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ENTITY xLSTM: Extended Long Short-Term Memory

xLSTM: Extended Long Short-Term Memory

PulseAugur coverage of xLSTM: Extended Long Short-Term Memory — every cluster mentioning xLSTM: Extended Long Short-Term Memory across labs, papers, and developer communities, ranked by signal.

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RECENT · PAGE 1/1 · 6 TOTAL
  1. TOOL · CL_129313 ·

    xLSTM models achieve near-lossless distillation from larger LLMs

    Researchers have developed an effective distillation pipeline to transfer knowledge from large language models (LLMs) with quadratic attention to sub-quadratic architectures based on xLSTM. This method aims for lossless…

  2. RESEARCH · CL_84478 ·

    xLSTM outperforms Mamba-2 and DeltaNet in sequence modeling tasks

    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,…

  3. TOOL · CL_48969 ·

    New X-TRACK model uses xLSTM and physics for realistic vehicle trajectory prediction

    Researchers have developed X-TRACK, a novel trajectory prediction model for autonomous driving that leverages the extended Long Short-Term Memory (xLSTM) architecture. This new model explicitly incorporates vehicle moti…

  4. RESEARCH · CL_39994 ·

    CogScale benchmark accelerates AI sequence processing evaluation

    Researchers have introduced CogScale, a new benchmark designed to efficiently evaluate the sequential processing capabilities of AI architectures. This benchmark comprises 14 scalable synthetic tasks that allow for rapi…

  5. TOOL · CL_16225 ·

    Short window attention boosts long-term memory in AI models

    Researchers have developed a hybrid architecture combining sliding window attention and xLSTM layers to improve long-term memorization in AI models. Their findings indicate that surprisingly, larger sliding windows can …

  6. RESEARCH · CL_08685 ·

    xLSTM networks enhance deep reinforcement learning for automated stock trading

    Researchers have developed a new automated stock trading system utilizing Extended Long Short-Term Memory (xLSTM) networks combined with deep reinforcement learning (DRL). This approach aims to overcome the limitations …