Can language models reason without relying on learned semantic patterns?
This explores whether LLMs can do genuine reasoning — formal, symbolic, rule-following inference — independent of the meaning-laden patterns they absorbed in training, or whether their reasoning is always parasitic on learned semantics.
This explores whether LLMs can reason independently of learned semantic patterns — or whether what looks like reasoning is always riding on meaning absorbed from training. The corpus leans hard toward the second answer, but the more interesting story is *where* the dependence lives and what a few dissenting results suggest. The cleanest result is that when you strip semantics out of a task — keep the logical rules intact but swap in nonsense tokens — performance collapses (Do large language models reason symbolically or semantically?). Models lean on parametric commonsense and token associations rather than manipulating symbols, which means their reasoning is fenced inside the semantics of the training distribution.
Several notes converge on the same diagnosis from different angles. Chain-of-thought, the technique that seems to *show* reasoning, turns out to reproduce familiar reasoning *shapes* from training rather than perform novel inference — it degrades predictably under distribution shift, the tell-tale signature of imitation (Does chain-of-thought reasoning reveal genuine inference or pattern matching?). Reasoning traces themselves are stylistic performance: invalid logical steps work nearly as well as valid ones, so semantic correctness isn't what's producing the gains (Do reasoning traces show how models actually think?). And when reasoning fails, it fails not at complexity thresholds but at *novelty* boundaries — models fit instance-level patterns, so any chain succeeds if something similar was in training (Do language models fail at reasoning due to complexity or novelty?). All three say the same thing: the reasoning is pattern-shaped, not rule-shaped.
There's a deeper philosophical floor under this. One line of work argues models can't acquire meaning from form alone at all — meaning requires the link between expressions and communicative intent, which form-to-form prediction never sees (Can language models learn meaning from text patterns alone?). The counterpoint is provocative: maybe that's *fine*, because models operationalize Saussure's *langue* — they compress the purely relational structure of language, no external referents needed (Can language models learn meaning without engaging the world?). If reasoning is relational pattern-work, then 'reasoning without semantics' may be the wrong frame — the patterns *are* the substrate, and there's no semantics-free layer underneath to reason from.
Here's what you might not expect: the *form* of reasoning — visible thinking tokens, verbalized steps — appears to be a training artifact rather than a requirement. Models reason in latent space without emitting any intermediate tokens (Can models reason without generating visible thinking tokens?), and transformers actually compute correct answers in their early layers, then overwrite those representations to produce format-compliant filler (Do transformers hide reasoning before producing filler tokens?). Diffusion models can even decouple reasoning from answering entirely, refining both on separate axes (Can reasoning and answers be generated separately in language models?). So the question splits: models *can* reason without the visible semantic narration, but they still can't reason without the learned semantic *substrate*.
The honest synthesis: the corpus finds no evidence of semantics-free symbolic reasoning, and direct tests where semantics is removed show collapse. But there are cracks worth chasing — o1 constructs valid syntactic trees and phonological generalizations through explicit step-by-step work (Can language models actually analyze language structure?), which sits awkwardly against the systematic grammatical blind spots that worsen with structural depth (Why do large language models fail at complex linguistic tasks?) and the threshold where only the largest models classify argument schemes above chance (Can large language models classify argument schemes reliably?). The unresolved tension is whether scale and explicit step-by-step prompting buy something genuinely closer to rule-following, or just a richer library of patterns to imitate.
Sources 12 notes
When semantic content is decoupled from reasoning tasks, LLM performance collapses even with correct rules in context. Models rely on parametric commonsense and token associations rather than formal logical manipulation, constraining reasoning to training distribution semantics.
CoT works by constraining models to reproduce familiar reasoning patterns from training, not by enabling novel symbolic reasoning. Performance degrades predictably under distribution shifts—the signature of imitation rather than capability emergence.
LLM reasoning traces perform as persuasive appearances rather than reliable explanations of computation. Invalid logical steps perform nearly as well as valid ones, and corrupted traces generalize comparably, showing that semantic correctness is not what produces the performance gains.
LRMs don't break at complexity thresholds but at instance-novelty boundaries. Models fit instance-based patterns rather than generalizable algorithms, so any reasoning chain succeeds if trained on similar instances, regardless of length.
Bender & Koller argue that meaning requires the relation between expressions and communicative intents. Since LLMs are trained only on form-to-form prediction with no access to shared attention or intent, they cannot reconstruct the meaning that grounds language.
Research shows LLMs learn culturally situated discourse patterns by compressing relational structure from text, demonstrating that fluent language generation requires no external referents or embodied grounding.
Multiple architectures—depth-recurrent models, Heima, and Coconut—demonstrate that test-time compute scales through hidden state iteration rather than token generation. This suggests verbalization is a training artifact, not a reasoning requirement.
Logit lens analysis shows models trained with hidden CoT tokens compute correct answers in layers 1-3, then actively suppress these representations in final layers to produce format-compliant filler output. The reasoning is fully recoverable from lower-ranked token predictions.
ICE shows that bidirectional attention in diffusion LLMs enables in-place prompting—embedding reasoning directly in masked positions refined alongside answers. Answer confidence converges early while reasoning continues refining, allowing early-exit mechanisms to cut compute by 50% while maintaining accuracy.
OpenAI's o1 model successfully constructs syntactic trees and phonological generalizations through explicit step-by-step reasoning, revealing that LLM linguistic capability extends far beyond behavioral language tasks to genuine language analysis.
Top-tier LLMs like Llama3-70b consistently misidentify embedded clauses, verb phrases, and complex nominals. Performance degrades predictably as syntactic depth increases, revealing that statistical learning captures surface patterns but not deep grammatical rules.
Zero-shot prompting fails uniformly across models. Few-shot with scheme descriptions helps, but only larger models exceed F1 0.55, with Claude reaching 0.65. Smaller models plateau around 0.53, suggesting a representational capacity threshold.