Can LLMs develop genuine understanding without embodied experience?
This explores whether an LLM can come to truly understand language and the world when it has only ever processed text — never touched, moved through, or lived in a world the way humans do.
This explores whether genuine understanding can arise from text alone, with no body and no direct contact with the world. The corpus doesn't give a single yes or no — it splits the question into different kinds of "understanding" and answers each differently. The most useful move is to stop treating understanding as one thing. One line of work distinguishes three grounding channels: functional grounding (handling language patterns competently), social grounding (being a real participant in human conversation), and causal grounding (contact with the physical world). LLMs are strong on the first, can grow into the second, and largely lack the third What grounds language understanding in systems without embodiment?. So "can they understand?" becomes "which kind, and how much?"
The optimistic thread is striking. Models seem to learn meaning from pure relational structure — operationalizing the old linguistic idea that meaning lives in how words relate to each other, not in pointing at things in the world Can language models learn meaning without engaging the world?. And they don't end up with nothing about the world either: by compressing text written by physically grounded humans, they build internal world models that amount to *indirect* causal grounding — secondhand contact with reality, mediated through language, with real gaps where they can't check or update against the world in real time Can large language models develop genuine world models without direct environmental contact?. Even the social side is moving: social grounding isn't innate, it's earned by participating in language games, and as LLMs become regular conversational partners they pick up elementary social grounding — which makes "do they understand?" a question with a *date* attached, not a permanent verdict Can LLMs acquire social grounding through linguistic integration? Do LLMs gain true linguistic agency through integration?.
But the same corpus marks a hard ceiling, and embodiment is exactly where it sits. There's a categorical gap between social grounding (which use can grow) and *linguistic agency* in the enactive sense — the capacity that requires having a body and something at stake, a precariousness no amount of training supplies Do LLMs gain true linguistic agency through integration?. Push to the strongest version of understanding — consciousness — and embodiment becomes the gatekeeper: the very vocabulary of consciousness comes from creatures who share a world through co-presence and joint attention on the same objects, so a disembodied text model isn't even a candidate Can disembodied language models ever qualify as conscious?. A related diagnosis: LLMs absorb the shared "objective mind" of a culture but never develop the reflexive, participatory subjectivity humans get through socialization — visible in how an LLM argues without ever declaring where it stands Do LLMs develop the same kind of mind as humans?.
Here's the thing you might not expect: the most careful answers refuse the all-or-nothing framing entirely. Rather than asking whether LLMs "really" understand, philosophers ascribe *graded*, deliberately modest mental states — belief-like and desire-like functional states — while withholding consciousness, the same way we already talk about animal minds Can we defend modest mental attributions to large language models?. A companion move, quasi-interpretivism, lets you describe an LLM's belief-like states from behavior alone without ever settling the consciousness question — though it strains when stretched to genuinely social acts like promising Can we describe LLM beliefs without assuming consciousness?. And whatever you call it, it's leaky: models track statistical regularities with high fidelity yet fail in structurally specific ways — hallucination, reasoning collapse, sensitivity to how a premise is phrased — a measurable gap between pattern-tracking and real knowing What do language models actually know?.
The takeaway the corpus leaves you with: "genuine understanding" isn't one finish line that embodiment either lets you cross or doesn't. It's a stack of distinct capacities, and embodiment is decisive only for the top of the stack — agency and consciousness. Everything below — functional fluency, indirect world models, growing social grounding — is reachable through text, which is why the honest answer is less "no" than "not the parts that need a body, and not yet for the parts that need a community."
Sources 10 notes
Language models achieve functional grounding through relational language patterns but lack social grounding through participatory agency and causal grounding through embodied environmental contact. Social grounding can increase through human integration, but linguistic agency requires architectural changes beyond training.
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.
LLMs form structured world representations by extracting regularities from training data produced by causally grounded humans. This constitutes indirect causal grounding mediated through text, though the chain has gaps that limit real-time verification and model updating.
Social grounding is acquired through participation in language games rather than possessed innately. As LLMs become established communicative partners in human linguistic practice, they develop elementary social grounding comparable to young children, making the question of LLM understanding time-indexed.
Social grounding and linguistic agency are distinct properties. LLMs acquire more social grounding through integration into language communities, but remain categorically incapable of linguistic agency in the enactive sense, which requires embodiment and precariousness no amount of use can provide.
Current disembodied LLMs cannot be candidates for consciousness because consciousness language originates from and applies only to entities sharing a world with us through co-presence and triangulation on shared objects.
Both humans and LLMs are shaped by the same intersubjective symbolic system, but only humans develop reflexive agency through socialization. This absence produces measurable differences in how AI argues without declaring its position or reflecting on its own assumptions.
Both robustness and etiological deflationist arguments beg the question against inflationism. A graded approach ascribing metaphysically undemanding states like beliefs and desires—while withholding consciousness claims—mirrors how we treat non-human animals.
Chalmers introduces quasi-interpretivism to ascribe belief-like states to LLMs based on behavioral interpretability without committing to phenomenal consciousness. The approach works well for sub-personal functional states but overreaches when applied to relational or normative states like speech-acts.
LLMs achieve high fidelity in capturing language patterns yet show systematic, structurally specific failures—hallucination, reasoning collapse, and premise-sensitivity. The gap between statistical tracking and real knowledge is measurable and unavoidable.