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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Review paper details long horizon forecasting challenges and deep learning solutions
A new review paper published on arXiv details the long horizon forecasting (LHF) problem in time series analysis, a challenge that has persisted for over 35 years. The paper explores how deep learning techniques, includ…
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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…
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TiRex-2 model advances multivariate time series forecasting with recurrent xLSTM design
Researchers have introduced TiRex-2, a novel recurrent foundation model based on xLSTM architecture designed for multivariate time series forecasting. This model addresses limitations of existing Transformer-based appro…
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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,…
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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…
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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…
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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 …
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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 …