Don't Forget Imagination!
- URL: http://arxiv.org/abs/2508.06062v1
- Date: Fri, 08 Aug 2025 06:50:43 GMT
- Title: Don't Forget Imagination!
- Authors: Evgenii E. Vityaev, Andrei Mantsivoda,
- Abstract summary: This paper is a call for greater attention to cognitive imagination as the next promising breakthrough in artificial intelligence.<n>We propose semantic models -- a new approach to mathematical models that can learn, like neural networks, and are based on probabilistic causal relationships.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Cognitive imagination is a type of imagination that plays a key role in human thinking. It is not a ``picture-in-the-head'' imagination. It is a faculty to mentally visualize coherent and holistic systems of concepts and causal links that serve as semantic contexts for reasoning, decision making and prediction. Our position is that the role of cognitive imagination is still greatly underestimated, and this creates numerous problems and diminishes the current capabilities of AI. For instance, when reasoning, humans rely on imaginary contexts to retrieve background info. They also constantly return to the context for semantic verification that their reasoning is still reasonable. Thus, reasoning without imagination is blind. This paper is a call for greater attention to cognitive imagination as the next promising breakthrough in artificial intelligence. As an instrument for simulating cognitive imagination, we propose semantic models -- a new approach to mathematical models that can learn, like neural networks, and are based on probabilistic causal relationships. Semantic models can simulate cognitive imagination because they ensure the consistency of imaginary contexts and implement a glass-box approach that allows the context to be manipulated as a holistic and coherent system of interrelated facts glued together with causal relations.
Related papers
- When and How Much to Imagine: Adaptive Test-Time Scaling with World Models for Visual Spatial Reasoning [108.73849507002195]
We present an in-depth analysis of test-time visual imagination as a controllable resource for spatial reasoning.<n>We study when static visual evidence is sufficient, when imagination improves reasoning, and how excessive or unnecessary imagination affects accuracy and efficiency.
arXiv Detail & Related papers (2026-02-09T03:21:48Z) - Internal World Models as Imagination Networks in Cognitive Agents [0.0]
We propose that imagination serves to access an internal world model (IWM) and use psychological network analysis to explore IWMs in humans and large language models (LLMs)<n>Our study offers a novel method for comparing internally-generated representations in humans and AI, providing insights for developing human-like imagination in artificial intelligence.
arXiv Detail & Related papers (2025-10-05T23:01:10Z) - Whither symbols in the era of advanced neural networks? [28.417833278000476]
We argue that modern neural networks and the artificial intelligence systems built upon them exhibit similar abilities.<n>This undermines the argument that the cognitive processes and representations used by human minds are symbolic.
arXiv Detail & Related papers (2025-08-07T18:42:55Z) - DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning [11.242852367476015]
DeepEyes is a model with "thinking with images" capabilities incentivized through end-to-end reinforcement learning.<n>We propose a tool-use-oriented data selection mechanism and a reward strategy to encourage successful tool-assisted reasoning trajectories.<n>DeepEyes achieves significant performance gains on fine-grained perception and reasoning benchmarks.
arXiv Detail & Related papers (2025-05-20T13:48:11Z) - Visual cognition in multimodal large language models [12.603212933816206]
Recent advancements have rekindled interest in the potential to emulate human-like cognitive abilities.
This paper evaluates the current state of vision-based large language models in the domains of intuitive physics, causal reasoning, and intuitive psychology.
arXiv Detail & Related papers (2023-11-27T18:58:34Z) - Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play
Multi-Character Belief Tracker [72.09076317574238]
ToM is a plug-and-play approach to investigate the belief states of characters in reading comprehension.
We show that ToM enhances off-the-shelf neural network theory mind in a zero-order setting while showing robust out-of-distribution performance compared to supervised baselines.
arXiv Detail & Related papers (2023-06-01T17:24:35Z) - Imagination-Augmented Natural Language Understanding [71.51687221130925]
We introduce an Imagination-Augmented Cross-modal (iACE) to solve natural language understanding tasks.
iACE enables visual imagination with external knowledge transferred from the powerful generative and pre-trained vision-and-language models.
Experiments on GLUE and SWAG show that iACE achieves consistent improvement over visually-supervised pre-trained models.
arXiv Detail & Related papers (2022-04-18T19:39:36Z) - What does it mean to represent? Mental representations as falsifiable
memory patterns [8.430851504111585]
We argue that causal and teleological approaches fail to provide a satisfactory account of representation.
We sketch an alternative according to which representations correspond to inferred latent structures in the world.
These structures are assumed to have certain properties objectively, which allows for planning, prediction, and detection of unexpected events.
arXiv Detail & Related papers (2022-03-06T12:52:42Z) - Emergence of Machine Language: Towards Symbolic Intelligence with Neural
Networks [73.94290462239061]
We propose to combine symbolism and connectionism principles by using neural networks to derive a discrete representation.
By designing an interactive environment and task, we demonstrated that machines could generate a spontaneous, flexible, and semantic language.
arXiv Detail & Related papers (2022-01-14T14:54:58Z) - Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles.
This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing.
The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
arXiv Detail & Related papers (2020-11-30T18:29:25Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z) - Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike
Common Sense [142.53911271465344]
We argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks.
We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense.
arXiv Detail & Related papers (2020-04-20T04:07:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.