Why isn’t LLM reasoning done in vector space instead of natural language?[D]

Reddit r/MachineLearning / 4/29/2026

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Key Points

The article asks why large language models (LLMs) typically express reasoning in natural language rather than performing explicit, vector-space-based reasoning steps.
It notes that LLMs already compute internally using high-dimensional vectors, raising the question of whether intermediate reasoning could remain entirely in latent/vector space until the final output.
The discussion considers whether vector-based reasoning would be faster, more compressed, and potentially better suited for “intuition-like” tasks.
It also weighs trade-offs: vector-only reasoning might be more opaque, harder to verify, and less reliable for tasks requiring rigorous math, programming, or legal logic.
Overall, the piece frames the problem as a design/verification challenge: how to “think” in vectors while still producing trustworthy, checkable outputs in language when needed.

Why don’t LLMs use explicit vector-based reasoning instead of language-based chain-of-thought? What would happen if they did?

Most LLM reasoning we see is expressed through language: step-by-step text, explanations, chain-of-thought style outputs, etc. But internally, models already operate on high-dimensional vectors.

So my question is:

Why don’t we have models that reason more explicitly in latent/vector space instead of producing intermediate reasoning in natural language?

Would vector-based reasoning be faster, more compressed, and better for intuition-like tasks? Or would it make reasoning too opaque, hard to verify, and unreliable for math/programming/legal logic?

In other words:

Could an LLM “think” in vectors and only translate the final reasoning into language at the end?

Curious how researchers/engineers think about this.

submitted by /u/ZeusZCC
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