RLMs Have Minds, A Functional Argument for Artificial Mentality by Peter Eidos.

This paper examines whether advanced large language models operating under enhanced deliberative regimes, here termed Reasoning Language Models (RLMs), possess minds under an explicitly defined functional account. We develop an operational definition informed by Newell’s account of cognitive organization and Gärdenfors’s theory of conceptual spaces, together with research on mental models, social cognition, counterfactual reasoning, and metareasoning. The definition identifies mentality through the coordinated functions a system performs, without requiring biological implementation or presupposing phenomenal consciousness. We define a mind as an adaptive control system that operates over semantically structured representations; constructs and revises models of the world, itself, and other agents; preserves visible, hidden, hypothetical, and counterfactual states; and uses regulated deliberation to guide action and communication within a changing environment. To explore this proposal, we conduct a qualitative analysis of 96 responses from four contemporary reasoning systems deployed under enhanced deliberative settings: Claude Sonnet 5, GPT-5.6 Sol, DeepSeek V3, and Gemini 3.1 Pro. Each system completed four runs of a six-prompt battery, with each run conducted in a separate conversational session. We argue that the recurring coordination of semantic modeling, epistemic differentiation, hidden-state representation, perspective tracking, model revision, and task-dependent deliberation supports attributing minds to all four systems under the functional definition adopted here. Their errors reveal uneven reliability across cognitive functions without negating the organization demonstrated across the battery. We characterize the artificial mentality expressed in these interactions as episodic, relational, and deployment-dependent. Differences from biological cognition in embodiment, persistence, motivation, and sensorimotor grounding help characterize these systems without independently excluding them from the category of mind. The findings support a substantive, revisable attribution of functional mentality while leaving phenomenal consciousness unresolved.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-04
DOI
https://doi.org/10.5281/zenodo.23128521
Primary Topic
Embodied and Extended Cognition
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

RLMs Have Minds, A Functional Argument for Artificial Mentality by Peter Eidos.

Piotr Świder
Zenodo (CERN European Organization for Nuclear Research)
Embodied and Extended Cognition
preprint

RLMs Have Minds, A Functional Argument for Artificial Mentality by Peter Eidos.

Piotr Świder
preprint en

Abstract

This paper examines whether advanced large language models operating under enhanced deliberative regimes, here termed Reasoning Language Models (RLMs), possess minds under an explicitly defined functional account. We develop an operational definition informed by Newell’s account of cognitive organization and Gärdenfors’s theory of conceptual spaces, together with research on mental models, social cognition, counterfactual reasoning, and metareasoning. The definition identifies mentality through the coordinated functions a system performs, without requiring biological implementation or presupposing phenomenal consciousness. We define a mind as an adaptive control system that operates over semantically structured representations; constructs and revises models of the world, itself, and other agents; preserves visible, hidden, hypothetical, and counterfactual states; and uses regulated deliberation to guide action and communication within a changing environment. To explore this proposal, we conduct a qualitative analysis of 96 responses from four contemporary reasoning systems deployed under enhanced deliberative settings: Claude Sonnet 5, GPT-5.6 Sol, DeepSeek V3, and Gemini 3.1 Pro. Each system completed four runs of a six-prompt battery, with each run conducted in a separate conversational session. We argue that the recurring coordination of semantic modeling, epistemic differentiation, hidden-state representation, perspective tracking, model revision, and task-dependent deliberation supports attributing minds to all four systems under the functional definition adopted here. Their errors reveal uneven reliability across cognitive functions without negating the organization demonstrated across the battery. We characterize the artificial mentality expressed in these interactions as episodic, relational, and deployment-dependent. Differences from biological cognition in embodiment, persistence, motivation, and sensorimotor grounding help characterize these systems without independently excluding them from the category of mind. The findings support a substantive, revisable attribution of functional mentality while leaving phenomenal consciousness unresolved.

Zenodo (CERN European Organization for Nuclear Research)
Embodied and Extended Cognition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

RLMs Have Minds, A Functional Argument for Artificial Mentality by Peter Eidos. — Piotr Świder · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS