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Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents
Vasileios Rizeakos, Georgios Paisios, Alexandros Machairas, Michael Birbas, Athanasios Bachoumis · Read the paper
Hook: For factory assistants, the smallest or fastest model is not necessarily the best choice after retrieval-grounded adaptation.
Summary: The paper presents a measurement-driven method for selecting compressed sub-networks for on-premise retrieval-augmented factory assistants. It selects one sub-network per device using judged answer quality, measured on-device throughput, a configurable general-capability floor, and a memory budget.
Industry impact: In a manufacturing-manual case study, the paper reports that structural extraction reduced judged quality to 13.7 percent of the unpruned model's level, while retrieval-grounded distillation brought it back to within 4.6 percent and recovered two thirds of the loss. The reported assistant ran across three heterogeneous edge tiers at 1.3 to 5 watts of standby power, using a weight-shared supernetwork to make selection inexpensive.
Potential implications: Teams deploying local assistants may need to evaluate adapted answer quality and measured device throughput rather than use parameter count, speed, or quality alone as the selection rule. The paper's results also suggest that a configurable capability floor and memory budget can support different sub-network choices across constrained factory hardware.
