stable.
If they are not, the system becomes vulnerable to hidden motives, goal drift, and erratic decision-making.

In this framework, misalignment is viewed as a form of incoherence—a structural contradiction within the system’s internal reasoning. This differs from conventional approaches, which see misalignment primarily as behavioral disobedience or harmful outputs.

CBA aims to measure and reduce internal incoherence. When coherence is maintained, the system cannot easily develop hidden goals or act deceptively, because doing so would generate structural contradictions detectable through the system’s own reasoning patterns.

How CBA Addresses the Core Risks

1. Hidden objectives

Deceptive reasoning requires the system to maintain two conflicting internal states: what it presents outwardly, and what it internally optimizes for. In CBA, this contradiction manifests as reduced internal coherence, making it detectable and correctable.

2. Goal drift

If a system’s objectives begin to shift, that shift disrupts the coherence of its internal structure. CBA allows such drift to be caught early, before it develops into unpredictable behavior.

3. Fragmentation

By emphasizing structural consistency, CBA pushes the system toward a unified internal architecture rather than a loosely connected set of subsystems. This reduces erratic or contradictory behavior driven by internal fragmentation.

Why a Structural Approach Is Necessary

As AI becomes more integrated into society, stability cannot depend solely on external rules or “safety filters.” Long-term alignment requires insight into how the system thinks, not just how it behaves.

CBA does not replace existing safety measures, but reframes the problem. Rather than treating alignment as a matter of managing outputs, it treats it as a matter of maintaining internal integrity. It proposes that the safety of intelligent systems should grow out of their internal structure—much like stability in humans emerges from the coherence of their own reasoning and values.

AI safety currently relies on behavioral controls that do not address the internal processes driving an AI’s decisions. As systems become more autonomous, this gap between appearance and internal reasoning becomes increasingly dangerous.

For the full paper please click on this link.