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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19758 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 3Nigc13tqcOi |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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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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