# Machines that know they are aging: a framework for hardware-aware autonomous intelligence

arXiv: https://arxiv.org/abs/2607.28451 · HTML: https://mlsanity.com/paper/2607.28451/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 30 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks](https://mlsanity.com/paper/1911.03852/) (2019)
- [ReaLM: Reliable and Efficient Large Language Model Inference with Statistical Algorithm-Based Fault Tolerance](https://mlsanity.com/paper/2503.24053/) (2025)
- [Robot Vitals and Robot Health: Towards Systematically Quantifying Runtime Performance Degradation in Robots Under Adverse Conditions](https://mlsanity.com/paper/2207.01684/) (2022)
- [Aging-aware CPU Core Management for Embodied Carbon Amortization in Cloud LLM Inference](https://mlsanity.com/paper/2501.15829/) (2025)
- [On-Chip Sensors Data Collection and Analysis for SoC Health Management](https://mlsanity.com/paper/2308.15917/) (2023)
- [Extending Silicon Lifetime: A Review of Design Techniques for Reliable Integrated Circuits](https://mlsanity.com/paper/2503.21165/) (2025)
- [Aging Aware Adaptive Voltage Scaling for Reliable and Efficient AI Accelerators](https://mlsanity.com/paper/2604.09994/) (2026)

## Abstract (arXiv)

Autonomous systems inevitably age, yet their artificial intelligence typically assumes hardware remains in its original condition. Batteries degrade, sensors drift, processors accumulate timing errors, and memory reliability declines, creating a growing mismatch between assumed and actual capability. This can lead to agnostic collapse, where mission failure arises from accumulated hardware degradation rather than a single component fault. We propose Aging-Aware Autonomous Intelligence (AAAI), a framework that integrates hardware health directly into reasoning, planning, and mission execution. AAAI is built on three pillars: hardware self-awareness, which continuously estimates the health of power, sensing, memory, and computation subsystems using physics-of-failure models; self-adaptive reasoning, which adjusts inference complexity, planning horizon, and task priorities according to remaining hardware capability; and survival-centric intelligence, which allocates remaining operational life across mission objectives through performance optimization, resource conservation, and graceful degradation. Rather than introducing new hardware, AAAI unifies prognostics, lifecycle management, and hardware-aware computing into a closed-loop cognitive architecture. We argue that such integration is essential for autonomous systems operating in inaccessible or safety-critical environments, including space missions, marine robotics, and implantable medical devices. By enabling machines to recognize and respond to their own aging, AAAI improves resilience, extends operational lifetime, and supports safer, more graceful mission completion.