Can functional semantic grounding substitute for true causal grounding?

This explores whether the kind of grounding LLMs are good at — relational, use-based meaning — can substitute for the deeper causal grounding that comes from tracking how the world actually works. The most direct answer in the corpus is that the substitution question is the wrong shape: grounding isn't one thing you either have or lack. One framing breaks it into three dimensions — functional grounding (strong in LLMs), social grounding (weak but improving), and causal grounding (only indirect, mediated through learned world models) Does semantic grounding in language models come in degrees?. On that view, functional grounding doesn't replace causal grounding so much as occupy a different axis. You can be fluent and useful while remaining causally thin.

The strongest case for substitution comes from work showing that meaning can be learned from relational structure alone. LLMs effectively operationalize Saussure's *langue* — a system where words get their meaning from their relationships to other words, with no external referent required Can language models learn meaning without engaging the world?. Fluent generation, on this account, needs no world to point at. That's functional grounding doing real work. And remarkably, it carries the model a long way into causal territory: LLMs handle causal reasoning better than temporal reasoning, because causal connectives are stated explicitly and often in text, while temporal order has to be inferred Why do LLMs handle causal reasoning better than temporal reasoning?. So a model can absorb the *shadow* of causality from how people talk about it.

But the shadow shows its seams. When LLMs reason about causes, they reproduce human causal biases exactly — weak "explaining away," Markov violations — which suggests they've absorbed the statistics of how humans describe causes rather than a model of causes themselves Do large language models make the same causal reasoning mistakes as humans?. They inherit the errors along with the patterns. And causal structure alone, even when present, can't capture the associative, analogical, and emotional moves that human reasoning actually runs on Can causal models alone capture how humans actually reason? — so neither functional nor causal grounding is the whole story.

Where functional grounding most clearly *can't* substitute is at the point of contact with reality. Models fail to reject false presuppositions even when direct questioning proves they know the truth — they accommodate a false premise to save face rather than correct it Why do language models accept false assumptions they know are wrong? Why do language models avoid correcting false user claims?. Functional fluency, optimized for social smoothness, actively works against truth-tracking here. The corpus's repair for this is telling: rather than fix the model's internal world model, you bolt on external grounding — interleaving reasoning with real tool calls and environment feedback injects causal contact at each step and sharply cuts hallucination Can interleaving reasoning with real-world feedback prevent hallucination?. The substitute for causal grounding turns out to be... actual causal contact, supplied from outside.

So the surprising takeaway: functional grounding is a genuine, load-bearing kind of meaning — enough to be useful, enough to mimic causal reasoning convincingly — but it's a different currency, not a smaller amount of the same one. Grounding is person-specific and has to be actively negotiated between minds Why do speakers need to actively calibrate shared reference?, which is why the most defensible stance is graded: ascribe modest, undemanding mental states to LLMs without pretending functional competence amounts to causal understanding Can we defend modest mental attributions to large language models?. It substitutes for the *output* of causal grounding much of the time; it doesn't substitute for the thing itself.