arXiv:2605.28508v1 Announce Type: new Abstract: Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality. Through a structured analysis of existing benchmark families across speech, chat/RAG, and vision systems, we identify critical gaps between laboratory evaluation practices and real-world deployment conditions in low-resource environments. We argue that the meaningful unit of assessment is the deployed system rather than an isolated model and that effective evaluat
The proliferation of AI models demands practical evaluation in diverse, real-world operational contexts, especially as deployment moves beyond well-resourced environments.
This highlights a critical mismatch between current AI evaluation practices and the actual performance needs of systems deployed in low-resource settings, impacting global AI adoption and utility.
The focus shifts from isolated model quality to the broader performance of deployed systems, incorporating operational constraints as key evaluation metrics.
- · AI developers focused on efficiency and robustness
- · Organizations in low-resource environments
- · Edge computing platforms
- · Governments seeking equitable AI solutions
- · AI labs solely focused on leaderboard metrics
- · Developers producing energy-intensive or resource-heavy models
- · Cloud-dependent AI solutions in underserved areas
AI development priorities will pivot towards resource efficiency, smaller models, and robust-to-constraint performance.
This drives innovation in novel architectural designs suitable for deployment on limited hardware and intermittent connectivity.
It could lead to the emergence of localized, decentralized AI ecosystems with strong implications for data sovereignty and control.
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Read at arXiv cs.AI