Vector Symbolic Algebras for the Abstraction and Reasoning Corpus

• URL: http://arxiv.org/abs/2511.08747v1
• Date: Thu, 13 Nov 2025 01:05:26 GMT
• Title: Vector Symbolic Algebras for the Abstraction and Reasoning Corpus
• Abstract summary: ARC-AGI is a generative, few-shot fluid intelligence benchmark.<n>We propose a cognitively plausible ARC-AGI solver.<n>Our solver integrates System 1 intuitions with System 2 reasoning.
• Score: 1.2031796234206134
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) is a generative, few-shot fluid intelligence benchmark. Although humans effortlessly solve ARC-AGI, it remains extremely difficult for even the most advanced artificial intelligence systems. Inspired by methods for modelling human intelligence spanning neuroscience to psychology, we propose a cognitively plausible ARC-AGI solver. Our solver integrates System 1 intuitions with System 2 reasoning in an efficient and interpretable process using neurosymbolic methods based on Vector Symbolic Algebras (VSAs). Our solver works by object-centric program synthesis, leveraging VSAs to represent abstract objects, guide solution search, and enable sample-efficient neural learning. Preliminary results indicate success, with our solver scoring 10.8% on ARC-AGI-1-Train and 3.0% on ARC-AGI-1-Eval. Additionally, our solver performs well on simpler benchmarks, scoring 94.5% on Sort-of-ARC and 83.1% on 1D-ARC -- the latter outperforming GPT-4 at a tiny fraction of the computational cost. Importantly, our approach is unique; we believe we are the first to apply VSAs to ARC-AGI and have developed the most cognitively plausible ARC-AGI solver yet. Our code is available at: https://github.com/ijoffe/ARC-VSA-2025.

Related papers

• The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning [5.870990919877351]
  The Abstraction and Reasoning Corpus (ARC-AGI) has become a key benchmark for fluid intelligence in AI.<n>This survey presents the first cross-generation analysis of 82 approaches across three benchmark versions and the ARC Prize 2024-2025 competitions.
  arXiv  Detail & Related papers  (2026-03-09T23:16:19Z)
• Think Visually, Reason Textually: Vision-Language Synergy in ARC [94.15522924153264]
  ARC-AGI is a rigorous testbed for conceptual rule induction and transfer to novel tasks.<n>Most existing methods treat ARC-AGI as a purely textual reasoning task, overlooking the fact that humans rely heavily on visual abstraction.<n>We introduce two synergistic strategies: Vision-Language Synergy Reasoning (VLSR) and Modality-Switch Self-Correction (MSSC)<n>Our findings suggest that unifying visual abstraction with linguistic reasoning is a crucial step toward achieving generalizable, human-like intelligence.
  arXiv  Detail & Related papers  (2025-11-19T18:59:04Z)
• ARC Is a Vision Problem! [50.59206008530851]
  We formulate ARC within a vision paradigm, framing it as an image-to-image translation problem.<n>Our framework, termed Vision ARC, achieves 60.4% accuracy on the ARC-1 benchmark.
  arXiv  Detail & Related papers  (2025-11-18T18:59:49Z)
• GIFARC: Synthetic Dataset for Leveraging Human-Intuitive Analogies to Elevate AI Reasoning [7.09254962218677]
  State-of-the-art models still achieve accuracy rates of merely 40-55% on 2024 ARC Competition.<n>We introduce an analogy-inspired ARC dataset, GIFARC.<n>GIFARC guides AI agents to evaluate the task analogically before engaging in brute-force pattern search.
  arXiv  Detail & Related papers  (2025-05-27T03:42:51Z)
• Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzles [46.71887319140096]
  We introduce Enigmata, the first comprehensive suite tailored for improving Large Language Models with puzzle reasoning skills.<n>It includes 36 tasks across seven categories, each with 1) a generator that produces unlimited examples with controllable difficulty and 2) a rule-based verifier for automatic evaluation.<n>Our trained model, Qwen2.5-32B-Enigmata, consistently surpasses o3-mini-high and o1 on the puzzle reasoning benchmarks.
  arXiv  Detail & Related papers  (2025-05-26T12:40:31Z)
• ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems [0.03431023404301193]
  ARC-AGI-2 preserves the input-output pair task format of its predecessor, ensuring continuity for researchers.<n>It incorporates a newly curated and expanded set of tasks specifically designed to assess abstract reasoning and problem-solving abilities.<n> ARC-AGI-2 aims to serve as a next-generation tool for rigorously measuring progress towards more general and human-like AI capabilities.
  arXiv  Detail & Related papers  (2025-05-17T04:34:48Z)
• ARC-NCA: Towards Developmental Solutions to the Abstraction and Reasoning Corpus [0.0]
  ARC-NCA is a developmental approach to tackle the ARC-AGI benchmark.<n> Developmental solutions may offer a promising avenue for enhancing AI's problem-solving capabilities.
  arXiv  Detail & Related papers  (2025-05-13T17:55:43Z)
• Neural networks for abstraction and reasoning: Towards broad generalization in machines [3.165509887826658]
  We look at novel approaches for solving the Abstraction & Reasoning Corpus (ARC) We adapt the DreamCoder neurosymbolic reasoning solver to ARC. We present the Perceptual Abstraction and Reasoning Language (PeARL) language, which allows DreamCoder to solve ARC tasks. We publish the arckit Python library to make future research on ARC easier.
  arXiv  Detail & Related papers  (2024-02-05T20:48:57Z)
• ArchGym: An Open-Source Gymnasium for Machine Learning Assisted Architecture Design [52.57999109204569]
  ArchGym is an open-source framework that connects diverse search algorithms to architecture simulators. We evaluate ArchGym across multiple vanilla and domain-specific search algorithms in designing custom memory controller, deep neural network accelerators, and custom SOC for AR/VR workloads.
  arXiv  Detail & Related papers  (2023-06-15T06:41:23Z)
• LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations [50.431003245201644]
  We show that GPT-4 is unable to "reason" perfectly within non-language domains such as the 1D-ARC or a simple ARC subset. We propose an object-based representation that is obtained through an external tool, resulting in nearly doubling the performance on solved ARC tasks and near-perfect scores on the easier 1D-ARC.
  arXiv  Detail & Related papers  (2023-05-26T16:32:17Z)
• OpenAGI: When LLM Meets Domain Experts [51.86179657467822]
  Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents. We introduce OpenAGI, an open-source platform designed for solving multi-step, real-world tasks.
  arXiv  Detail & Related papers  (2023-04-10T03:55:35Z)
• Abstract Visual Reasoning Enabled by Language [8.627180519837657]
  We propose a general learning-based framework for solving ARC. It is centered on transforming tasks from the vision to the language domain. This composition of language and vision allows for pre-trained models to be leveraged at each stage.
  arXiv  Detail & Related papers  (2023-03-07T17:52:46Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• Communicating Natural Programs to Humans and Machines [43.00640440047027]
  Abstraction and Reasoning Corpus (ARC) is a set of procedural tasks that tests an agent's ability to solve flexibly novel problems. We study the difference of emphlanguage: While humans readily generate and interpret instructions in a general language, computer systems are shackled to a narrow domain-specific language. We analyze the collected instructions as natural programs', finding that while they resemble computer programs, they are distinct in two ways.
  arXiv  Detail & Related papers  (2021-06-15T01:05:04Z)

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.