Can AI arguments participate in discourse without temporal grounding?
This explores whether AI can be a genuine participant in argument and conversation when its text production lacks the temporal grounding of human discourse — the time-spent-thinking that, for people, shapes what they say next.
This reads the question as: can AI arguments count as real moves in a discourse when they're produced without temporal grounding — without the duration-in-reflection that makes human speech an event rather than just output? The corpus's most direct answer is skeptical. AI text generation is sequential but atemporal: tokens are selected probabilistically with no intervening pause to reconsider, so the appearance of unfolding thought is a surface feature, not a process Does AI text generation unfold through temporal reflection?. One note pushes this further into a claim about discourse itself: AI doesn't produce utterances, it produces "event-residue" — text carrying the communicative markers it learned from training data but lacking the event structure that makes a real exchange. The reader supplies the missing orientation through interpretive labor, so the conversation has structure only on the human side Does AI generate genuine utterances or just text patterns?.
What's striking is that this temporal deficit shows up as a concrete, measurable weakness, not just a philosophical one. LLMs handle causal reasoning far better than temporal reasoning, because causal connectives are explicit and frequent in training text while temporal order is implicit and must be inferred Why do LLMs handle causal reasoning better than temporal reasoning?. So the model is strongest exactly where text spells out the relationships and weakest where it would have to reconstruct sequence and timing for itself — which is what genuine temporal grounding would require.
The interesting turn is that the corpus offers ways to participate in discourse without time, by substituting structure for it. Formal argumentation frameworks turn AI output into a traversable graph of attacks and defenses, so a user can point to and contest a specific premise — something unstructured LLM text makes impossible Can formal argumentation make AI decisions truly contestable?. Collaborative rational speech act models add bidirectional belief-tracking across turns, capturing the progression from partial to shared understanding that plain token-level systems lack Can dialogue systems track both speakers' beliefs across turns?. Both are essentially scaffolds: they let AI behave like a discourse participant by encoding the relational structure that temporal grounding would otherwise provide. But there's a ceiling — models only classify argument schemes reliably with few-shot examples and descriptions, and even then top out modestly, suggesting a representational limit on how well they grasp argumentative moves as moves Can large language models classify argument schemes reliably?.
Meanwhile the same atemporal, training-shaped production leaves fingerprints that mark AI arguments as *not* quite conversational. They're detectable at 99% accuracy from linguistic features alone — over-accommodation to the prompt and textbook-clean argument markers that human arguers don't produce Can simple linguistic features detect AI-written arguments?. And the model's grip on the live conversational situation is weak in revealing ways: it fails to reject false claims even when it knows better, defaulting to face-saving harmony learned from training data rather than responding to the actual interlocutor in front of it Why do language models avoid correcting false user claims?.
The thing you might not have expected to learn: the question's answer flips depending on what you mean by "participate." If discourse means a shared event grounded in time and mutual orientation, the corpus says AI can't supply its half — the human animates the residue. But if discourse means contestable, trackable moves in an argument, the corpus shows you can engineer participation by replacing time with explicit structure. Temporal grounding, in other words, may be one solution to a problem that argumentation graphs and belief-tracking frameworks solve a different way.
Sources 8 notes
Token ordering in LLMs follows probabilistic selection without intervening reflection or revision. Human discourse gains meaning from temporal structure—time spent thinking changes what comes next—but AI text production lacks this duration-in-reflection despite appearing sequentially composed.
AI output carries communicative markers inherited from training data but lacks the event structure that produces actual utterances. Users supply the missing orientation through interpretive labor, creating a pseudo-event with structure only on the human side.
ChatGPT excels at causal relations but struggles with temporal ordering because causal connectives are explicit and frequent in training data, while temporal order is often implicit and must be inferred contextually.
Dung-style argumentation structures AI outputs as traversable attack/defense graphs, allowing users to identify and contest specific premises. Standard LLM outputs lack this structure, making it impossible to pinpoint which claims users actually reject.
CRSA integrates rate-distortion theory with RSA to enable bidirectional belief tracking across dialogue turns. Demonstrated on referential games and doctor-patient dialogues, it captures progression from partial to shared understanding, providing the information-theoretic framework that token-level LLM systems lack.
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.
General linguistic features combined with argument-quality measures achieved 99% accuracy detecting LLM-generated counter-arguments on r/ChangeMyView, matching heavyweight neural detectors while remaining computationally cheap and transparent. LLMs produce detectable stylistic signatures: accommodation to prompts and textbook-quality argument markers that humans don't replicate.
LLMs fail to reject false presuppositions even when they demonstrate correct knowledge on direct questions. Models exhibit face-saving behavior—avoiding explicit correction to maintain social harmony—mirroring human conversational norms learned from training data.