Search arXiv⌕ Search

arXiv · 2506.11001

Rethinking Technological Readiness in the Era of AI Uncertainty

Abstract

Artificial intelligence (AI) is poised to revolutionize military combat systems, but ensuring these AI-enabled capabilities are truly mission-ready presents new challenges. We argue that current technology readiness assessments fail to capture critical AI-specific factors, leading to potential risks in deployment. We propose a new AI Readiness Framework to evaluate the maturity and trustworthiness of AI components in military systems. The central thesis is that a tailored framework - analogous to traditional Technology Readiness Levels (TRL) but expanded for AI - can better gauge an AI system's reliability, safety, and suitability for combat use. Using current data evaluation tools and testing practices, we demonstrate the framework's feasibility for near-term implementation. This structured approach provides military decision-makers with clearer insight into whether an AI-enabled system has met the necessary standards of performance, transparency, and human integration to be deployed with confidence, thus advancing the field of defense technology management and risk assessment.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

S. Tucker Browne, Mark M. Bailey. 2025-04-15. Rethinking Technological Readiness in the Era of AI Uncertainty. https://arxiv.org/abs/2506.11001

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap

Search-based software engineering (SBSE), which integrates metaheuristic search techniques with software engineering, has been an active area of research for about 25 years. It has been applied to solve numerous problems across the entire software engineering lifecycle and has demonstrated its versatility in multiple domains. With recent advances in Artificial Intelligence (AI), particularly the emergence of foundation models (FMs) such as large language models (LLMs), the evolution of SBSE alongside these models remains undetermined. In this window of opportunity, we present a research roadmap that articulates the current landscape of SBSE in relation to FMs, identifies open challenges, and outlines potential research directions to advance SBSE through its synergy with FMs. Specifically, we analyze three core aspects: utilizing FMs to enhance SBSE, applying SBSE to advance FMs, and exploring the integration of SBSE and FMs. Furthermore, we present a forward-thinking perspective that envisions the future of SBSE in the era of FMs, highlighting promising research opportunities to address challenges in emerging domains.

cs.SE↗

QMon: Monitoring the Execution of Quantum Circuits with Mid-Circuit Measurement and Reset

Unlike classical software, where logging and runtime tracing can effectively reveal internal execution status, quantum circuits possess unique properties, such as the no-cloning theorem and measurement-induced collapse, that prevent direct observation or duplication of their states. These characteristics make it especially challenging to monitor the execution of quantum circuits, complicating essential tasks such as debugging and runtime monitoring. This paper presents QMon, a practical methodology that leverages mid-circuit measurements, reset operations, and causal-cone replay to monitor selected intermediate values of quantum circuits while preserving their original runtime behavior under explicit conditions. QMon enables the instrumentation of monitoring operators at selected locations within the circuit, allowing comparisons between expected and observed one-qubit outcome probabilities at those locations. Under an ideal noise-free model, we prove that QMon preserves the full circuit state when the monitored qubit is separable, replay is exact, and the measurement record does not control later operations. Across 310 benchmark circuits with a 24-qubit limit per run, QMon monitors 44.54% of gate-qubit locations and 89.75% of circuit qubits at least once, on average. On 2,860 simulated buggy circuits (mutated circuits that alter final outputs), it achieves a detection rate of 62.4%, remaining competitive with three assertion baselines while requiring a median of one planned run per circuit, compared with 21 for the assertion baselines. By collecting multiple checkpoint observations within continued executions, QMon combines practical efficiency with an exact preservation guarantee under explicit conditions.

cs.SE↗

Self-Evolving Coding Agents

Large language models are increasingly embedded in software engineering workflows as coding agents that can inspect repositories, invoke tools, execute tests, debug failures, and generate patches. Yet most existing coding agents remain largely static after deployment, even though software development is a dynamic, feedback-rich process in which repositories evolve, dependencies change, tests fail, and repair attempts leave reusable experience. This tension has motivated a growing body of work on self-evolving coding agents, where the agent improves its future behavior by persistently updating its framework, memory, skills and tools, components, workflow and topology, or environment and context from prior coding interactions. In this survey, we provide a structured synthesis of this emerging area. We first define the concept of self-evolving coding agents and distinguish it from conventional coding agents and general self-evolving agents. We then develop a taxonomy centered on the targets of evolution, complemented by two orthogonal perspectives: when evolution occurs and which code-specific signals drive it. We further examine the benchmarks used to measure the effect of evolution and related coding products. Across the literature, we find that executable feedback, repository-level context, and coding trajectories make software engineering a natural domain for agent self-evolution, but also introduce challenges in feedback reliability, benchmark overfitting, reversibility, system complexity, safety, cost, and generalization. By organizing existing work around these dimensions, this survey aims to clarify the conceptual boundaries of self-evolving coding agents and provide a foundation for designing more adaptive, reliable, and software-aware agentic systems.

cs.SE↗