Armando Da Silva Vieira, Lecturer of Applied AI

The dominant paradigm in modern AI rests on a deceptively simple premise: intelligence emerges from minimizing prediction error across vast datasets. We train neural networks to be accurate, to match patterns, to reduce loss functions. Yet this approach, for all its empirical success, may be fundamentally misaligned with the nature of intelligence itself. Here is my claim: The brain doesn’t minimize error. It maximizes coherence.

The Coherence Imperative

True intelligence — the kind that generalizes robustly, that distinguishes sense from nonsense, that builds understanding rather than merely matching patterns — emerges not from eliminating mistakes, but from constructing and continuously refining multi-scale coherent models of reality. These are models that aren’t just statistically accurate, but meaningful. Coherent models are not simply statistically consistent; they preserve meaningful relationships, maintain internal unity across perception and action, and exhibit explanatory adequacy.

Consider a simple thought experiment: A lookup table achieves zero error on its training set. It is also incapable of thought. You can be perfectly accurate and profoundly stupid. When you encounter a photograph of a giraffe with zebra stripes, you immediately recognize something is wrong — not because you’ve memorized every invalid animal configuration, but because it violates your coherent understanding of how species, genetics, and biology work. This is coherence detection: the identification of inputs that fail to fit the unified narrative of how reality operates.

A Fundamental Reorientation

The Coherence Alignment Paradigm proposes three critical shifts:

From minimizing prediction error → To discovering and maintaining invariant structure across time, modality, and scale

From statistical pattern-matching → To energy-efficient sense-making

From narrow task accuracy → To robust, generalizable understanding

This is not a return to the brittle symbolic AI of the 1980s with its hand-crafted rules and rigid logic. I do not reject learning from data but the mechanism by which current systems learn.

The difference between pattern-matching and sense-making is not merely semantic — it represents a divergence in how systems engage with reality. Statistical pattern-matching operates through correlation. Given sufficient data, a system learns that certain inputs reliably predict certain outputs. It discovers that pixels arranged in particular configurations correspond to the label “cat,” that certain word sequences predict the next token, that specific acoustic patterns map to phonemes. This approach is fundamentally reactive: the system responds to statistical regularities in its training distribution without necessarily understanding why those regularities exist or when they should break down.

Energy-efficient sense-making, by contrast, is generative and explanatory. It constructs internal models that capture the causal, structural, and relational fabric of reality — models that can answer “why” and “what if” questions, that know their own boundaries, and that allocate computational resources based on genuine uncertainty rather than exhaustive processing.

Consider a modern large language model predicting the next word in a sentence. It processes every token through billions of parameters, computing attention weights across the entire context window, activating vast neural pathways to determine that “The cat sat on the ___” should probably be completed with “mat” or “floor.”

A human child, by contrast, expends minimal cognitive effort on this task. The sentence activates a compact, coherent model: cats are physical objects, they interact with gravity, they rest on surfaces. The prediction emerges almost effortlessly from this structured understanding. The child reserves expensive, deliberate thought for genuine surprises: “The cat sat on the quantum superposition” — now that requires sense-making.

The difference is not just efficiency — it’s architectural. The pattern-matcher must maintain statistical associations for every possible context. The sense-maker maintains a sparse, structured model of how the world works and uses it to generate expectations.

What Energy Efficiency Forces

When energy becomes a first-class constraint — not something to optimize after the fact, but a fundamental design principle — several critical capabilities must emerge:

1. Selective Attention and Processing

The system cannot afford to process everything deeply. It must develop mechanisms to rapidly assess:

  • Is this situation familiar or novel?
  • Does this input cohere with my current model?
  • Where should I allocate expensive computation?

This drives the emergence of a two-tier architecture: fast, low-energy pattern recognition for routine situations, and slow, high-energy deliberation for genuine anomalies. Not because we engineer it explicitly, but because energy constraints make exhaustive processing untenable.

2. Compressed, Reusable Representations

Storing every observed pattern is energetically prohibitive. The system must discover abstractions — compact representations that capture invariant structure across many instances.

Rather than memorizing millions of examples of objects falling, the system learns a single, reusable principle: “unsupported objects accelerate downward.” This principle compresses vast experience into a tiny, energy-efficient model that generalizes to novel situations.

This is why analogical reasoning becomes central. As structure-mapping research demonstrates, human cognition excels at extracting relational structure from one domain and applying it to another. “Electricity flows through circuits like water flows through pipes” isn’t just a pedagogical trick — it’s a demonstration of how compressed, structural knowledge enables efficient transfer without relearning from scratch.

3. Coherence as Computational Shortcut

Checking coherence is energetically cheaper than exhaustive verification.

When you read “The surgeon operated on her son,” you don’t laboriously verify every possible interpretation. Your coherent model of reality — surgeons are medical professionals, they perform operations, people have children — instantly resolves ambiguity. The sentence makes sense, so minimal processing is required.

But “The surgeon operated on her son, but the surgeon was not the boy’s mother” creates incoherence with common assumptions. Now you must engage expensive deliberation to resolve the conflict, updating your model to accommodate female surgeons and challenging implicit biases.

Coherence checking acts as a gating mechanism: coherent inputs flow through fast, cached pathways; incoherent inputs trigger deeper, more costly analysis.

4. Knowing What You Don’t Know

Pattern-matchers fail silently. They generate predictions even in domains far outside their training distribution, with no signal that these predictions are unreliable.

Energy-efficient sense-makers must develop epistemic awareness — knowing the boundaries of their own models. When a situation falls outside the scope of learned invariants, the system should recognize its own uncertainty and either:

  • Refuse to make confident predictions
  • Allocate additional computational resources
  • Seek new information to resolve ambiguity

This metacognitive capability emerges naturally when energy is constrained: the system learns that confident, low-energy predictions in unfamiliar domains lead to costly errors, while acknowledging uncertainty and engaging deliberation improves outcomes.

The Architectural Implications

Energy-efficient sense-making demands fundamentally different architectures:

Pattern-matchers are homogeneous: every input receives similar processing depth, every parameter contributes to every decision, computation scales with input size.

Sense-makers are heterogeneous: they maintain multiple processing pathways with different energy profiles, route inputs based on coherence and familiarity, and dynamically allocate resources based on genuine need.

Pattern-matchers learn by adjusting weights to minimize error across a dataset.

Sense-makers learn by discovering invariant structure, building causal models, and refining their understanding of when those models apply.

Pattern-matchers scale by adding more parameters, more data, more compute.

Sense-makers scale by discovering deeper invariants, building more abstract representations, and improving their ability to recognize when existing models suffice versus when new learning is required.

Why This Matters

The shift from pattern-matching to sense-making is not about making AI more “brain-like” for aesthetic reasons. It’s about building systems that:

  • Generalize robustly beyond their training distributions by understanding underlying structure rather than memorizing surface statistics
  • Fail gracefully by recognizing the boundaries of their own knowledge
  • Learn efficiently by discovering reusable principles rather than accumulating isolated examples
  • Operate sustainably without requiring data centers to power every inference
  • Explain themselves through coherent models rather than inscrutable correlations

The brain’s 20-watt budget isn’t a constraint it overcomes — it’s a constraint that shapes the nature of biological intelligence. Energy efficiency forces sense-making. It forces abstraction. It forces the construction of coherent, structured models of reality. Perhaps our artificial systems should face the same constraint.

When we force AI to make sense of the world efficiently, we force it to actually make sense of the world. The energy constraint and the intelligence constraint may, ultimately, be the same constraint.

Three Foundational Principles

1. Coherence Over Correctness

Intelligence requires the ability to ask “Does this make sense?” before asking “Is this accurate?” We must build systems that seek elegant, parsimonious explanations over complex, piecemeal corrections. Systems that value internal consistency and explanatory power — that prefer theories which are beautiful in their coherence.

This mirrors how scientific reasoning operates: we don’t simply accumulate facts, we construct frameworks that unify observations, predict novel phenomena, and maintain logical consistency. As research in analogical reasoning demonstrates, human cognition excels at structure-mapping — finding deep relational correspondences between domains that enable transfer of knowledge and insight. Intelligence is fundamentally about building and manipulating these structured, coherent representations.

2. Energy Efficiency as Fundamental Constraint

The brain’s 20-watt miracle is not merely an engineering challenge. It is a profound clue about intelligence itself.

Biological intelligence achieves efficiency through sparse, selective processing. The brain employs fast, cached intuitions for routine situations and reserves expensive deliberation for genuine novelty. It knows when to trust pattern recognition and when to engage effortful reasoning.

This isn’t a limitation — it’s a feature. Energy constraints force the development of robust, reusable models rather than exhaustive memorization. They drive the emergence of abstraction, generalization, and common sense. We must design AI systems with energy as a first-class constraint, not an afterthought. Systems that learn when computation justifies its cost, that develop intuitive “fast paths” for familiar contexts, and that allocate deep processing strategically.

3. Learning from Invariants, Not Supervision

Current deep learning treats the world as a collection of labeled examples. The Coherence Paradigm treats the world as a source of invariant regularities waiting to be discovered.

The fast, intuitive components of intelligence should not be trained through supervised learning. They should learn by discovering what never changes — the deep structure of reality that remains constant across contexts:

Physical invariants: Object permanence, impenetrability, consistent lighting, gravity’s directionality

Causal invariants: Actions precede effects, interventions have predictable consequences, temporal ordering of cause and effect

Social invariants: Gaze direction indicates attention, emotional expressions correlate with contexts, communication follows structured patterns

These aren’t programmed. They’re discovered because violations create massive incoherence in the system’s temporal and cross-modal predictions. The system learns them because reality keeps teaching the same lessons, repeatedly, in every context.

The Path Forward

Building coherence-driven AI requires rethinking our fundamental architectures, learning objectives, and evaluation metrics. It demands systems that:

  • Actively detect and reject incoherent inputs
  • Discover and exploit invariant structure across modalities
  • Allocate computational resources based on novelty and uncertainty
  • Build structured, relational representations amenable to analogical reasoning
  • Maintain consistency across multiple scales of abstraction

This is not incremental improvement. It is a fundamental reconceptualization of what we’re trying to build and why. The question is not whether our systems can achieve high accuracy on benchmarks, but whether they can construct meaningful, coherent models of reality — models that support genuine understanding, robust generalization, and efficient reasoning.

The brain doesn’t minimize error. Perhaps it’s time our artificial systems stopped trying to.

References

The article was published on Medium, a digital publishing platform.