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cs.AI updates on arXiv.org

In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models TIGER: Text-Informed Generalized Enzyme-Reaction Retrieval Hypothesis Generation and Inductive Inference in Children and Language Models Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs Confidence Calibration in Large Language Models Hylos: Operability Contracts for Model-Native Spatial Intelligence Toward Enactive Artificial Intelligence AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning Remote sensing data imputation using deep learning for multispectral imagery Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation High-Risk AI Systems and the Problem of Identity in the European AI Act MobileGym: A Verifiable and Highly Parallel Simulation Platform for Mobile GUI Agent Research ConceptM$^3$oE: Concept-Guided Multimodal Mixture of Experts for Interpretable Computational Pathology Safety-Oriented Routing Analysis of Mixtral MoE Under Benign and Harmful Prompts Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis Reason--Imagine--Act: Closed-Loop LLM Decision Making with World Models for Autonomous Driving When Does Multi-Agent RL Improve LLM Workflows? 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AI-Driven Controlled Environment Agriculture as Resilient Infrastructure for U.S. Fresh-Produce Supply Chains
Andrii Vakhn · 2026-05-26 · via cs.AI updates on arXiv.org

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Abstract:Climate volatility, regional production concentration, labor constraints, cyber risk, and dependence on long-distance fresh-produce supply chains expose vulnerabilities in U.S. fresh-produce and specialty-crop
systems. Controlled environment agriculture (CEA) can reduce some exposure by moving selected production into protected, sensor-rich environments, but recent failures in venture-backed vertical farming show
that CEA cannot be treated as a universal food-security solution. This paper proposes the Controlled Environment Agriculture Resilience Infrastructure Framework, Version 2.0 (CEA-RIF 2.0), for evaluating
AI-driven CEA as targeted regional fresh-produce continuity infrastructure. The framework assesses seven dimensions: supply continuity, climate isolation, energy and grid integration, water and nutrient
circularity, cyber-physical reliability, economic viability, and governance and deployment. Drawing on U.S. government reports, peer-reviewed CEA and energy literature, demand-response research, cybersecurity
standards, international smart-agriculture programs, 2025-2026 financing and policy signals, and public autonomous-greenhouse datasets, the paper argues that AI creates resilience value only when it improves
measured operational outcomes such as climate stability, energy flexibility, yield consistency, anomaly detection, labor productivity, and safe recovery from faults. The analysis reframes AI-driven CEA as a
cyber-physical infrastructure problem: energy-aware, grid-interactive, secure, interoperable, regionally distributed, financially disciplined, and connected to public resilience goals. The paper concludes with
a research agenda for interagency testbeds, open datasets, standardized metrics, demand-response pilots, and cyber-physical reference architectures.
Comments: 12 pages, 5 figures, 7 tables. Includes open-data greenhouse control metrics demonstration
Subjects: Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
ACM classes: I.2.1; J.2
Cite as: arXiv:2605.23946 [cs.CY]
  (or arXiv:2605.23946v1 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.2605.23946

arXiv-issued DOI via DataCite

Submission history

From: Andrii Vakhnovskyi [view email]
[v1] Mon, 4 May 2026 22:21:06 UTC (190 KB)