Physical Complexity of a Cognitive Artifact

• URL: http://arxiv.org/abs/2509.12495v1
• Date: Mon, 15 Sep 2025 22:39:30 GMT
• Title: Physical Complexity of a Cognitive Artifact
• Authors: Gülce Kardeş, David Krakauer, Joshua Grochow,
• Abstract summary: We map concepts from the computational complexity of a physical puzzle, the Soma Cube, onto cognitive problem-solving strategies.<n>We quantitatively assess task difficulty and examine how different strategies modify complexity.<n>We propose a model of intelligence as a library of algorithms that recruit the capabilities of both mind and matter.
• Score: 0.009558392439655011
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cognitive science and theoretical computer science both seek to classify and explain the difficulty of tasks. Mechanisms of intelligence are those that reduce task difficulty. Here we map concepts from the computational complexity of a physical puzzle, the Soma Cube, onto cognitive problem-solving strategies through a ``Principle of Materiality''. By analyzing the puzzle's branching factor, measured through search tree outdegree, we quantitatively assess task difficulty and systematically examine how different strategies modify complexity. We incrementally refine a trial-and-error search by layering preprocessing (cognitive chunking), value ordering (cognitive free-sorting), variable ordering (cognitive scaffolding), and pruning (cognitive inference). We discuss how the competent use of artifacts reduces effective time complexity by exploiting physical constraints and propose a model of intelligence as a library of algorithms that recruit the capabilities of both mind and matter.

Related papers

• Thinker: Training LLMs in Hierarchical Thinking for Deep Search via Multi-Turn Interaction [57.67217258741752]
  Thinker is a hierarchical thinking model for deep search through multi-turn interaction.<n>It decomposes complex problems into independently solvable sub-problems.<n> dependencies between sub-problems are passed as parameters via these logical functions.
  arXiv  Detail & Related papers  (2025-11-11T07:48:45Z)
• Exploring a Cognitive Architecture for Learning Arithmetic Equations [0.0]
  This paper explores the cognitive mechanisms powering arithmetic learning. I implement a number vectorization embedding network and an associative memory model to investigate how an intelligent system can learn and recall arithmetic equations. I aim to contribute to ongoing research into the neural correlates of mathematical cognition in intelligent systems.
  arXiv  Detail & Related papers  (2024-05-05T18:42:00Z)
• Are Language Models Puzzle Prodigies? Algorithmic Puzzles Unveil Serious Challenges in Multimodal Reasoning [24.386388107656334]
  This paper introduces the novel task of multimodal puzzle solving, framed within the context of visual question-answering. We present a new dataset, AlgoVQA, designed to challenge and evaluate the capabilities of multimodal language models in solving algorithmic puzzles.
  arXiv  Detail & Related papers  (2024-03-06T17:15:04Z)
• Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models [49.23348672822087]
  We propose Knowledge Crosswords, a benchmark consisting of incomplete knowledge networks bounded by structured factual constraints. The novel setting of geometric knowledge reasoning necessitates new LM abilities beyond existing atomic/linear multi-hop QA. We conduct extensive experiments to evaluate existing LLMs and approaches on Knowledge Crosswords.
  arXiv  Detail & Related papers  (2023-10-02T15:43:53Z)
• Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration [116.09561564489799]
  Solo Performance Prompting transforms a single LLM into a cognitive synergist by engaging in multi-turn self-collaboration with multiple personas. A cognitive synergist is an intelligent agent that collaboratively combines multiple minds' strengths and knowledge to enhance problem-solving in complex tasks. Our in-depth analysis shows that assigning multiple fine-grained personas in LLMs improves problem-solving abilities compared to using a single or fixed number of personas.
  arXiv  Detail & Related papers  (2023-07-11T14:45:19Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Exceeding Computational Complexity Trial-and-Error Dynamic Action and Intelligence [0.0]
  Computational complexity is a core theory of computer science, which dictates the degree of difficulty of computation. In this paper, we try to clarify concepts, and we propose definitions such as unparticularized computing, particularized computing, computing agents, and dynamic search. We also propose and discuss a framework, i.e., trial-and-error + dynamic search.
  arXiv  Detail & Related papers  (2022-12-22T21:23:27Z)
• Intelligent problem-solving as integrated hierarchical reinforcement learning [11.284287026711125]
  Development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms. We propose steps to integrate biologically inspired hierarchical mechanisms to enable advanced problem-solving skills in artificial agents. We expect our results to guide the development of more sophisticated cognitively inspired hierarchical machine learning architectures.
  arXiv  Detail & Related papers  (2022-08-18T09:28:03Z)
• Learning Iterative Reasoning through Energy Minimization [77.33859525900334]
  We present a new framework for iterative reasoning with neural networks. We train a neural network to parameterize an energy landscape over all outputs. We implement each step of the iterative reasoning as an energy minimization step to find a minimal energy solution.
  arXiv  Detail & Related papers  (2022-06-30T17:44:20Z)
• Divide & Conquer Imitation Learning [75.31752559017978]
  Imitation Learning can be a powerful approach to bootstrap the learning process. We present a novel algorithm designed to imitate complex robotic tasks from the states of an expert trajectory. We show that our method imitates a non-holonomic navigation task and scales to a complex simulated robotic manipulation task with very high sample efficiency.
  arXiv  Detail & Related papers  (2022-04-15T09:56:50Z)
• End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking [52.05847268235338]
  We show how machine learning systems can perform logical extrapolation without overthinking problems. We propose a recall architecture that keeps an explicit copy of the problem instance in memory so that it cannot be forgotten. We also employ a progressive training routine that prevents the model from learning behaviors that are specific to number and instead pushes it to learn behaviors that can be repeated indefinitely.
  arXiv  Detail & Related papers  (2022-02-11T18:43:28Z)
• HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem Solving [104.79156980475686]
  Humans learn compositional and causal abstraction, ie, knowledge, in response to the structure of naturalistic tasks. We argue there shall be three levels of generalization in how an agent represents its knowledge: perceptual, conceptual, and algorithmic. This benchmark is centered around a novel task domain, HALMA, for visual concept development and rapid problem-solving.
  arXiv  Detail & Related papers  (2021-02-22T20:37:01Z)

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.