Universality in Collective Intelligence on the Rubik's Cube

• URL: http://arxiv.org/abs/2511.18609v1
• Date: Sun, 23 Nov 2025 20:30:38 GMT
• Title: Universality in Collective Intelligence on the Rubik's Cube
• Abstract summary: We use the Rubik's Cube as a cognitive model system existing at the intersection of puzzle solving, skill learning, expert knowledge, cultural transmission, and group theory.<n>We find evidence for universality in the collective learning of the Rubik's Cube in both sighted and blindfolded conditions.
• Score: 0.009558392439655011
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Progress in understanding expert performance is limited by the scarcity of quantitative data on long-term knowledge acquisition and deployment. Here we use the Rubik's Cube as a cognitive model system existing at the intersection of puzzle solving, skill learning, expert knowledge, cultural transmission, and group theory. By studying competitive cube communities, we find evidence for universality in the collective learning of the Rubik's Cube in both sighted and blindfolded conditions: expert performance follows exponential progress curves whose parameters reflect the delayed acquisition of algorithms that shorten solution paths. Blindfold solves form a distinct problem class from sighted solves and are constrained not only by expert knowledge but also by the skill improvements required to overcome short-term memory bottlenecks, a constraint shared with blindfold chess. Cognitive artifacts such as the Rubik's Cube help solvers navigate an otherwise enormous mathematical state space. In doing so, they sustain collective intelligence by integrating communal knowledge stores with individual expertise and skill, illustrating how expertise can, in practice, continue to deepen over the course of a single lifetime.

Related papers

• Online library learning in human visual puzzle solving [4.633616287561709]
  People often form efficient reusable abstractions that simplify future work, despite uncertainty about the future.<n>We study this process in a visual puzzle task where participants define and reuse helpers.<n>Online library learning is a core mechanism in human problem solving, allowing people to flexibly build, refine, and reuse abstractions as task demands grow.
  arXiv  Detail & Related papers  (2026-03-24T14:12:28Z)
• CubeBench: Diagnosing Interactive, Long-Horizon Spatial Reasoning Under Partial Observations [60.51118188315758]
  Large Language Model (LLM) agents, while proficient in the digital realm, face a significant gap in physical-world deployment.<n>We identify three core cognitive challenges hindering this transition: spatial reasoning, long-horizon state tracking via mental simulation, and active exploration under partial observation.<n>We introduce CubeBench, a novel generative benchmark centered on the Rubik's Cube.
  arXiv  Detail & Related papers  (2025-12-29T09:25:56Z)
• Physical Complexity of a Cognitive Artifact [0.009558392439655011]
  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.
  arXiv  Detail & Related papers  (2025-09-15T22:39:30Z)
• CubeRobot: Grounding Language in Rubik's Cube Manipulation via Vision-Language Model [1.644433638087587]
  We introduce CubeRobot, a novel vision-language model (VLM) tailored for solving 3x3 Rubik's Cubes.<n>We incorporate a dual-loop VisionCoT architecture and Memory Stream, a paradigm for extracting task-related features from VLM-generated planning queries.<n>In low-level Rubik's Cube restoration tasks, CubeRobot achieved a high accuracy rate of 100%, similar to 100% in medium-level tasks, and achieved an accuracy rate of 80% in high-level tasks.
  arXiv  Detail & Related papers  (2025-03-25T02:23:47Z)
• BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom's-Taxonomy-Inspired Prompts [59.83547898874152]
  BloomWise is a cognitively-inspired prompting technique for large language models (LLMs)<n>It is designed to enhance LLMs' performance on mathematical problem solving while making their solutions more explainable.
  arXiv  Detail & Related papers  (2024-10-05T09:27:52Z)
• Private Knowledge Sharing in Distributed Learning: A Survey [50.51431815732716]
  The rise of Artificial Intelligence has revolutionized numerous industries and transformed the way society operates. It is crucial to utilize information in learning processes that are either distributed or owned by different entities. Modern data-driven services have been developed to integrate distributed knowledge entities into their outcomes.
  arXiv  Detail & Related papers  (2024-02-08T07:18:23Z)
• 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)
• Learning to solve arithmetic problems with a virtual abacus [0.35911228556176483]
  We introduce a deep reinforcement learning framework that allows to simulate how cognitive agents could learn to solve arithmetic problems. The proposed model successfully learns to perform multi-digit additions and subtractions, achieving an error rate below 1%. We analyze the most common error patterns to better understand the limitations and biases resulting from our design choices.
  arXiv  Detail & Related papers  (2023-01-17T13:25:52Z)
• Are Deep Neural Networks SMARTer than Second Graders? [85.60342335636341]
  We evaluate the abstraction, deduction, and generalization abilities of neural networks in solving visuo-linguistic puzzles designed for children in the 6--8 age group. Our dataset consists of 101 unique puzzles; each puzzle comprises a picture question, and their solution needs a mix of several elementary skills, including arithmetic, algebra, and spatial reasoning. Experiments reveal that while powerful deep models offer reasonable performances on puzzles in a supervised setting, they are not better than random accuracy when analyzed for generalization.
  arXiv  Detail & Related papers  (2022-12-20T04:33:32Z)
• Knowledge acquisition via interactive Distributed Cognitive skill Modules [0.0]
  The human's cognitive capacity for problem solving is always limited to his/her educational background, skills, experiences, etc. This work aims to introduce an early stage of a modular approach to procedural skill acquisition and storage via distributed cognitive skill modules.
  arXiv  Detail & Related papers  (2022-10-13T01:41:11Z)
• 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)
• Self-Supervision is All You Need for Solving Rubik's Cube [0.0]
  This work introduces a simple and efficient deep learning method for solving problems with a predefined goal, represented by Rubik's Cube. We demonstrate that, for such problems, training a deep neural network on random scrambles branching from the goal state is sufficient to achieve near-optimal solutions.
  arXiv  Detail & Related papers  (2021-06-06T15:38:50Z)
• Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills [155.11646755470582]
  'Explore, Discover and Learn' (EDL) is an alternative approach to information-theoretic skill discovery. We show that EDL offers significant advantages, such as overcoming the coverage problem, reducing the dependence of learned skills on the initial state, and allowing the user to define a prior over which behaviors should be learned.
  arXiv  Detail & Related papers  (2020-02-10T10:49:53Z)

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.