mlsanityData to 7 Oct 2026

Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus

arXiv 2210.09880 ↗First posted 18 Oct 2022cs.AIcited by 32

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Abstract (arXiv)

The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the performance of general artificial intelligence algorithms. The ARC's focus on broad generalization and few-shot learning has made it difficult to solve using pure machine learning. A more promising approach has been to perform program synthesis within an appropriately designed Domain Specific Language (DSL). However, these too have seen limited success. We propose Abstract Reasoning with Graph Abstractions (ARGA), a new object-centric framework that first represents images using graphs and then performs a search for a correct program in a DSL that is based on the abstracted graph space. The complexity of this combinatorial search is tamed through the use of constraint acquisition, state hashing, and Tabu search. An extensive set of experiments demonstrates the promise of ARGA in tackling some of the complicated object-centric tasks of the ARC rather efficiently, producing programs that are correct and easy to understand.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 18 Oct 2022.

Builds on

6 of 28 references.

References · 6

  1. 2020DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learningcited by 159
  2. 2021Communicating Natural Programs to Humans and Machinescited by 35
  3. 2021Fast and flexible: Human program induction in abstract reasoning taskscited by 33
  4. 2021Neural-guided, Bidirectional Program Search for Abstraction and Reasoningcited by 23
  5. 2021First Steps of an Approach to the ARC Challenge based on Descriptive Grid Models and the Minimum Description Length Principlecited by 13
  6. 2020Neural Abstract Reasonercited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 9; 2024: 14; 2025: 4; 2026: 5.

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  1. 2023Large Language Models as General Pattern Machinescited by 196
  2. 2023Hypothesis Search: Inductive Reasoning with Language Modelscited by 119
  3. 2023The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domaincited by 97
  4. 2023LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representationscited by 81
  5. 2024Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpuscited by 32
  6. 2023Abstract Visual Reasoning Enabled by Languagecited by 15
  7. 2024HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesiscited by 14
  8. 2024Neural networks for abstraction and reasoning: Towards broad generalization in machinescited by 13
  9. 2023Unraveling the ARC Puzzle: Mimicking Human Solutions with Object-Centric Decision Transformercited by 12
  10. 2023Large Language Model (LLM) as a System of Multiple Expert Agents: An Approach to solve the Abstraction and Reasoning Corpus (ARC) Challengecited by 11
  11. 2024Generalized Planning for the Abstraction and Reasoning Corpuscited by 10
  12. 2024Tackling the Abstraction and Reasoning Corpus with Vision Transformers: the Importance of 2D Representation, Positions, and Objectscited by 9
20 more
  1. 2023Solving ARC visual analogies with neural embeddings and vector arithmetic: A generalized methodcited by 8
  2. 2024ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)cited by 8
  3. 2024ARCLE: The Abstraction and Reasoning Corpus Learning Environment for Reinforcement Learningcited by 7
  4. 2024Creativity in AI: Progresses and Challengescited by 7
  5. 2023Tackling the Abstraction and Reasoning Corpus (ARC) with Object-centric Models and the MDL Principlecited by 6
  6. 2024Relational decomposition for program synthesiscited by 6
  7. 2024Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language Modelscited by 4
  8. 2024Program Synthesis using Inductive Logic Programming for the Abstraction and Reasoning Corpuscited by 4
  9. 2025Language Models, Graph Searching, and Supervision Adulteration: When More Supervision is Less and How to Make More Morecited by 3
  10. 2026One Step Forward and K Steps Back: Better Reasoning with Denoising Recursion Modelscited by 3
  11. 2024Abductive Symbolic Solver on Abstraction and Reasoning Corpuscited by 2
  12. 2025NSA: Neuro-symbolic ARC Challengecited by 2
  13. 2025From Reasoning to Generalization: Knowledge-Augmented LLMs for ARC Benchmarkcited by 2
  14. 2024Intelligence Analysis of Language Modelscited by 1
  15. 2025Vector Symbolic Algebras for the Abstraction and Reasoning Corpuscited by 1
  16. 2026TraceViT: Grounded Trace Supervision for Visual Abstract Reasoningcited by 1
  17. 2024System 2 Reasoning for Human-AI Alignment: Generality and Adaptivity via ARC-AGIcited by 0
  18. 2026From Global to Factor-Wise Expert Composition in Discrete Diffusion Modelscited by 0
  19. 2026Narcissus: Program Synthesis Using Context-Aware LLM Approximationscited by 0
  20. 2026A Multi-Stage Rule-Chaining Framework for Compositional and Interpretable Cognitive Reasoningcited by 0