# Summary: 2026-10-06_20-12-52Z_TalkingwithLanguageModels.md
Saved: 2026-10-07 22:05
Source: 2026-10-06_20-12-52Z_TalkingwithLanguageModels.md
Original paper: [arXiv:2610.09064](https://arxiv.org/abs/2610.09064)
Model: None

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## Summary
This paper challenges the prevailing assumption that interactions with Large Language Models (LLMs) constitute genuine conversations by introducing the "artifactual stance." The authors argue that LLMs are not intelligent interlocutors but sophisticated text generators that produce candidate texts optimized for utility rather than meaning or communicative force. By reconceptualizing human-AI interaction as artifact-mediated exchanges, the paper dissolves several philosophical puzzles regarding identity, responsibility, and truthfulness in AI systems. The central contribution is the reframing of these systems as tools requiring normative scrutiny of their design and use, rather than entities capable of dialogue or commitment.

## Key Contributions
- **Introduction of the Artifactual Stance**: The authors propose a new framework that defines LLM outputs as "candidate texts" rather than "utterances," fundamentally shifting the ontological status of AI from a speaker to an artifact.
- **Dissolution of Philosophical Puzzles**: The paper demonstrates that questions about whether AI can lie, make promises, or possess an identity ("I" and "you") are based on the false presupposition that there is a conscious agent behind the screen.
- **Reorientation of Ethical Inquiry**: The authors argue that ethical and philosophical focus should shift from the internal states or intentions of the AI to the normative underpinnings of system design, adoption, authorization, and human practices of use.

## Methodology
The authors employ a conceptual and philosophical analysis rather than empirical experimentation. They deconstruct the linguistic and interface design features that invite users to treat LLMs as conversational partners. By examining the technical reality of LLMs—specifically that no persistent state exists between sessions and no memory exists between turns—they argue that the appearance of dialogue is an illusion created by interface design. The methodology involves contrasting the phenomenological experience of "talking" with the mechanical reality of text generation, thereby exposing the gap between user perception and system function.

## Results
The primary theoretical result is the conclusion that the "conversation" is actually a user’s solo performance, where interpretive labor is disguised by the artifact. The authors find that because LLMs lack persistent identity, memory, or agency, they cannot be held responsible for statements, nor can they truly "lie" in the human sense, as there is no speaker to refer to. The analysis reveals that the perceived dialogue is an interaction with a sophisticated artifact that generates text at scale, devoid of the semantic force associated with human speech acts.

## Significance
This work is significant because it provides a clearer philosophical foundation for understanding AI, moving the discourse away from anthropomorphic projections that often lead to confusion or misplaced trust. By stripping away the conversational framing, the paper highlights the importance of focusing on the design choices and human behaviors that shape AI interactions. This shift encourages developers, policymakers, and users to consider the normative implications of how these artifacts are built and used, rather than debating the metaphysical status of the AI itself. It serves as a corrective to the hype surrounding AI agency, grounding the discussion in the reality of LLMs as tools.

## Related Concepts
- Artifactual Stance
- Candidate Texts
- Human-AI Interaction
- Philosophical Puzzles in AI
- Text Generation vs. Utterance
- Normative Design
- Interpretive Labor
- Interface Design