The short answer: They didn’t. LLMs don't think. What appears as reasoning is the resolution of constraints in structured fields.

Why Are We so Mystified by How LLMs Work?

Much of the published research on LLMs, including articles from established journals like Computational Linguistics, fails to provide structural models of how language models work. Instead, it relies on observations (LLMs sometimes “hallucinate”), normative prescriptions (“we need better training data”), or speculative hopes (“diversity might improve outcomes”).

But none of these statements rest on an articulated causal theory. Why should better data help, if the architecture cannot reason? What exactly counts as “reasoning,” and where does it emerge from?

Instead of symbolic logic, I argue LLMs operate through constraint resolution across token fields — a defining characteristic of field-based intelligence. This concept underpins the model's ability to appear to reason, despite lacking internal world models or goals.

The inference in LLMs is constraint collapse in a high-dimensional semantic field. This is not speculative. It is the closest descriptive analogue to what transformer math implements:

  • Embeddings = token states in a semantic field
  • Attention = weighted constraints reshaping local fields
  • Layer stacks = multi-resolution refinement of coherence
  • Output = coherent vector collapse to token space
  • And the instructions on how transformers calculate are structurally encoded in language and LLM learn the pattern of this structure and encode it in their weights. Language isn’t just symbolic — it encodes procedures: logic, recursion, conditionals, and constraints. These mirror computational structure.

The Natural Language Processing (NLP) field continues to propose solutions without defining the problem. If the issue is reliability, it is a question of statistical behavior, not bias. If the issue is hallucination, then one must ask: why are we surprised that a system with no grounding invents plausible-sounding errors?

Until we define what reasoning is structurally — and what its absence means — we will mistake fluency for cognition and correction for learning.

My previous articles The Theory of Between and Field-Based Intelligence already explained that human cognition and language model dynamics are structurally isomorphic processes governed by constraint resolution within a tensor field. Token prediction can be accurately modelled as a process of "coherence collapse," wherein possible interpretations converge to a singular, contextually coherent outcome. A symbol gains meaning by where and when it appears. A given language has a finite number of symbols which are arranged sequentially to encode communication. This sequential ordering can be seen as travelling through a semantic field under constraints. Therefore, symbolic systems require rules that define sequences: where they begin, where they end, and how they proceed. We use symbols to encode concepts, often conflated with "words," but the correct unit is the minimum set of symbols required to initiate communication. Words shift in meaning with context, because language structurally encodes reasoning. LLMs approximate reasoning because language, used correctly, reflects structured human inference.

More details can be found here.