arXiv subjects

Panagiotis Michelakis

Publications and source records attributed to Panagiotis Michelakis.

3 recordsLinked to original sources

arXiv2026

Deploying and Evaluating a Smart-Agriculture Agentic Engine for Full-Season Soybean Farm Operations

This paper presents FAIRY, a full-stack smart-agriculture agent system developed for and deployed to an operating soybean research farm at Harbin Institute of Technology's smart-agriculture site. We develop FAIRY to execute and evaluate agentic agronomic operations on full-season spatiotemporal workflows that span ridge preparation, planting, irrigation, fertilization, pest and disease treatment, harvest, grain handling, drying, and storage. FAIRY integrates APIs and infrastructure across production-grade machinery, fixed soil and canopy sensors, multispectral and thermal drones, satellite vegetation products, a weather station, calibrated crop-process models, agronomic records, and multi-season yield histories. The system is built around the novel "everything is an event" execution paradigm, which represents spatiotemporal world evolution, remote sensing and UAV observations, sensor readings, crop-growth transitions, machinery actions, and management interventions as state-changing events in a shared farm process engine. On top of this event-driven world model, FAIRY implements a complete agentic stack: a knowledge library of atomic agronomic skills; multi-agent controller and orchestration backends; frontier- and edge-model execution; full-path trace logging; and deployment profiling on local nodes. We use FAIRY to evaluate nine state-of-the-art agent controllers across one hundred full-season soybean scenarios that preserve the operational coupling between spatial observations in a 64-ridge field, temporal decision sequences, agronomic constraints, delayed effects, and final yield. We develop an evaluation suite that combines agentic success, full-path spatiotemporal correctness, token cost, and edge-device runtime.

cs.AI↗

arXiv2026

Toward a Small ML Runtime Stack for Raspberry Pi 5 QPUs

We present a QPU-first ML runtime stack for Raspberry Pi 5's VideoCore VII QPU, built on top of the py-videocore7 assembly library. The system comprises reusable tiled matrix-multiplication substrate, GEMM-backed convolution, a single-head attention-style core, persistent executors, and integer execution based on smul24 instructions. For dense integer kernels, packed INT16-input with INT32 accumulation achieves nearly two orders of magnitude higher throughput over NumPy. Across operations (min/max, pooling, convolution, attention), we report improved performance over both PyTorch and NumPy. Our preliminary results indicate that Raspberry QPUs can serve as a practical execution substrate towards accelerating AI model execution at the edge.

cs.AR↗

arXiv2025

CORE: Full-Path Evaluation of LLM Agents Beyond Final State

Evaluating AI agents that solve real-world tasks through function-call sequences remains an open challenge. Existing agentic benchmarks often reduce evaluation to a binary judgment of the final state, overlooking critical aspects such as safety, efficiency, and intermediate correctness. We propose a framework based on deterministic finite automata (DFAs) that encodes tasks as sets of valid tool-use paths, enabling principled assessment of agent behavior in diverse world models. Building on this foundation, we introduce CORE, a suite of five metrics, namely Path Correctness, Path Correctness - Kendall's tau Composite, Prefix Criticality, Harmful-Call Rate, and Efficiency, that quantify alignment with expected execution patterns. Across diverse worlds, our method reveals important performance differences between agents that would otherwise appear equivalent under traditional final-state evaluation schemes.

cs.AI↗