# Towards Efficient Neurally-Guided Program Induction for ARC-AGI

arXiv: https://arxiv.org/abs/2411.17708 · HTML: https://mlsanity.com/paper/2411.17708/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [RobustFill: Neural Program Learning under Noisy I/O](https://mlsanity.com/paper/1703.07469/) (2017)
- [Latent Execution for Neural Program Synthesis](https://mlsanity.com/paper/2107.00101/) (2021)
- [Representing Partial Programs with Blended Abstract Semantics](https://mlsanity.com/paper/2012.12964/) (2020)
- [Diffusion On Syntax Trees For Program Synthesis](https://mlsanity.com/paper/2405.20519/) (2024)
- [Scaling Neural Program Synthesis with Distribution-based Search](https://mlsanity.com/paper/2110.12485/) (2021)

## Built on by (most cited first, 11 of 11)

- [ARC Prize 2024: Technical Report](https://mlsanity.com/paper/2412.04604/) (2024)
- [ARC-GEN: A Mimetic Procedural Benchmark Generator for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2511.00162/) (2025)
- [The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning](https://mlsanity.com/paper/2603.13372/) (2026)
- [Out-of-Distribution Generalization in the ARC-AGI Domain: Comparing Execution-Guided Neural Program Synthesis and Test-Time Fine-Tuning](https://mlsanity.com/paper/2507.15877/) (2025)
- [Slots, Transitions, Loops: Learning Composable World Models for ARC](https://mlsanity.com/paper/2606.12316/) (2026)
- [Bridging Reasoning to Learning: Unmasking Illusions using Complexity Out of Distribution Generalization](https://mlsanity.com/paper/2510.06274/) (2025)
- [Vector Symbolic Algebras for the Abstraction and Reasoning Corpus](https://mlsanity.com/paper/2511.08747/) (2025)
- [ARCTraj: A Dataset and Benchmark of Human Reasoning Trajectories for Abstract Problem Solving](https://mlsanity.com/paper/2511.11079/) (2025)
- [ARC-TGI: Human-Validated Task Generators with Reasoning Chain Templates for ARC-AGI](https://mlsanity.com/paper/2603.05099/) (2026)
- [TraceViT: Grounded Trace Supervision for Visual Abstract Reasoning](https://mlsanity.com/paper/2607.29586/) (2026)
- [On the Exact Algorithmic Extraction of Finite Tesselations Through Prime Extraction of Minimal Representative Forms](https://mlsanity.com/paper/2603.00911/) (2026)

## Abstract (arXiv)

ARC-AGI is an open-world problem domain in which the ability to generalize out-of-distribution is a crucial quality. Under the program induction paradigm, we present a series of experiments that reveal the efficiency and generalization characteristics of various neurally-guided program induction approaches. The three paradigms we consider are Learning the grid space, Learning the program space, and Learning the transform space. We implement and experiment thoroughly on the first two, and retain the second one for ARC-AGI submission. After identifying the strengths and weaknesses of both of these approaches, we suggest the third as a potential solution, and run preliminary experiments.