CogScale: Scalable Benchmark for Sequence Processing
The paper introduces CogScale, a benchmark designed to evaluate the cognitive and memory abilities of various AI architectures. It presents 14 scalable synthetic tasks that allow researchers to test models efficiently without incurring high computational costs. The study evaluates seven different architectures, revealing that modern models perform better as task complexity increases.
- ▪CogScale is a benchmark consisting of 14 scalable synthetic tasks for evaluating AI architectures.
- ▪The benchmark allows for rapid validation of architectural innovations before large-scale training.
- ▪The study evaluates seven architectures, including GRU, LSTM, and Transformer models.
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Computer Science > Artificial Intelligence arXiv:2605.19758 (cs) [Submitted on 19 May 2026] Title:CogScale: Scalable Benchmark for Sequence Processing Authors:Yannis Bendi-Ouis (Mnemosyne), Romain de Coudenhove (ENS-PSL), Xavier Hinaut (Mnemosyne) View a PDF of the paper titled CogScale: Scalable Benchmark for Sequence Processing, by Yannis Bendi-Ouis (Mnemosyne) and 2 other authors View PDF Abstract:The ability to maintain and manipulate information over time is a fundamental aspect of living beings and Artificial Intelligence. While modern models have achieved remarkable success in tasks like natural language processing, evaluating the capacity of novel architectures to process sequential information remains computationally expensive and time-consuming.
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