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xLSTM outperforms Mamba-2 and DeltaNet in sequence modeling tasks

By PulseAugur Editorial · [3 sources] ·

A new research paper compares three subquadratic architectures—xLSTM, Mamba-2, and Gated DeltaNet—for sequence modeling tasks. The study found that xLSTM outperformed the others in code-model pre-training, distillation, and time-series foundation models. Researchers attribute xLSTM's superior performance to its flexible and stable memory correction capabilities through a gating scheme, enabling robust state tracking and accumulation. AI

IMPACT xLSTM's demonstrated advantage in state tracking and memory correction could influence future sequence model development, potentially leading to more efficient and capable AI systems.

RANK_REASON The cluster contains a research paper comparing different model architectures.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

xLSTM outperforms Mamba-2 and DeltaNet in sequence modeling tasks

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COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Anamaria-Roberta Hartl, Levente Z\'olyomi, David Stap, Pieter-Jan Hoedt, Niklas Schmidinger, Lukas Hauzenberger, Sebastian B\"ock, G\"unter Klambauer, Sepp Hochreiter ·

    On Subquadratic Architectures: From Applications to Principles

    arXiv:2606.12364v1 Announce Type: new Abstract: Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost. Subquadratic architectures offer a scalable alternative. However, it remains unclear which designs yield the most e…

  2. arXiv cs.LG TIER_1 English(EN) · Sepp Hochreiter ·

    On Subquadratic Architectures: From Applications to Principles

    Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost. Subquadratic architectures offer a scalable alternative. However, it remains unclear which designs yield the most effective sequence models. We compare three leadi…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    On Subquadratic Architectures: From Applications to Principles

    xLSTM demonstrates superior performance in sequence modeling tasks compared to Mamba-2 and Gated DeltaNet due to enhanced state tracking and memory dynamics.

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