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

Summary

Autonomous systems inevitably age, yet their artificial intelligence typically assumes hardware remains in its original condition.

Abstract Summary

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.

Key Points

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.

The contract nobody reads

Modern artificial intelligence systems implicitly assume stable hardware: that sensors faithfully measure the world, that batteries deliver their rated energy, that processors execute without error, and that memory retains what it stores.

This assumption holds reasonably well in the short term but becomes increasingly unreliable over longer deployment timescales, and the underlying physics has been well-documented for decades.

The contract nobody reads — Machines that know they are aging: a framework for hardware-aware autonomous intelligence — Figure 1: The Aging-Aware Autonomous Intelligence (AAAI) Architecture. The framework comprises three functional pillars that connect physical hardware state to cognitive behaviour. Pillar 1, the cognitive aging model, represents the condition of each subsystem, namely power, sensing, memory, and computation, as a continuous health scalar $h\in[0,1]$, updated in real time using physics-of-failure models. Pillar 2, the self-adaptive reasoning engine, adjusts inference complexity, planning horizon, and task prioritisation in proportion to current hardware capability. Pillar 3, the survival-centric intelligence layer, distributes remaining hardware life across mission objectives through three operational modes: full-health operation, resource-conservation, and graceful withdrawal to a minimal functional core. Together, the three pillars maintain task performance as hardware degrades, in contrast to the aging-agnostic baseline, which undergoes abrupt mission failure as accumulated degradation silently crosses an unmonitored threshold, the condition we term agnostic collapse.

The collapse nobody planned

We term this failure mode agnostic collapse. It does not manifest as a discrete fault event, but rather as a gradual drift in hardware capability that crosses a performance threshold without triggering conventional diagnostics. Hardware capabilities decline silently until they fall below the thresholds the AI has always assumed, and mission performance degrades without warning.

What existing approaches offer and where they stop

Recent work in AI-based device health monitoring addresses aspects of this problem but remains at the hardware management level, sensing degradation, adjusting resource allocation, and protecting components from further wear. Cognitive adaptation, in which the system actively reasons about its physical condition and modifies planning and prioritisation accordingly, remains absent.

The following maps existing research to the pillar it most closely addresses, making explicit where prior work ends and where AAAI begins.

The cognitive gap

Existing research addresses hardware health monitoring and model-complexity matching but stops short of cognitive integration: a unified architecture in which continuous multi-subsystem health estimation actively shapes reasoning, planning, and mission strategy in real time.

To the best of our knowledge, a system that integrates all four layers and explicitly links hardware health decisions to higher-level mission objectives has not been reported.

Three ways to grow old gracefully

The first pillar of Aging-Aware Autonomous Intelligence (AAAI) is genuine hardware self-knowledge (Fig. 1 ). We represent the condition of each critical subsystem, namely: power, sensing, memory, and computation, as a number between one (fully healthy) and zero (failed), continuously updated using the same physics-of-failure models that materials scientists apply to predict degradation [ 7 ] .

Think within your limits

Hardware self-awareness confers limited autonomous operational benefit unless it actively informs system behaviour. The second pillar of AAAI provides a reasoning layer that dynamically modulates task scope and complexity as a function of available hardware capacity. Under nominal computational and memory conditions, AAAI deploys high-capacity deep learning models with extended planning horizons.

Survive with purpose

The third pillar extends the mission objective to include active management of remaining operational capacity. Rather than treating operational lifespan as a passive consequence of hardware endurance, AAAI treats it as a resource to be explicitly allocated across competing mission priorities, with the objective of maximising mission utility over the remaining deployment period.

This framework translates into three adaptive operational states. Under healthy conditions, the system operates normally with full capability.

What this means in practice

In low Earth orbit, microsatellites are continuously exposed to thermal cycling and ionising radiation, both of which progressively degrade processors, memory, and power subsystems.

An ageing-aware architecture can adapt its operational profile in response, for example by reducing processor activity during thermally stressful orbital phases, adjustments that may cumulatively extend functional lifetime over longer deployment periods.

Sources and Demonstrations

Figures are reproduced from Chin et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.