# Situation Perception: A Necessary Primitive to Artificial Superintelligence

arXiv: https://arxiv.org/abs/2606.30481 · HTML: https://mlsanity.com/paper/2606.30481/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 29 Jun 2026.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Mastering Atari with Discrete World Models](https://mlsanity.com/paper/2010.02193/) (2020)
- [Causal World Modeling for Robot Control](https://mlsanity.com/paper/2601.21998/) (2026)
- [TextWorld: A Learning Environment for Text-based Games](https://mlsanity.com/paper/1806.11532/) (2018)
- [BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning](https://mlsanity.com/paper/1810.08272/) (2018)
- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design](https://mlsanity.com/paper/2603.01229/) (2026)
- [Learning Object Permanence from Video](https://mlsanity.com/paper/2003.10469/) (2020)
- [REMem: Reasoning with Episodic Memory in Language Agent](https://mlsanity.com/paper/2602.13530/) (2026)
- [World-Value-Action Model: Implicit Planning for Vision-Language-Action Systems](https://mlsanity.com/paper/2604.14732/) (2026)
- [MemoryWAM: Efficient World Action Modeling with Persistent Memory](https://mlsanity.com/paper/2606.20562/) (2026)
- [World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child](https://mlsanity.com/paper/2503.15168/) (2025)
- [Looped World Models](https://mlsanity.com/paper/2606.18208/) (2026)
- [Analyzing Advanced AI Systems Against Definitions of Life and Consciousness](https://mlsanity.com/paper/2502.05007/) (2025)
- [Alignment Is Not Enough: A Relational Framework for Moral Standing in Human-AI Interaction](https://mlsanity.com/paper/2603.00078/) (2026)
- [X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling](https://mlsanity.com/paper/2605.24892/) (2026)

## Abstract (arXiv)

Current large language models are extraordinary statistical engines. They compress vast amounts of text into useful patterns and can explain science, write code, imitate reasoning, and participate in philosophical conversation. Yet pattern mastery is not the same as general intelligence. A human infant begins with little explicit knowledge, but gradually discovers object permanence, cause and effect, other minds, bodily agency, and the persistence of the physical world. We make an argument that the path to artificial superintelligence (ASI) depends on a missing capacity we call situation perception: the ability to construct, revise, and act within internal simulations of possible worlds across latent time.  perception requires at least three core components: abstract prediction, long-term compressed memory, and active learning guided by objectives. In this work, we analyse why modern large language models remain incomplete, and propose the appropriate tests for measuring progress and consequences of machines that can simulate futures, pursue self-directed goals, and possibly judge their own creators.