# Digital Gene: Learning about the Physical World through Analytic Concepts

arXiv: https://arxiv.org/abs/2504.04170 · HTML: https://mlsanity.com/paper/2504.04170/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 4 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [ConceptFactory: Facilitate 3D Object Knowledge Annotation with Object Conceptualization](https://mlsanity.com/paper/2411.00448/) (2024)
- [Arti-PG: A Toolbox for Procedurally Synthesizing Large-Scale and Diverse Articulated Objects with Rich Annotations](https://mlsanity.com/paper/2412.14974/) (2024)
- [Discovering Conceptual Knowledge with Analytic Ontology Templates for Articulated Objects](https://mlsanity.com/paper/2409.11702/) (2024)
- [Physically Ground Commonsense Knowledge for Articulated Object Manipulation with Analytic Concepts](https://mlsanity.com/paper/2503.23348/) (2025)

## Built on by (most cited first, 3 of 3)

- [SceneParser: Hierarchical Scene Parsing for Visual Semantics Understanding](https://mlsanity.com/paper/2605.14923/) (2026)
- [Analytic Dynamics: Learning Physics-Grounded Representation for Fast Intrinsic Dynamics Inference from Monocular Videos](https://mlsanity.com/paper/2608.31025/) (2026)
- [NeuIDO: Neural Intrinsic Dynamics Operator for Physics-Informed 4D World Models](https://mlsanity.com/paper/2609.24313/) (2026)

## Abstract (arXiv)

Reviewing the progress in artificial intelligence over the past decade, various significant advances (e.g. object detection, image generation, large language models) have enabled AI systems to produce more semantically meaningful outputs and achieve widespread adoption in internet scenarios. Nevertheless, AI systems still struggle when it comes to understanding and interacting with the physical world. This reveals an important issue: relying solely on semantic-level concepts learned from internet data (e.g. texts, images) to understand the physical world is far from sufficient -- machine intelligence currently lacks an effective way to learn about the physical world. This research introduces the idea of analytic concept -- representing the concepts related to the physical world through programs of mathematical procedures, providing machine intelligence a portal to perceive, reason about, and interact with the physical world. Except for detailing the design philosophy and providing guidelines for the application of analytic concepts, this research also introduce about the infrastructure that has been built around analytic concepts. I aim for my research to contribute to addressing these questions: What is a proper abstraction of general concepts in the physical world for machine intelligence? How to systematically integrate structured priors with neural networks to constrain AI systems to comply with physical laws?