The paper introduces a five‑level framework for message formation and comprehension in artificial superintelligence, arguing that language model context alone cannot reveal the underlying foundations of utterance creation. These foundations are built from pre‑acquired schemas, systemic representations of the world of activity, and accepted connections within them. The levels—systemic representations, a specific situation, its narrative description, the full sense of a message, and the text—enable reconstruction of these foundations from abbreviated communication and support reverse engineering from sense to systemic bases.
arXiv:2607. 25620v1 Announce Type: new Abstract: Quattrociocchi and colleagues warn that the fluent outputs of large language models may allow linguistic plausibility to substitute for epistemic evaluation, producing the condition they call *Epistemia*: the experience of possessing knowledge without undertaking the practices through which judgment would ordinarily be warranted.
The article examines chatbots as partners in problem‑solving conversations, arguing that basic chatbots—comprising a large language model (LLM) and a simple interface—are multifaceted but cannot match human cognitive flexibility. Drawing on Aggregation Dynamics, Cognitive Linguistics, Neuropsychology, and Psychology, the authors describe how LLMs encode artificial metaphorical problem propagations from training data, which only partially imitate human thinking. They conclude that further LLM development will not yield true thinking partners, yet chatbots are widely used, making their understanding socially and politically important.
The paper argues that large language models (LLMs) should not be viewed as conversational partners but as sophisticated text-generating artifacts. It introduces the concept of an artifactual stance, framing human‑AI interaction as exchanges of candidate texts optimized for utility rather than meaningful utterances. By rejecting the illusion of dialogue, the authors suggest that philosophical concerns about identity, responsibility, and truth in AI interactions are misplaced, and instead focus should shift to the normative design and use of these artifacts.
The article presents a minimal working model for large language model (LLM) systems, emphasizing four key distinctions—pretraining vs. deployment, distribution vs. samples, types of memory, and task competence vs. agency. Using this framework, it diagnoses six common misconceptions about LLMs (next‑token prediction, regression to the mean, training‑data regurgitation, model memory, alignment, and understanding), explaining what each misconception captures correctly, where it conflates distinctions, and the implications for evaluation, design, and governance. The model is applied to AI policy language, illustrating how policy can misrepresent these distinctions and offering a diagnostic toolkit to correct such errors.
arXiv:2408.11827v2 Announce Type: replace Abstract: Understanding how language models compose meaning from linguistic input remains a central problem in interpretability research. Mechanistic studies...
arXiv:2608. 13604v1 Announce Type: new Abstract: Detection of misunderstanding is an urgent problem to solve because communication has moved away from real-time, in-person interaction and is increasingly handled by AI-mediated channels.
What do a language model's hidden states say about the organization of a single text? From one forward pass, without training, we score every token position on two properties.
MUSE is a story‑generation engine that applies Robert McKee’s narrative theory to guide decisions about plot, character, and language throughout planning, drafting, and revision. It structures story knowledge into rule atoms, semantic consolidations, and mechanisms, and uses intermediate deliverables to preserve decisions across creative stages. Experiments show MUSE improves benchmark scores over zero‑shot generation and maintains low consistency errors across multiple models.
arXiv:2602. 15851v2 Announce Type: replace-cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising methods in automatic story generation and understanding tasks.
arXiv:2607. 07891v1 Announce Type: cross Abstract: Roy Harris's Integrationist linguistics offers a compelling critique of the referentialist tradition embedded deep at the heart of computational approaches to language, arguing that language is not a code that maps onto a pre-given world but a situated, bipartite activity oriented toward prospective joint action.
arXiv:2505.22767v4 Announce Type: replace-cross Abstract: Dialogue with a large language model can lead a person to insight: a sudden change in how they understand a problem. This perspective asks ho...
The paper argues that new interactional concepts are needed to understand how humans change when engaging with large language models (LLMs). Drawing on enactive theory and the notion of Linguistic Bodies, it proposes that humans incorporate LLMs both as tools that enable specific skills and as linguistic partners that shape identity and language use. The authors introduce the idea of "susceptive sense‑making" to describe the heightened semiotic labor involved when friction is absent in these interactions, and suggest that this dynamic may affect users’ autonomy and development.