# Just aware enough: Evaluating awareness across artificial systems

arXiv: https://arxiv.org/abs/2601.14901 · HTML: https://mlsanity.com/paper/2601.14901/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 112 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Multi-Agent Collaboration Mechanisms: A Survey of LLMs](https://mlsanity.com/paper/2501.06322/) (2025)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Power Hungry Processing: Watts Driving the Cost of AI Deployment?](https://mlsanity.com/paper/2311.16863/) (2023)
- [Consciousness in Artificial Intelligence: Insights from the Science of Consciousness](https://mlsanity.com/paper/2308.08708/) (2023)
- [Could a Large Language Model be Conscious?](https://mlsanity.com/paper/2303.07103/) (2023)
- [Affordances in Robotic Tasks -- A Survey](https://mlsanity.com/paper/2004.07400/) (2020)
- [I Think, Therefore I am: Benchmarking Awareness of Large Language Models Using AwareBench](https://mlsanity.com/paper/2401.17882/) (2024)
- [The feasibility of artificial consciousness through the lens of neuroscience](https://mlsanity.com/paper/2306.00915/) (2023)
- [On the link between conscious function and general intelligence in humans and machines](https://mlsanity.com/paper/2204.05133/) (2022)
- [Is artificial consciousness achievable? Lessons from the human brain](https://mlsanity.com/paper/2405.04540/) (2024)
- [What is it for a Machine Learning Model to Have a Capability?](https://mlsanity.com/paper/2405.08989/) (2024)
- [Mixture of Experts for Network Optimization: A Large Language Model-enabled Approach](https://mlsanity.com/paper/2402.09756/) (2024)
- [Bodily aware soft robots: integration of proprioceptive and exteroceptive sensors](https://mlsanity.com/paper/1710.05419/) (2017)
- [AI Consciousness is Inevitable: A Theoretical Computer Science Perspective](https://mlsanity.com/paper/2403.17101/) (2024)
- [Preliminaries to artificial consciousness: a multidimensional heuristic approach](https://mlsanity.com/paper/2403.20177/) (2024)
- [Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside''](https://mlsanity.com/paper/2402.09030/) (2024)

## Built on by (most cited first, 1 of 1)

- [Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cues](https://mlsanity.com/paper/2606.31644/) (2026)

## Abstract (arXiv)

Recent debates on artificial intelligence increasingly emphasise questions of AI consciousness and moral status, yet there remains little agreement on how such properties should be evaluated. In this paper, we argue that awareness offers a more productive and methodologically tractable alternative. We introduce a practical method for evaluating awareness across diverse systems, where awareness is understood as encompassing a system's abilities to process, store and use information in the service of goal-directed action. Central to this approach is the claim that any evaluation aiming to capture the diversity of artificial systems must be domain-sensitive, deployable at any scale, multidimensional, and enable the prediction of task performance, while generalising to the level of abilities for the sake of comparison. Given these four desiderata, we outline a structured approach to evaluating and comparing awareness profiles across artificial systems with differing architectures, scales, and operational domains. By shifting the focus from artificial consciousness to being just aware enough, this approach aims to facilitate principled assessment, support design and oversight, and enable more constructive scientific and public discourse.