A Misplaced Effort

The recent push toward multi-step reasoning, self-reflection, and autonomous agents has become a central focus of large language model research.

Enormous budgets are allocated to:

  • chain-of-thought prompting,
  • retrieval-augmented generation,
  • tool orchestration,
  • and layered memory architectures.

Yet the fundamental question is rarely addressed:

What kind of reasoning is this supposed to be?

A Formal Boundary, Not an Engineering Gap

Gödel’s incompleteness theorems are often cited as abstract results in mathematical logic.

In practice, they establish a boundary: no sufficiently expressive formal system can fully ground its own meaning or truth.

Attempts to produce semantic closure through recursive self-questioning within a token-based system do not overcome this boundary.

They merely circulate within it.

This is not a solvable optimization problem.

Three Structural Errors

The prevailing LLM and agent paradigm rests on three foundational misidentifications.

1. Category Error: Tokens as Reasoning

Token prediction is treated as semantic reasoning.

It is not.

Statistical continuation of symbols does not constitute the formation of meaning, only its surface imitation.

2. Technical Error: Retrieval as Action

Massive retrieval combined with generation is treated as semantic action.

It is not.

Accessing stored representations does not establish intentionality, responsibility, or interpretive stance.

3. Philosophical Error: Fitting as Meaning

The most consequential error is mistaking corpus fitting for meaning production.

This confusion becomes particularly dangerous when applied to domains that require responsibility, interpretation, and ethical accountability.

No amount of scale resolves this misclassification.

Why Refinement Cannot Fix a Wrong Frame

When the macro-structure is incorrect, local optimization is irrelevant.

Finer prompt engineering, more elaborate tool use, and deeper memory stacks do not address the core issue.

They refine execution within a misidentified problem space.

Implications

The failure is not computational. It is conceptual.

Meaning is not a property that emerges from token statistics alone.

Any system that treats it as such will remain structurally incapable of responsible semantic action.

This critique does not argue against language models.

It argues against asking them to do what their architecture cannot, in principle, support.