RetNet proposes recurrence as an alternative to attention
Published on September 24, 2026, the Retentive Network paper presents a language-model architecture that combines parallel training with recurrent inference.
Source: RetNet combina treinamento paralelo com inferência recorrente (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on September 24, 2026, the Retentive Network paper proposes a retention mechanism for sequence modeling and describes parallel, recurrent, and chunkwise recurrent computation. It derives a connection between recurrence and attention, and presents these inference paradigms as relevant to evaluating Transformer architectures.
A company evaluating models could implement a controlled comparison across architectures, measuring quality, latency, and resource use on its actual workloads. This can include preparing test data, automating evaluations, and monitoring results to inform architecture decisions, without assuming one alternative will be superior in every case.
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Wendelmaques can diagnose the company’s inference requirements and evaluation criteria, design a suitable comparison, and implement testing and monitoring pipelines. The scope can include operating these evaluations on the client’s infrastructure.
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