arXiv (All)
open access
Algorithmic Reflectivity in Large Language Models: Methodological Study of Linguistic Structure in Mentalization
Mentalization is a core human capacity involving the interpretation of one's own and others' behavior in terms of underlying mental states. Large language models (LLMs) can generate linguistically reflective discourse, raising the question of whether the formal linguistic structure of mentalization
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- The Large Silence Model (LSM): A Theoretical Framework for the Pre-Linguistic Ground of Intelligence and the Structural Limits of Language-Based AI#244921
- A large-scale investigation of domain-general and experience-based sources of individual differences in syntactic processing#286129
- Syntactic complexity convergence in dialogue: Analysis of the phenomenon and application to LLM detection#622691
- AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?#1084162
- MLLMs Hallucinate when Information Distribution Drifts in Synergy Heads#1013209
Record · ID 1412753
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive