xLSTM shines as an Electrocardiogram (ECG) foundation model: arxiv.org/abs/2509.10151 "xECG achieves superior performance over earlier approaches, defining a new baseline for future ECG foundation models." xLSTM is perfectly suited for time series prediction as shown by TiRex.

xLSTM excels in time series forecasting: arxiv.org/abs/2509.01187 . Introduces "stochastic xLSTM" (StoxLSTM). "StoxLSTM consistently outperforms state-of-the-art baselines with better robustness and stronger generalization ability." We know that xLSTM is king at time series from our TiRex.

xLSTM for Cellular Traffic Forecasting: arxiv.org/abs/2507.19513 "Empirical results showed a 23% MAE reduction over the original STN and a 30% improvement on unseen data, highlighting strong generalization." xLSTM shines again in time series forecasting.

xLSTM for Monaural Speech Enhancement: arxiv.org/abs/2507.04368 xLSTM has superior performance vs. Mamba and Transformers but is slower than Mamba. New Triton kernels: xLSTM is faster than MAMBA at training and inference: arxiv.org/abs/2503.13427 and arxiv.org/abs/2503.14376

xLSTM for Aspect-based Sentiment Analysis: arxiv.org/abs/2507.01213 Another success story of xLSTM. MEGA: xLSTM with Multihead Exponential Gated Fusion. Experiments on 3 benchmarks show that MEGA outperforms state-of-the-art baselines with superior accuracy and efficiency”

xLSTM for multivariate time series anomaly detection: arxiv.org/abs/2506.22837 “In our results, xLSTM showcases state-of-the-art accuracy, outperforming 23 popular anomaly detection baselines.” Again, xLSTM excels in time series analysis.

xLSTM for Human Action Segmentation: arxiv.org/abs/2506.09650 "HopaDIFF, leveraging a novel cross-input gate attentional xLSTM to enhance holistic-partial long-range reasoning" "HopaDIFF achieves state-of-theart results on RHAS133 in diverse evaluation settings."

Mein Buch “Was kann Künstliche Intelligenz?“ ist erschienen. Eine leicht zugängliche Einführung in das Thema Künstliche Intelligenz. LeserInnen – auch ohne technischen Hintergrund – wird erklärt, was KI eigentlich ist, welche Potenziale sie birgt und welche Auswirkungen sie hat.

We are soooo proud. Our European-developed TiRex is leading the field—significantly ahead of U.S. competitors like Amazon, Datadog, Salesforce, and Google, as well as Chinese models from companies such as Alibaba.

Attention!! Our TiRex time series model, built on xLSTM, is topping all major international leaderboards. A European-developed model is leading the field—significantly ahead of U.S. competitors like Amazon, Datadog, Salesforce, and Google, as well as Chinese models from Alibaba.

TiRex 🦖 time series xLSTM model ranked #1 on all leaderboards. ➡️ Outperforms models by Amazon, Google, Datadog, Salesforce, Alibaba ➡️ industrial applications ➡️ limited data ➡️ embedded AI and edge devices ➡️ Europe is leading Code: lnkd.in/eHXb-XwZ Paper: lnkd.in/e8e7xnri shorturl.at/jcQeq

Recommended read for the weekend: Sepp Hochreiter's book on AI! Lots of fun anecdotes and easily accessible basics on AI! www.beneventopublishing.com/ecowing/prod...

xLSTM for the classification of assembly tasks: arxiv.org/abs/2505.18012 "xLSTM model demonstrated better generalization capabilities to new operators. The results clearly show that for this type of classification, the xLSTM model offers a slight edge over Transformers."

Happy to introduce 🔥LaM-SLidE🔥! We show how trajectories of spatial dynamical systems can be modeled in latent space by --> leveraging IDENTIFIERS. 📚Paper: arxiv.org/abs/2502.12128 💻Code: github.com/ml-jku/LaM-S... 📝Blog: ml-jku.github.io/LaM-SLidE/ 1/n

1/11 Excited to present our latest work "Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics" at #ICLR2025 on Fri 25 Apr at 10 am! #CombinatorialOptimization #StatisticalPhysics #DiffusionModels

xLSTM for Multi-label ECG Classification: arxiv.org/abs/2504.16101 "This approach significantly improves ECG classification accuracy, thereby advancing clinical diagnostics and patient care." Cool.

Precall: 4 Tenure-Tracks in AI for females. Check it out.

Huge achievement: arxiv.org/abs/2503.14376 xLSTM kernels are now the fastest kernels both for training and inference. Faster than Flashattention or Mamba kernels. High arithmetic intensity. Optimized use of GPUs that set a new state of the art. Congratulations to the team. It is highly impressive.

Visited with legendary #AI pioneer @hochreitersepp.bsky.social at #JKULinz. We discussed the efficiency of #xLSTM against #GPT and why AI's future may be located on the edge of networks, rather than in centralised data centres via @bloomberg.com www.bloomberg.com/news/article...

xLSTM for Automated Stock Trading: arxiv.org/abs/2503.09655 xLSTM outperforms LSTM. "These findings mark the potential of xLSTM for enhancing DRL-based stock trading systems." This reinforcement learning approach uses xLSTM in both actor and critic components, which increases the performance.

Join Our Research Team in Linz! We are looking for 5 PostDocs and 10 PhDs in Machine Learning working on xLSTM, NLP, robustness, density ratio. Deadline: 04/20/25. More details: www.jku.at/en/lit-artif... #MachineLearning #DeepLearning #ResearchOpportunities #PhDPositions

Exploration imitation learning architectures: Transformer, Mamba, xLSTM: arxiv.org/abs/2502.12330 *LIBERO: “xLSTM shows great potential” *RoboCasa: “xLSTM models, we achieved success rate of 53.6%, compared to 40.0% of BC-Transformer” *Point Clouds: “xLSTM model achieves a 60.9% success rate”

xLSTM for time series with Granger causality: arxiv.org/abs/2502.09981 xLSTM again shows superb performance at time series analysis. "Our experimental evaluations on three datasets demonstrate the overall efficacy of our proposed GC-xLSTM model."

xLSTM shines at tumor segmentation: arxiv.org/abs/2502.00314 “evaluated state-of-the-art segmentation methods, including U-Net and its enhanced variants with Transformers and Mamba. Our proposed ViLU-Net [vision xLSTM-Net] model achieved superior performance with reduced complexity.” Cool.

Don't miss this workshop. Super interesting .

xLSTM for molecular property prediction: arxiv.org/abs/2501.18439 "AUROC improvement of 3.18% for classification tasks and an RMSE reduction of 3.83% across regression datasets compared to the baseline methods." Again xLSTM excels in a life science. Compare Bio-xLSTM: arxiv.org/abs/2411.04165.

xLSTM for knowledge tracing: arxiv.org/abs/2501.14256 xLSTM excels at another task. “DKT2 [the xLSTM] consistently outperforms 17 baseline models in various prediction tasks” Baseline models include Transformers, Mamba and graph-based methods. DKT2 exploits exponential gating and matrix memory.

EMBL & ELLIS deepen partnership to drive #AI in European #LifeSciences. 🤝 We aim to unlock new insights, train the next gen of AI researchers, & develop innovative solutions for human & planetary health. Read the full article here: ellis.eu/news/embl-an...

xLSTM outperforms Conformers (Transformers) and Mamba on Speech Enhancement: arxiv.org/abs/2501.06146 “xLSTM-SENet2, outperforms state-of-the-art Mamba- and Conformer-based systems on the Voicebank+DEMAND.” New xLSTM-Triton-Kernels are faster than FlashAttention 3 & Mamba for training and inference.

Often LLMs hallucinate because of semantic uncertainty due to missing factual training data. We propose a method to detect such uncertainties using only one generated output sequence. Super efficient method to detect hallucination in LLMs.