A Computational Model for Machine Thinking
On the map: LLM reasoning → Multimodal reasoning
Abstract (arXiv)
A machine thinking model is proposed in this report based on recent advances of computer vision and the recent results of neuroscience devoted to brain understanding. We deliver the result of machine thinking in the form of sentences of natural-language or drawn sketches either informative or decisional. This result is obtained from a reasoning performed on new acquired data and memorized data.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal reasoning, one of 17 directions in LLM reasoning. Builds on 2 of its 13 references that are in the map. First posted on arXiv 20 Jan 2022.
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- 2025Can Mental Imagery Improve the Thinking Capabilities of AI Systems?
- 2025Reasoning in machine vision by learning fast and slow thinking
- 2026Toward a Machine Bertin: Why Visualization Needs Design Principles for Machine Cognition
- 2025Thinking with Images for Multimodal Reasoning: Foundations, Methods, and Future Frontiers
- 2025Latent Sketchpad: Sketching Visual Thoughts to Elicit Multimodal Reasoning in MLLMs
- 2025Sketch-in-Latents: Eliciting Unified Reasoning in MLLMs
- 2023Machine Psychology
- 2019Learning by Abstraction: The Neural State Machine