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Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents

Paper recorded by Signals 4 on 2026-09-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.AI · 人工智能 · first seen 2026-09-03

Abstract

On-premise assistants can give factory workers conversational access to machine documentation, but models capable of the task rarely fit shop-floor hardware. We show that after structural compression and retrieval-grounded adaptation, model size is no longer a reliable predictor of adapted answer quality: general capability falls almost linearly with parameter count, while judged retrieval-augment

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#210 most recent of 300 cs.AI papers we have recorded · ↑ newer: From Reweighting to Rewriting: Unlocking the Intervention Effects of I · ↓ older: Untangling the Mechanisms of Misleading Context in Medical Question An
Cite this page: Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents: the #210 most recent of 300 cs.AI papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/measurement-driven-sub-network-selection-for-on-premise-retrieval-augmented-fact.html
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